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Browse files- data/clustering_battle-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl +0 -1
- data/clustering_battle-e34a3715-f8ac-4a02-b87e-a06c4cae2113.jsonl +0 -1
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- data/sts_side_by_side-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl +0 -7
data/clustering_battle-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl
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data/clustering_individual-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl
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data/clustering_individual-e34a3715-f8ac-4a02-b87e-a06c4cae2113.jsonl
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data/clustering_individual-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
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{"tstamp": 1722227987.6746, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722227987.5975, "finish": 1722227987.6746, "ip": "", "conv_id": "c8dcb7f5eb3748299a0c816a5e9d7d50", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228002.2561, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722228002.0843, "finish": 1722228002.2561, "ip": "", "conv_id": "ee01c3f7086a42fe9ffb269072bc221b", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "Beijing"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228002.2561, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722228002.0843, "finish": 1722228002.2561, "ip": "", "conv_id": "08b698a53590411c80c8976023cb7145", "model_name": "embed-english-v3.0", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "Beijing"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228037.6596, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722228037.3076, "finish": 1722228037.6596, "ip": "", "conv_id": "b270659bf45f4b5791f2ab941eeb5e14", "model_name": "voyage-multilingual-2", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "New York"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228037.6596, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228037.3076, "finish": 1722228037.6596, "ip": "", "conv_id": "b8e04216cf124dcab01d421ce78b6877", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "New York"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228042.7793, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722228042.1158, "finish": 1722228042.7793, "ip": "", "conv_id": "bbd3840eeb4649218e3657b7e8943b9e", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "New York"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228042.7793, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228042.1158, "finish": 1722228042.7793, "ip": "", "conv_id": "89b29266161d4e88959459df482231d3", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Nara", "Kobe", "Osaka", "Tokyo", "New York"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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{"tstamp": 1722228447.8356, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722228447.7468, "finish": 1722228447.8356, "ip": "", "conv_id": "98eb1579858d4265ab4bf3f051c51797", "model_name": "GritLM/GritLM-7B", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 62 |
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{"tstamp": 1722228518.9824, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228518.773, "finish": 1722228518.9824, "ip": "", "conv_id": "24c2545ccdbb490cb29140185d8f0880", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D"], "ncluster": 3, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
|
| 63 |
-
{"tstamp": 1722228528.9922, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722228528.8888, "finish": 1722228528.9922, "ip": "", "conv_id": "98eb1579858d4265ab4bf3f051c51797", "model_name": "GritLM/GritLM-7B", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
|
| 64 |
-
{"tstamp": 1722228528.9922, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228528.8888, "finish": 1722228528.9922, "ip": "", "conv_id": "24c2545ccdbb490cb29140185d8f0880", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
|
| 65 |
-
{"tstamp": 1722228545.3912, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722228545.279, "finish": 1722228545.3912, "ip": "", "conv_id": "98eb1579858d4265ab4bf3f051c51797", "model_name": "GritLM/GritLM-7B", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb", "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 66 |
-
{"tstamp": 1722228545.3912, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228545.279, "finish": 1722228545.3912, "ip": "", "conv_id": "24c2545ccdbb490cb29140185d8f0880", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb", "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 67 |
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{"tstamp": 1722228646.3615, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722228646.0203, "finish": 1722228646.3615, "ip": "", "conv_id": "28a1cb1675d14f6d89eb72d0a361efdb", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 68 |
-
{"tstamp": 1722228646.3615, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722228646.0203, "finish": 1722228646.3615, "ip": "", "conv_id": "7a63c532288c465abd7da7cb98ee7ba5", "model_name": "embed-english-v3.0", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 69 |
-
{"tstamp": 1722228649.5433, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722228649.3281, "finish": 1722228649.5433, "ip": "", "conv_id": "28a1cb1675d14f6d89eb72d0a361efdb", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 70 |
-
