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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: intfloat/multilingual-e5-small
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:867042
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: An air strike.
+  sentences:
+  - מר פרקינסון היה מזועזע אם היה יודע איך מר פוקס מתנהג.
+  - 'Sonia: Jangan berkata begitu.'
+  - En luftattack.
+- source_sentence: The European Parliament has recently called for a guarantee that
+    40 % of the 10 % target will come from sources that do not compete with food production.
+  sentences:
+  - L' ordre du jour appelle l' examen du projet définitif d' ordre du jour tel qu'
+    il a été établi par la Conférence des présidents, le jeudi 13 janvier, conformément
+    à l' article 110 du règlement.
+  - می توانم با تمام وجود به این باور داشته باشم؟ می توانم در این باره چنین خشمگین
+    باشم؟"
+  - Europaparlamentet ba nylig om en garanti for at 40 % av de 10 % kommer fra kilder
+    som ikke konkurrerer med matvareproduksjon.
+- source_sentence: In effect, this adds to the length of the workday and to its tensions.
+  sentences:
+  - Musimy wysłuchać opinii zainteresowanych stron, które rozwiązanie jest najatrakcyjniejsze
+    dla spółek.
+  - Вам надо держать себя в руках.
+  - درحقیقت ،‏ یہ دن‌بھر کے کام اور اس سے وابستہ دباؤ میں اضافہ کرتا ہے ۔‏
+- source_sentence: A few HIV positive mothers NOT in their first pregnancy (one was
+    in her ninth).
+  sentences:
+  - Beberapa ibu mengidap HIV positif TIDAK di kehamilan pertama mereka (salah satunya
+    bahkan di kehamilan kesembilan).
+  - Taigi, manau, kad taip ir pristatysiu jus – kaip pasakorę".
+  - הוא איפשר ראייה לשני מיליון אנשים ללא תשלום.
+- source_sentence: What do they think it is that prevents the products of human ingenuity
+    from being themselves, fruits of the tree of life, and hence, in some sense, obeying
+    evolutionary rules?
+  sentences:
+  - 'Կարծում եք ի՞նչն է խանգարում, որ մարդկային հնարամտության արդյունքները իրենք էլ
+    լինեն կյանքի ծառի պտուղներ և այդպիսով ինչ-որ իմաստով ենթարկվեն էվոլուցիայի կանոններին:'
+  - Ja mēs varētu aktivēt šūnas, mēs varētu redzēt, kādus spēkus tās var atbrīvot,
+    ko tās var ierosināt un ko stiprināt. Ja mēs tās varētu izslēgt,
+  - (Smiech) No dobre, idem do Ameriky.
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What do they think it is that prevents the products of human ingenuity from being themselves, fruits of the tree of life, and hence, in some sense, obeying evolutionary rules?',
+    'Կարծում եք ի՞նչն է խանգարում, որ մարդկային հնարամտության արդյունքները իրենք էլ լինեն կյանքի ծառի պտուղներ և այդպիսով ինչ-որ իմաստով ենթարկվեն էվոլուցիայի կանոններին:',
+    '(Smiech) No dobre, idem do Ameriky.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 867,042 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 21.83 tokens</li><li>max: 177 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.92 tokens</li><li>max: 229 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                | sentence_1                                                                                                                               |
+  |:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>I like English best of all subjects.</code>                                                                                         | <code>Tykkään englannista eniten kaikista aineista.</code>                                                                               |
+  | <code>We shall offer negotiations. Quite right.</code>                                                                                    | <code>- Oferecer-nos-emos para negociar.</code>                                                                                          |
