diff --git a/qgen-tasb/1_Pooling/config.json b/1_Pooling/config.json similarity index 100% rename from qgen-tasb/1_Pooling/config.json rename to 1_Pooling/config.json diff --git a/qgen-tasb/README.md b/README.md similarity index 87% rename from qgen-tasb/README.md rename to README.md index 9a8cae122328782065f3434baf17c7dde04427af..621fd61b3f162f066e72a3be93d0cf4eb82b734f 100644 --- a/qgen-tasb/README.md +++ b/README.md @@ -84,17 +84,14 @@ The model was trained with the parameters: **DataLoader**: -`torch.utils.data.dataloader.DataLoader` of length 2993 with parameters: +`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters: ``` -{'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} +{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: -`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: - ``` - {'scale': 20.0, 'similarity_fct': 'cos_sim'} - ``` +`gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` @@ -105,13 +102,11 @@ Parameters of the fit()-Method: "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { - "correct_bias": false, - "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", - "steps_per_epoch": null, - "warmup_steps": 299, + "steps_per_epoch": 140000, + "warmup_steps": 1000, "weight_decay": 0.01 } ``` diff --git a/qgen-tsdae/config.json b/config.json similarity index 84% rename from qgen-tsdae/config.json rename to config.json index ac4db3717ebc3ad37bc502acd397ccd2239351bb..2340d24f07f7e9e92d1f3a9f3f5cffe26d892c31 100644 --- a/qgen-tsdae/config.json +++ b/config.json @@ -1,5 +1,5 @@ { - "_name_or_path": "/ukp-storage-1/kwang/date-exps/results/adaptation/distilbert-base-uncased/dbpedia-entity/tsdae2mdl-msv3-70k-nes-@100K/seed1/70000/0_Transformer", + "_name_or_path": "/ukp-storage-1/kwang/date-exps/results/adaptation/distilbert-base-uncased/fever/tsdae2mdl-msv3-70k-nes-@100K/seed1/70000/0_Transformer", "activation": "gelu", "architectures": [ "DistilBertModel" diff --git a/qgen-tasb/config_sentence_transformers.json b/config_sentence_transformers.json similarity index 100% rename from qgen-tasb/config_sentence_transformers.json rename to config_sentence_transformers.json diff --git a/gpl b/gpl deleted file mode 160000 index 98a7c075f0ee63f12d63e3bfdf311858dec34603..0000000000000000000000000000000000000000 --- a/gpl +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 98a7c075f0ee63f12d63e3bfdf311858dec34603 diff --git a/gpl-tasb/1_Pooling/config.json b/gpl-tasb/1_Pooling/config.json deleted file mode 100644 index da5bfd57e34ca45582e4bdbaa3e6deb9efffa08d..0000000000000000000000000000000000000000 --- a/gpl-tasb/1_Pooling/config.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "word_embedding_dimension": 768, - "pooling_mode_cls_token": true, - "pooling_mode_mean_tokens": false, - "pooling_mode_max_tokens": false, - "pooling_mode_mean_sqrt_len_tokens": false -} \ No newline at end of file diff --git a/gpl-tasb/README.md b/gpl-tasb/README.md deleted file mode 100644 index b4836e4a8d0b591fc57564ba705a8cb74222d664..0000000000000000000000000000000000000000 --- a/gpl-tasb/README.md +++ /dev/null @@ -1,122 +0,0 @@ ---- -pipeline_tag: sentence-similarity -tags: -- sentence-transformers -- feature-extraction -- sentence-similarity -- transformers ---- - -# {MODEL_NAME} - -This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. - - - -## Usage (Sentence-Transformers) - -Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: - -``` -pip install -U sentence-transformers -``` - -Then you can use the model like this: - -```python -from sentence_transformers import SentenceTransformer -sentences = ["This is an example sentence", "Each sentence is converted"] - -model = SentenceTransformer('{MODEL_NAME}') -embeddings = model.encode(sentences) -print(embeddings) -``` - - - -## Usage (HuggingFace Transformers) -Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. - -```python -from transformers import AutoTokenizer, AutoModel -import torch - - -def cls_pooling(model_output, attention_mask): - return model_output[0][:,0] - - -# Sentences we want sentence embeddings for -sentences = ['This is an example sentence', 'Each sentence is converted'] - -# Load model from HuggingFace Hub -tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') -model = AutoModel.from_pretrained('{MODEL_NAME}') - -# Tokenize sentences -encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') - -# Compute token embeddings -with torch.no_grad(): - model_output = model(**encoded_input) - -# Perform pooling. In this case, cls pooling. -sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) - -print("Sentence embeddings:") -print(sentence_embeddings) -``` - - - -## Evaluation Results - - - -For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) - - -## Training -The model was trained with the parameters: - -**DataLoader**: - -`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters: -``` -{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} -``` - -**Loss**: - -`gpl.toolkit.loss.MarginDistillationLoss` - -Parameters of the fit()-Method: -``` -{ - "epochs": 1, - "evaluation_steps": 0, - "evaluator": "NoneType", - "max_grad_norm": 1, - "optimizer_class": "", - "optimizer_params": { - "lr": 2e-05 - }, - "scheduler": "WarmupLinear", - "steps_per_epoch": 140000, - "warmup_steps": 1000, - "weight_decay": 0.01 -} -``` - - -## Full Model Architecture -``` -SentenceTransformer( - (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel - (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) -) -``` - -## Citing & Authors - - \ No newline at end of file diff --git a/gpl-tasb/config.json b/gpl-tasb/config.json deleted file mode 100644 index f7a7a715e4f60533360cd2dd539b1afbf3380988..0000000000000000000000000000000000000000 --- a/gpl-tasb/config.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "_name_or_path": "/ukp-storage-1/kwang/.cache/torch/sentence_transformers/sentence-transformers_msmarco-distilbert-base-tas-b/", - "activation": "gelu", - "architectures": [ - "DistilBertModel" - ], - "attention_dropout": 0.1, - "dim": 768, - "dropout": 0.1, - "hidden_dim": 3072, - 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"pooling_mode_max_tokens": false, - "pooling_mode_mean_sqrt_len_tokens": false -} \ No newline at end of file diff --git a/qgen-tsdae/README.md b/qgen-tsdae/README.md deleted file mode 100644 index df0bcaff5ca46f4215f7508f2113c58c85589d9f..0000000000000000000000000000000000000000 --- a/qgen-tsdae/README.md +++ /dev/null @@ -1,130 +0,0 @@ ---- -pipeline_tag: sentence-similarity -tags: -- sentence-transformers -- feature-extraction -- sentence-similarity -- transformers ---- - -# {MODEL_NAME} - -This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. - - - -## Usage (Sentence-Transformers) - -Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: - -``` -pip install -U sentence-transformers -``` - -Then you can use the model like this: - -```python -from sentence_transformers import SentenceTransformer -sentences = ["This is an example sentence", "Each sentence is converted"] - -model = SentenceTransformer('{MODEL_NAME}') -embeddings = model.encode(sentences) -print(embeddings) -``` - - - -## Usage (HuggingFace Transformers) -Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. - -```python -from transformers import AutoTokenizer, AutoModel -import torch - - -#Mean Pooling - Take attention mask into account for correct averaging -def mean_pooling(model_output, attention_mask): - token_embeddings = model_output[0] #First element of model_output contains all token embeddings - input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - - -# Sentences we want sentence embeddings for -sentences = ['This is an example sentence', 'Each sentence is converted'] - -# Load model from HuggingFace Hub -tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') -model = AutoModel.from_pretrained('{MODEL_NAME}') - -# Tokenize sentences -encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') - -# Compute token embeddings -with torch.no_grad(): - model_output = model(**encoded_input) - -# Perform pooling. In this case, mean pooling. -sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) - -print("Sentence