{"tstamp": 1722228649.5433, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722228649.3281, "finish": 1722228649.5433, "ip": "", "conv_id": "7a63c532288c465abd7da7cb98ee7ba5", "model_name": "embed-english-v3.0", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 71 |
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{"tstamp": 1722228693.673, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722228693.5855, "finish": 1722228693.673, "ip": "", "conv_id": "b2f09ce4818c402eaefe87a2c69a004b", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 72 |
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{"tstamp": 1722228693.673, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722228693.5855, "finish": 1722228693.673, "ip": "", "conv_id": "987bfd2913264d47a83287f17042c13f", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 73 |
-
{"tstamp": 1722228695.6411, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722228695.5558, "finish": 1722228695.6411, "ip": "", "conv_id": "b2f09ce4818c402eaefe87a2c69a004b", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 74 |
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{"tstamp": 1722228695.6411, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722228695.5558, "finish": 1722228695.6411, "ip": "", "conv_id": "987bfd2913264d47a83287f17042c13f", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 75 |
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{"tstamp": 1722228721.6691, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722228721.5702, "finish": 1722228721.6691, "ip": "", "conv_id": "06049337a7c5475683e5a879a274dc71", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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| 76 |
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{"tstamp": 1722228721.6691, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722228721.5702, "finish": 1722228721.6691, "ip": "", "conv_id": "ffd86641c34c432783c038b34bc180ea", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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data/clustering_side_by_side-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
DELETED
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@@ -1,4 +0,0 @@
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| 1 |
-
{"tstamp": 1722228581.4527, "task_type": "clustering", "type": "tievote", "models": ["### Model A: GritLM/GritLM-7B", "### Model B: BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "98eb1579858d4265ab4bf3f051c51797", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb", "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "24c2545ccdbb490cb29140185d8f0880", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["Airsoft video in Japan (LVOA,MK18Mod1)", "Grumpy review of the late 2015 5k iMac", "My item has not been in stock for more than a week. What do?", "I figured while I was making my gun Commiefornia compatible, Id customize a little :D", "Pikachu", "Charmander", "Squirtle", "Chikorita", "Electabuzz", "Ponyta", "Poliwhirl", "Sunflora", "Mareep", "Slugma", "Staryu", "Grovyle", "Bellossom", "Voltorb", "Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
|
| 2 |
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{"tstamp": 1722228660.1137, "task_type": "clustering", "type": "leftvote", "models": ["### Model A: Salesforce/SFR-Embedding-2_R", "### Model B: embed-english-v3.0"], "ip": "", "0_conv_id": "28a1cb1675d14f6d89eb72d0a361efdb", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "7a63c532288c465abd7da7cb98ee7ba5", "1_model_name": "embed-english-v3.0", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
|
| 3 |
-
{"tstamp": 1722228705.2882, "task_type": "clustering", "type": "rightvote", "models": ["### Model A: intfloat/e5-mistral-7b-instruct", "### Model B: intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "b2f09ce4818c402eaefe87a2c69a004b", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "987bfd2913264d47a83287f17042c13f", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
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| 4 |
-
{"tstamp": 1722228727.7822, "task_type": "clustering", "type": "tievote", "models": ["### Model A: Salesforce/SFR-Embedding-2_R", "### Model B: intfloat/e5-mistral-7b-instruct"], "ip": "", "0_conv_id": "06049337a7c5475683e5a879a274dc71", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "ffd86641c34c432783c038b34bc180ea", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
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data/retrieval_battle-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl
DELETED
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| 1 |
-
{"tstamp": 1722223022.6009, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "776f02bd82f74b80aff4960db5817568", "0_model_name": "BM25", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: Controller Testing with Minitest and Devise failing In a Rails 3.28 application using Devise, I setup a controller that checks whether a user is logged in or not with if user_signed_in?. Once the tests hit this line, I always get a undefined method 'authenticate!' for nil:NilClass error. I wanted to test some code in a scenario that no user is logged in.\nI am thinking that I need to setup the tests according to the options specified in the Devise Wiki here, however, all the instructions pertain to rspec.\nIn particular, I think I need to have the following to work in minitest:\nRSpec.configure do |config|\n config.include Devise::TestHelpers, :type => :controller\n config.extend ControllerMacros, :type => :controller\nend\n\nHas anyone succeded in setting this up to have Devise work with minitest?