+  | <code>It was soon learned that Zelaya had been taken to Costa Rica, where he continued to call himself as the legal head of state.</code> | <code>Al snel werd bekend dat Zelaya naar Costa Rica was overgebracht, waar hij zich nog steeds het officiële staatshoofd noemde.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `num_train_epochs`: 1
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.0046 | 500    | 0.0378        |
+| 0.0092 | 1000   | 0.0047        |
+| 0.0138 | 1500   | 0.006         |
+| 0.0185 | 2000   | 0.0045        |
+| 0.0231 | 2500   | 0.0027        |
+| 0.0277 | 3000   | 0.005         |
+| 0.0323 | 3500   | 0.0045        |
+| 0.0369 | 4000   | 0.005         |
+| 0.0415 | 4500   | 0.0066        |
+| 0.0461 | 5000   | 0.0029        |
+| 0.0507 | 5500   | 0.0041        |
+| 0.0554 | 6000   | 0.0064        |
+| 0.0600 | 6500   | 0.0044        |
+| 0.0646 | 7000   | 0.0039        |
+| 0.0692 | 7500   | 0.0025        |
+| 0.0738 | 8000   | 0.0026        |
+| 0.0784 | 8500   | 0.0036        |
+| 0.0830 | 9000   | 0.0027        |
+| 0.0877 | 9500   | 0.0015        |
+| 0.0923 | 10000  | 0.003         |
+| 0.0969 | 10500  | 0.0013        |
+| 0.1015 | 11000  | 0.002         |
+| 0.1061 | 11500  | 0.0038        |
+| 0.1107 | 12000  | 0.0017        |
+| 0.1153 | 12500  | 0.0029        |
+| 0.1199 | 13000  | 0.0032        |
+| 0.1246 | 13500  | 0.0036        |
+| 0.1292 | 14000  | 0.004         |
+| 0.1338 | 14500  | 0.0036        |
+| 0.1384 | 15000  | 0.0025        |
+| 0.1430 | 15500  | 0.0022        |
+| 0.1476 | 16000  | 0.0017        |
+| 0.1522 | 16500  | 0.0019        |
+| 0.1569 | 17000  | 0.0022        |
+| 0.1615 | 17500  | 0.0028        |
+| 0.1661 | 18000  | 0.0033        |
+| 0.1707 | 18500  | 0.0025        |
+| 0.1753 | 19000  | 0.0014        |
+| 0.1799 | 19500  | 0.0033        |
+| 0.1845 | 20000  | 0.0023        |
+| 0.1891 | 20500  | 0.0023        |
+| 0.1938 | 21000  | 0.0009        |
+| 0.1984 | 21500  | 0.0043        |
+| 0.2030 | 22000  | 0.0021        |
+| 0.2076 | 22500  | 0.0025        |
+| 0.2122 | 23000  | 0.0017        |
+| 0.2168 | 23500  | 0.0024        |
+| 0.2214 | 24000  | 0.0021        |
+| 0.2261 | 24500  | 0.0023        |
+| 0.2307 | 25000  | 0.0014        |
+| 0.2353 | 25500  | 0.0027        |
+| 0.2399 | 26000  | 0.0025        |
+| 0.2445 | 26500  | 0.0022        |
+| 0.2491 | 27000  | 0.0022        |
+| 0.2537 | 27500  | 0.0024        |
+| 0.2583 | 28000  | 0.0035        |
+| 0.2630 | 28500  | 0.0032        |
+| 0.2676 | 29000  | 0.0048        |
+| 0.2722 | 29500  | 0.0008        |
+| 0.2768 | 30000  | 0.0027        |
+| 0.2814 | 30500  | 0.004         |
+| 0.2860 | 31000  | 0.0013        |
+| 0.2906 | 31500  | 0.002         |
+| 0.2953 | 32000  | 0.0016        |
+| 0.2999 | 32500  | 0.0027        |
+| 0.3045 | 33000  | 0.0014        |
+| 0.3091 | 33500  | 0.0022        |
+| 0.3137 | 34000  | 0.0017        |
+| 0.3183 | 34500  | 0.0022        |
+| 0.3229 | 35000  | 0.0026        |
+| 0.3275 | 35500  | 0.003         |
+| 0.3322 | 36000  | 0.0022        |
+| 0.3368 | 36500  | 0.0022        |
+| 0.3414 | 37000  | 0.0018        |
+| 0.3460 | 37500  | 0.0028        |
+| 0.3506 | 38000  | 0.0018        |
+| 0.3552 | 38500  | 0.0037        |
+| 0.3598 | 39000  | 0.003         |
+| 0.3645 | 39500  | 0.002         |
+| 0.3691 | 40000  | 0.001         |
+| 0.3737 | 40500  | 0.0015        |
+| 0.3783 | 41000  | 0.0023        |
+| 0.3829 | 41500  | 0.0017        |
+| 0.3875 | 42000  | 0.0034        |
+| 0.3921 | 42500  | 0.0016        |
+| 0.3967 | 43000  | 0.0019        |
+| 0.4014 | 43500  | 0.0015        |
+| 0.4060 | 44000  | 0.0026        |
+| 0.4106 | 44500  | 0.0012        |
+| 0.4152 | 45000  | 0.0014        |
+| 0.4198 | 45500  | 0.0027        |