embeddings:") -print(sentence_embeddings) -``` - - - -## Evaluation Results - - - -For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) - - -## Training -The model was trained with the parameters: - -**DataLoader**: - -`torch.utils.data.dataloader.DataLoader` of length 2998 with parameters: -``` -{'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} -``` - -**Loss**: - -`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: - ``` - {'scale': 20.0, 'similarity_fct': 'cos_sim'} - ``` - -Parameters of the fit()-Method: -``` -{ - "epochs": 1, - "evaluation_steps": 0, - "evaluator": "NoneType", - "max_grad_norm": 1, - "optimizer_class": "", - "optimizer_params": { - "correct_bias": false, - "eps": 1e-06, - "lr": 2e-05 - }, - "scheduler": "WarmupLinear", - "steps_per_epoch": null, - "warmup_steps": 299, - "weight_decay": 0.01 -} -``` - - -## Full Model Architecture -``` -SentenceTransformer( - (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel - (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) -) -``` - -## Citing & Authors - - \ No newline at end of file diff --git a/qgen-tsdae/config_sentence_transformers.json b/qgen-tsdae/config_sentence_transformers.json deleted file mode 100644 index 8eabe9973600d631d20cefde5fdd7582ac6a0174..0000000000000000000000000000000000000000 --- a/qgen-tsdae/config_sentence_transformers.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "__version__": { - "sentence_transformers": "2.1.0", - "transformers": "4.15.0", - "pytorch": "1.10.1+cu102" - } -} \ No newline at end of file diff --git a/qgen-tsdae/modules.json b/qgen-tsdae/modules.json deleted file mode 100644 index f7640f94e81bb7f4f04daf1668850b38763a13d9..0000000000000000000000000000000000000000 --- a/qgen-tsdae/modules.json +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "idx": 0, - "name": "0", - "path": "", - "type": "sentence_transformers.models.Transformer" - }, - { - "idx": 1, - "name": "1", - "path": "1_Pooling", - "type": "sentence_transformers.models.Pooling" - } -] \ No newline at end of file diff --git a/qgen-tsdae/pytorch_model.bin b/qgen-tsdae/pytorch_model.bin deleted file mode 100644 index 154557e18c90ac72be837692f45bc37aa4ff5686..0000000000000000000000000000000000000000 --- a/qgen-tsdae/pytorch_model.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3d46d056badcac5fd4ef6729df023d01cc5db0c4bebd3f91f3074943833cae08 -size 265488185 diff --git a/qgen-tsdae/sentence_bert_config.json 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"pooling_mode_cls_token": false, - "pooling_mode_mean_tokens": true, - "pooling_mode_max_tokens": false, - "pooling_mode_mean_sqrt_len_tokens": false -} \ No newline at end of file diff --git a/qgen/README.md b/qgen/README.md deleted file mode 100644 index df0bcaff5ca46f4215f7508f2113c58c85589d9f..0000000000000000000000000000000000000000 --- a/qgen/README.md +++ /dev/null @@ -1,130 +0,0 @@ ---- -pipeline_tag: sentence-similarity -tags: -- sentence-transformers -- feature-extraction -- sentence-similarity -- transformers ---- - -# {MODEL_NAME} - -This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. - - - -## Usage (Sentence-Transformers) - -Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: - -``` -pip install -U sentence-transformers -``` - -Then you can use the model like this: - -```python -from sentence_transformers import SentenceTransformer -sentences = ["This is an example sentence", "Each sentence is converted"] - -model = SentenceTransformer('{MODEL_NAME}') -embeddings = model.encode(sentences) -print(embeddings) -``` - - - -## Usage (HuggingFace Transformers) -Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. - -```python -from transformers import AutoTokenizer, AutoModel -import torch - - -#Mean Pooling - Take attention mask into account for correct averaging -def mean_pooling(model_output, attention_mask): - token_embeddings = model_output[0] #First element of model_output contains all token embeddings - input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - - -# Sentences we want sentence