\n\nA: Add the following to your minitest_helper.rb file:\nclass MiniTest::Rails::ActionController::TestCase\n include Devise::TestHelpers\nend\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "d88e38d4fcfa46b08b4d9c9a414d77c7", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: android - getting nullpointerexception for no specific reason I am working on an android project and as far as I know, I don't make any specific mistakes in my code that would lead to an exception. \nI get these two error lines everytime I run my project, but the rest of my code runs without any issues.\nThe NullPointerException doesn't have a concrete occurance location, which confuses me.\nI have already searched for similar issues. A possible solution was adding the READ_EXTERNAL_STORAGE and WRITE_EXTERNAL_STORAGE as uses-permission in my Android Manifest file, but to no avail.\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: Fail to get file list com.example.package_name\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: getFolderSize() : Exception_1 = java.lang.NullPointerException: Attempt to get length of null array\n\n\nA: The error was thrown by my OnePlus, on the android emulator there was no error shown\n"]], "1_corpus": "stackexchange"}
|
| 2 |
-
{"tstamp": 1722223253.8945, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "42ce372770374b24ad9d88378f058800", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "0_corpus": "arxiv", "1_conv_id": "4cce5456d4794da8bc4d8b54a94fa7a6", "1_model_name": "text-embedding-004", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "1_corpus": "arxiv"}
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data/retrieval_battle-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
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data/retrieval_individual-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl
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{"tstamp": 1722222959.9785, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722222943.427, "finish": 1722222959.9785, "ip": "", "conv_id": "776f02bd82f74b80aff4960db5817568", "model_name": "BM25", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: Controller Testing with Minitest and Devise failing In a Rails 3.28 application using Devise, I setup a controller that checks whether a user is logged in or not with if user_signed_in?. Once the tests hit this line, I always get a undefined method 'authenticate!' for nil:NilClass error. I wanted to test some code in a scenario that no user is logged in.\nI am thinking that I need to setup the tests according to the options specified in the Devise Wiki here, however, all the instructions pertain to rspec.\nIn particular, I think I need to have the following to work in minitest:\nRSpec.configure do |config|\n config.include Devise::TestHelpers, :type => :controller\n config.extend ControllerMacros, :type => :controller\nend\n\nHas anyone succeded in setting this up to have Devise work with minitest?\n\nA: Add the following to your minitest_helper.rb file:\nclass MiniTest::Rails::ActionController::TestCase\n include Devise::TestHelpers\nend\n\n"]], "corpus": "stackexchange"}
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{"tstamp": 1722222959.9785, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722222943.427, "finish": 1722222959.9785, "ip": "", "conv_id": "d88e38d4fcfa46b08b4d9c9a414d77c7", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: android - getting nullpointerexception for no specific reason I am working on an android project and as far as I know, I don't make any specific mistakes in my code that would lead to an exception. \nI get these two error lines everytime I run my project, but the rest of my code runs without any issues.\nThe NullPointerException doesn't have a concrete occurance location, which confuses me.\nI have already searched for similar issues. A possible solution was adding the READ_EXTERNAL_STORAGE and WRITE_EXTERNAL_STORAGE as uses-permission in my Android Manifest file, but to no avail.\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: Fail to get file list com.example.package_name\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: getFolderSize() : Exception_1 = java.lang.NullPointerException: Attempt to get length of null array\n\n\nA: The error was thrown by my OnePlus, on the android emulator there was no error shown\n"]], "corpus": "stackexchange"}
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{"tstamp": 1722223229.0432, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722223205.3663, "finish": 1722223229.0432, "ip": "", "conv_id": "42ce372770374b24ad9d88378f058800", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
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{"tstamp": 1722223229.0432, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722223205.3663, "finish": 1722223229.0432, "ip": "", "conv_id": "4cce5456d4794da8bc4d8b54a94fa7a6", "model_name": "text-embedding-004", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
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data/retrieval_individual-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
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data/sts_battle-e34a3715-f8ac-4a02-b87e-a06c4cae2113.jsonl
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{"tstamp": 1722222732.4655, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "6d368b70fe664613a32db88b5c963974", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_txt0": "Lush, green fields that stretched for miles, wisps of white clouds scattered across a clear, blue sky, tranquil silence broken only by cheerful bird songs, the fresh, sweet scent of morning air.", "0_txt1": "The birds were making noise early in the day.", "0_txt2": "The fields were full of strawberry crops.", "0_output": "", "1_conv_id": "7955244e294f448dbee3431bb72c5eab", "1_model_name": "voyage-multilingual-2", "1_txt0": "Lush, green fields that stretched for miles, wisps of white clouds scattered across a clear, blue sky, tranquil silence broken only by cheerful bird songs, the fresh, sweet scent of morning air.", "1_txt1": "The birds were making noise early in the day.", "1_txt2": "The fields were full of strawberry crops.", "1_output": ""}
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data/sts_individual-e34a3715-f8ac-4a02-b87e-a06c4cae2113.jsonl
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{"tstamp": 1722222699.6837, "task_type": "sts", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722222695.2264, "finish": 1722222699.6837, "ip": "", "conv_id": "6d368b70fe664613a32db88b5c963974", "model_name": "intfloat/e5-mistral-7b-instruct", "txt0": "Lush, green fields that stretched for miles, wisps of white clouds scattered across a clear, blue sky, tranquil silence broken only by cheerful bird songs, the fresh, sweet scent of morning air.", "txt1": "The birds were making noise early in the day.", "txt2": "The fields were full of strawberry crops.", "output": ""}
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{"tstamp": 1722222699.6837, "task_type": "sts", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722222695.2264, "finish": 1722222699.6837, "ip": "", "conv_id": "7955244e294f448dbee3431bb72c5eab", "model_name": "voyage-multilingual-2", "txt0": "Lush, green fields that stretched for miles, wisps of white clouds scattered across a clear, blue sky, tranquil silence broken only by cheerful bird songs, the fresh, sweet scent of morning air.", "txt1": "The birds were making noise early in the day.", "txt2": "The fields were full of strawberry crops.", "output": ""}
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data/sts_individual-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
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{"tstamp": 1722228201.5891, "task_type": "sts", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722228201.4532, "finish": 1722228201.5891, "ip": "", "conv_id": "7a0f76b9d65f40babe6a836b1c652195", "model_name": "GritLM/GritLM-7B", "txt0": "Miyares spent the next four months in hospitals in Japan and the U.S. before being medically discharged from the Army with as service-connected disability for diabetes.", "txt1": "Miyares spent time in hospitals in at least two different countries.", "txt2": "Miyares was not medically discharges from the Army.", "output": ""}
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{"tstamp": 1722228201.5891, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228201.4532, "finish": 1722228201.5891, "ip": "", "conv_id": "a86568394b624381a06672a6c8c03cb4", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "Miyares spent the next four months in hospitals in Japan and the U.S. before being medically discharged from the Army with as service-connected disability for diabetes.", "txt1": "Miyares spent time in hospitals in at least two different countries.", "txt2": "Miyares was not medically discharges from the Army.", "output": ""}
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{"tstamp": 1722228239.0578, "task_type": "sts", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722228239.0131, "finish": 1722228239.0578, "ip": "", "conv_id": "8168625f595640f4a76cb0f339584b02", "model_name": "GritLM/GritLM-7B", "txt0": "hello", "txt1": "good morning", "txt2": "\u65e9\u4e0a\u597d", "output": ""}
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{"tstamp": 1722228239.0578, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722228239.0131, "finish": 1722228239.0578, "ip": "", "conv_id": "7002e7c1477a4da6a98030adeb10a333", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "hello", "txt1": "good morning", "txt2": "\u65e9\u4e0a\u597d", "output": ""}
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| 5 |
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{"tstamp": 1722228253.9961, "task_type": "sts", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722228253.9416, "finish": 1722228253.9961, "ip": "", "conv_id": "7e65cef189d64405be8f160240d16099", "model_name": "Salesforce/SFR-Embedding-2_R", "txt0": "We were humble as a result of these circumstances.", "txt1": "It was impossible not to remain humble in these circumstances.", "txt2": "We were not humble given these circumstances.", "output": ""}
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{"tstamp": 1722228253.9961, "task_type": "sts", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722228253.9416, "finish": 1722228253.9961, "ip": "", "conv_id": "abd5aca80a9f4b1fb1b6a6867ffd7316", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "txt0": "We were humble as a result of these circumstances.", "txt1": "It was impossible not to remain humble in these circumstances.", "txt2": "We were not humble given these circumstances.", "output": ""}
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{"tstamp": 1722228282.3969, "task_type": "sts", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722228282.1911, "finish": 1722228282.3969, "ip": "", "conv_id": "ec5342ebebe44fae9e320560e8bda9c7", "model_name": "embed-english-v3.0", "txt0": "The dog likes to catch baseballs.", "txt1": "a puppy about to jump to intercept a yellow ball", "txt2": "The dog is trying to catch a tennis ball.", "output": ""}