embeddings for -sentences = ['This is an example sentence', 'Each sentence is converted'] - -# Load model from HuggingFace Hub -tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') -model = AutoModel.from_pretrained('{MODEL_NAME}') - -# Tokenize sentences -encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') - -# Compute token embeddings -with torch.no_grad(): - model_output = model(**encoded_input) - -# Perform pooling. In this case, mean pooling. -sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) - -print("Sentence embeddings:") -print(sentence_embeddings) -``` - - - -## Evaluation Results - - - -For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) - - -## Training -The model was trained with the parameters: - -**DataLoader**: - -`torch.utils.data.dataloader.DataLoader` of length 2998 with parameters: -``` -{'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} -``` - -**Loss**: - -`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: - ``` - {'scale': 20.0, 'similarity_fct': 'cos_sim'} - ``` - -Parameters of the fit()-Method: -``` -{ - "epochs": 1, - "evaluation_steps": 0, - "evaluator": "NoneType", - "max_grad_norm": 1, - "optimizer_class": "", - "optimizer_params": { - "correct_bias": false, - "eps": 1e-06, - "lr": 2e-05 - }, - "scheduler": "WarmupLinear", - "steps_per_epoch": null, - "warmup_steps": 299, - "weight_decay": 0.01 -} -``` - - -## Full Model Architecture -``` -SentenceTransformer( - (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel - (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) -) -``` - -## Citing & Authors - - \ No newline at end of file diff --git a/qgen/config.json b/qgen/config.json deleted file mode 100644 index 1e7cfe3a79a81addcacfa9c696888c203f5ba58b..0000000000000000000000000000000000000000 --- a/qgen/config.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "_name_or_path": "GPL/msmarco-distilbert-margin-mse", - "activation": "gelu", - "architectures": [ - "DistilBertModel" - ], - "attention_dropout": 0.1, - "dim": 768, - "dropout": 0.1, - "hidden_dim": 3072, - "initializer_range": 0.02, - "max_position_embeddings": 512, - "model_type": "distilbert", - "n_heads": 12, - "n_layers": 6, - "pad_token_id": 0, - "qa_dropout": 0.1, - "seq_classif_dropout": 0.2, - "sinusoidal_pos_embds": false, - "tie_weights_": true, - "torch_dtype": "float32", - "transformers_version": "4.15.0", - "vocab_size": 30522 -} diff --git a/qgen/config_sentence_transformers.json b/qgen/config_sentence_transformers.json deleted file mode 100644 index 8eabe9973600d631d20cefde5fdd7582ac6a0174..0000000000000000000000000000000000000000 --- a/qgen/config_sentence_transformers.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "__version__": { - "sentence_transformers": "2.1.0", - "transformers": "4.15.0", - "pytorch": "1.10.1+cu102" - } -} \ No newline at end of file diff --git a/qgen/modules.json b/qgen/modules.json deleted file mode 100644 index f7640f94e81bb7f4f04daf1668850b38763a13d9..0000000000000000000000000000000000000000 --- a/qgen/modules.json +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "idx": 0, - "name": "0", - "path": "", - "type": "sentence_transformers.models.Transformer" - }, - { - "idx": 1, - "name": "1", - "path": "1_Pooling", - "type": "sentence_transformers.models.Pooling" - } -] \ No newline at end of file diff --git a/qgen/pytorch_model.bin b/qgen/pytorch_model.bin deleted file mode 100644 index c63dd0a4b6ffac2bb590a54a6be66feb2d8f0db0..0000000000000000000000000000000000000000 --- a/qgen/pytorch_model.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:56ec0c86359e95c3bee8f08824c8fa8a8f9ad6f44033762572e16dbf08f794dd -size 265488185 diff --git a/qgen/sentence_bert_config.json b/qgen/sentence_bert_config.json deleted file mode 100644 index fa624dec36735a16b21478bae5d9a379ebbbed17..0000000000000000000000000000000000000000 --- a/qgen/sentence_bert_config.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "max_seq_length": 350, - "do_lower_case": false -} \ No newline at end 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