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{"tstamp": 1722228282.3969, "task_type": "sts", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722228282.1911, "finish": 1722228282.3969, "ip": "", "conv_id": "4e596a3af6df447e9328d48dad96d738", "model_name": "intfloat/e5-mistral-7b-instruct", "txt0": "The dog likes to catch baseballs.", "txt1": "a puppy about to jump to intercept a yellow ball", "txt2": "The dog is trying to catch a tennis ball.", "output": ""}
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| 9 |
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{"tstamp": 1722228316.839, "task_type": "sts", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722228316.8044, "finish": 1722228316.839, "ip": "", "conv_id": "b0a2e38209de42a48a0a28eac87403e2", "model_name": "intfloat/multilingual-e5-large-instruct", "txt0": "There are technical reasons behind the confusion surrounding American 77's location", "txt1": "Everything on the plane was working up to standard and did not contribute to the difficulty in locating it.", "txt2": "The reasons are technical, arising from the way the software processed radar information, as well as from poor primary radar coverage where American 77 was flying.", "output": ""}
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| 10 |
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{"tstamp": 1722228316.839, "task_type": "sts", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722228316.8044, "finish": 1722228316.839, "ip": "", "conv_id": "e8f4f54fdc8e4290afe9595fe55bb1e0", "model_name": "jinaai/jina-embeddings-v2-base-en", "txt0": "There are technical reasons behind the confusion surrounding American 77's location", "txt1": "Everything on the plane was working up to standard and did not contribute to the difficulty in locating it.", "txt2": "The reasons are technical, arising from the way the software processed radar information, as well as from poor primary radar coverage where American 77 was flying.", "output": ""}
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| 11 |
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{"tstamp": 1722228361.3655, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722228361.324, "finish": 1722228361.3655, "ip": "", "conv_id": "89e23abe42fc49279620e11852910287", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "Children are napping on a floor.", "txt1": "Children are playing childish games while others stand by.", "txt2": "Children nap on a floor while others stand by.", "output": ""}
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{"tstamp": 1722228361.3655, "task_type": "sts", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722228361.324, "finish": 1722228361.3655, "ip": "", "conv_id": "f307dc09df3f46b188e4c8f312a2a123", "model_name": "nomic-ai/nomic-embed-text-v1.5", "txt0": "Children are napping on a floor.", "txt1": "Children are playing childish games while others stand by.", "txt2": "Children nap on a floor while others stand by.", "output": ""}
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| 13 |
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{"tstamp": 1722228381.4081, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722228380.8037, "finish": 1722228381.4081, "ip": "", "conv_id": "507beb5ac1124422aca9a199d7a3faf1", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "txt1": "A red bus making a turn", "txt2": "A red bus backing up into a spot", "output": ""}
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| 14 |
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{"tstamp": 1722228381.4081, "task_type": "sts", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722228380.8037, "finish": 1722228381.4081, "ip": "", "conv_id": "a9597960b05740e2b59401189b786f9b", "model_name": "text-embedding-004", "txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "txt1": "A red bus making a turn", "txt2": "A red bus backing up into a spot", "output": ""}
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{"tstamp": 1722228404.6631, "task_type": "sts", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722228404.1993, "finish": 1722228404.6631, "ip": "", "conv_id": "15b58d94bbcf4bd58ef48b92caf67360", "model_name": "text-embedding-3-large", "txt0": "People are shopping.", "txt1": "Numerous customers browsing for produce in a market", "txt2": "People are showering.", "output": ""}
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{"tstamp": 1722228404.6631, "task_type": "sts", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722228404.1993, "finish": 1722228404.6631, "ip": "", "conv_id": "4df90cd617e346758a1144fda1d6ddb1", "model_name": "voyage-multilingual-2", "txt0": "People are shopping.", "txt1": "Numerous customers browsing for produce in a market", "txt2": "People are showering.", "output": ""}
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data/sts_side_by_side-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl
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{"tstamp": 1722228226.8067, "task_type": "sts", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "7a0f76b9d65f40babe6a836b1c652195", "0_model_name": "GritLM/GritLM-7B", "0_txt0": "Miyares spent the next four months in hospitals in Japan and the U.S. before being medically discharged from the Army with as service-connected disability for diabetes.", "0_txt1": "Miyares spent time in hospitals in at least two different countries.", "0_txt2": "Miyares was not medically discharges from the Army.", "0_output": "", "1_conv_id": "a86568394b624381a06672a6c8c03cb4", "1_model_name": "BAAI/bge-large-en-v1.5", "1_txt0": "Miyares spent the next four months in hospitals in Japan and the U.S. before being medically discharged from the Army with as service-connected disability for diabetes.", "1_txt1": "Miyares spent time in hospitals in at least two different countries.", "1_txt2": "Miyares was not medically discharges from the Army.", "1_output": ""}
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{"tstamp": 1722228265.7645, "task_type": "sts", "type": "leftvote", "models": ["Salesforce/SFR-Embedding-2_R", "Alibaba-NLP/gte-Qwen2-7B-instruct"], "ip": "", "0_conv_id": "7e65cef189d64405be8f160240d16099", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_txt0": "We were humble as a result of these circumstances.", "0_txt1": "It was impossible not to remain humble in these circumstances.", "0_txt2": "We were not humble given these circumstances.", "0_output": "", "1_conv_id": "abd5aca80a9f4b1fb1b6a6867ffd7316", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_txt0": "We were humble as a result of these circumstances.", "1_txt1": "It was impossible not to remain humble in these circumstances.", "1_txt2": "We were not humble given these circumstances.", "1_output": ""}
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| 3 |
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{"tstamp": 1722228301.4286, "task_type": "sts", "type": "tievote", "models": ["embed-english-v3.0", "intfloat/e5-mistral-7b-instruct"], "ip": "", "0_conv_id": "ec5342ebebe44fae9e320560e8bda9c7", "0_model_name": "embed-english-v3.0", "0_txt0": "The dog likes to catch baseballs.", "0_txt1": "a puppy about to jump to intercept a yellow ball", "0_txt2": "The dog is trying to catch a tennis ball.", "0_output": "", "1_conv_id": "4e596a3af6df447e9328d48dad96d738", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_txt0": "The dog likes to catch baseballs.", "1_txt1": "a puppy about to jump to intercept a yellow ball", "1_txt2": "The dog is trying to catch a tennis ball.", "1_output": ""}
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| 4 |
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{"tstamp": 1722228346.8536, "task_type": "sts", "type": "tievote", "models": ["intfloat/multilingual-e5-large-instruct", "jinaai/jina-embeddings-v2-base-en"], "ip": "", "0_conv_id": "b0a2e38209de42a48a0a28eac87403e2", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_txt0": "There are technical reasons behind the confusion surrounding American 77's location", "0_txt1": "Everything on the plane was working up to standard and did not contribute to the difficulty in locating it.", "0_txt2": "The reasons are technical, arising from the way the software processed radar information, as well as from poor primary radar coverage where American 77 was flying.", "0_output": "", "1_conv_id": "e8f4f54fdc8e4290afe9595fe55bb1e0", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_txt0": "There are technical reasons behind the confusion surrounding American 77's location", "1_txt1": "Everything on the plane was working up to standard and did not contribute to the difficulty in locating it.", "1_txt2": "The reasons are technical, arising from the way the software processed radar information, as well as from poor primary radar coverage where American 77 was flying.", "1_output": ""}
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| 5 |
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{"tstamp": 1722228368.3339, "task_type": "sts", "type": "tievote", "models": ["mixedbread-ai/mxbai-embed-large-v1", "nomic-ai/nomic-embed-text-v1.5"], "ip": "", "0_conv_id": "89e23abe42fc49279620e11852910287", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_txt0": "Children are napping on a floor.", "0_txt1": "Children are playing childish games while others stand by.", "0_txt2": "Children nap on a floor while others stand by.", "0_output": "", "1_conv_id": "f307dc09df3f46b188e4c8f312a2a123", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_txt0": "Children are napping on a floor.", "1_txt1": "Children are playing childish games while others stand by.", "1_txt2": "Children nap on a floor while others stand by.", "1_output": ""}
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| 6 |
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{"tstamp": 1722228389.3721, "task_type": "sts", "type": "tievote", "models": ["sentence-transformers/all-MiniLM-L6-v2", "text-embedding-004"], "ip": "", "0_conv_id": "507beb5ac1124422aca9a199d7a3faf1", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "0_txt1": "A red bus making a turn", "0_txt2": "A red bus backing up into a spot", "0_output": "", "1_conv_id": "a9597960b05740e2b59401189b786f9b", "1_model_name": "text-embedding-004", "1_txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "1_txt1": "A red bus making a turn", "1_txt2": "A red bus backing up into a spot", "1_output": ""}
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{"tstamp": 1722228413.2177, "task_type": "sts", "type": "tievote", "models": ["text-embedding-3-large", "voyage-multilingual-2"], "ip": "", "0_conv_id": "15b58d94bbcf4bd58ef48b92caf67360", "0_model_name": "text-embedding-3-large", "0_txt0": "People are shopping.", "0_txt1": "Numerous customers browsing for produce in a market", "0_txt2": "People are showering.", "0_output": "", "1_conv_id": "4df90cd617e346758a1144fda1d6ddb1", "1_model_name": "voyage-multilingual-2", "1_txt0": "People are shopping.", "1_txt1": "Numerous customers browsing for produce in a market", "1_txt2": "People are showering.", "1_output": ""}
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