+| 0.4244 | 46000  | 0.0016        |
+| 0.4290 | 46500  | 0.0027        |
+| 0.4337 | 47000  | 0.0033        |
+| 0.4383 | 47500  | 0.0023        |
+| 0.4429 | 48000  | 0.0024        |
+| 0.4475 | 48500  | 0.0019        |
+| 0.4521 | 49000  | 0.0017        |
+| 0.4567 | 49500  | 0.004         |
+| 0.4613 | 50000  | 0.0036        |
+| 0.4659 | 50500  | 0.001         |
+| 0.4706 | 51000  | 0.0016        |
+| 0.4752 | 51500  | 0.0024        |
+| 0.4798 | 52000  | 0.0009        |
+| 0.4844 | 52500  | 0.0011        |
+| 0.4890 | 53000  | 0.0018        |
+| 0.4936 | 53500  | 0.0012        |
+| 0.4982 | 54000  | 0.0012        |
+| 0.5029 | 54500  | 0.0014        |
+| 0.5075 | 55000  | 0.0025        |
+| 0.5121 | 55500  | 0.0016        |
+| 0.5167 | 56000  | 0.0015        |
+| 0.5213 | 56500  | 0.002         |
+| 0.5259 | 57000  | 0.0008        |
+| 0.5305 | 57500  | 0.0017        |
+| 0.5351 | 58000  | 0.0015        |
+| 0.5398 | 58500  | 0.0009        |
+| 0.5444 | 59000  | 0.0019        |
+| 0.5490 | 59500  | 0.0014        |
+| 0.5536 | 60000  | 0.0028        |
+| 0.5582 | 60500  | 0.0014        |
+| 0.5628 | 61000  | 0.0032        |
+| 0.5674 | 61500  | 0.0013        |
+| 0.5721 | 62000  | 0.002         |
+| 0.5767 | 62500  | 0.0018        |
+| 0.5813 | 63000  | 0.0015        |
+| 0.5859 | 63500  | 0.0008        |
+| 0.5905 | 64000  | 0.0021        |
+| 0.5951 | 64500  | 0.0008        |
+| 0.5997 | 65000  | 0.002         |
+| 0.6043 | 65500  | 0.0023        |
+| 0.6090 | 66000  | 0.0022        |
+| 0.6136 | 66500  | 0.0013        |
+| 0.6182 | 67000  | 0.0011        |
+| 0.6228 | 67500  | 0.0014        |
+| 0.6274 | 68000  | 0.0027        |
+| 0.6320 | 68500  | 0.002         |
+| 0.6366 | 69000  | 0.0013        |
+| 0.6413 | 69500  | 0.0026        |
+| 0.6459 | 70000  | 0.0014        |
+| 0.6505 | 70500  | 0.0017        |
+| 0.6551 | 71000  | 0.0023        |
+| 0.6597 | 71500  | 0.0025        |
+| 0.6643 | 72000  | 0.0013        |
+| 0.6689 | 72500  | 0.0008        |
+| 0.6735 | 73000  | 0.0017        |
+| 0.6782 | 73500  | 0.0022        |
+| 0.6828 | 74000  | 0.0021        |
+| 0.6874 | 74500  | 0.0008        |
+| 0.6920 | 75000  | 0.0007        |
+| 0.6966 | 75500  | 0.0038        |
+| 0.7012 | 76000  | 0.0011        |
+| 0.7058 | 76500  | 0.0016        |
+| 0.7105 | 77000  | 0.0013        |
+| 0.7151 | 77500  | 0.0042        |
+| 0.7197 | 78000  | 0.0009        |
+| 0.7243 | 78500  | 0.0004        |
+| 0.7289 | 79000  | 0.0006        |
+| 0.7335 | 79500  | 0.0007        |
+| 0.7381 | 80000  | 0.0014        |
+| 0.7428 | 80500  | 0.002         |
+| 0.7474 | 81000  | 0.0017        |
+| 0.7520 | 81500  | 0.0014        |
+| 0.7566 | 82000  | 0.0015        |
+| 0.7612 | 82500  | 0.0013        |
+| 0.7658 | 83000  | 0.001         |
+| 0.7704 | 83500  | 0.0019        |
+| 0.7750 | 84000  | 0.0009        |
+| 0.7797 | 84500  | 0.0021        |
+| 0.7843 | 85000  | 0.0015        |
+| 0.7889 | 85500  | 0.001         |
+| 0.7935 | 86000  | 0.0008        |
+| 0.7981 | 86500  | 0.0039        |
+| 0.8027 | 87000  | 0.0018        |
+| 0.8073 | 87500  | 0.0009        |
+| 0.8120 | 88000  | 0.0018        |
+| 0.8166 | 88500  | 0.0008        |
+| 0.8212 | 89000  | 0.0007        |
+| 0.8258 | 89500  | 0.0009        |
+| 0.8304 | 90000  | 0.002         |
+| 0.8350 | 90500  | 0.001         |
+| 0.8396 | 91000  | 0.0007        |
+| 0.8442 | 91500  | 0.0008        |
+| 0.8489 | 92000  | 0.0021        |
+| 0.8535 | 92500  | 0.0013        |
+| 0.8581 | 93000  | 0.0009        |
+| 0.8627 | 93500  | 0.002         |
+| 0.8673 | 94000  | 0.0012        |
+| 0.8719 | 94500  | 0.0034        |
+| 0.8765 | 95000  | 0.0027        |
+| 0.8812 | 95500  | 0.0006        |
+| 0.8858 | 96000  | 0.002         |
+| 0.8904 | 96500  | 0.0005        |
+| 0.8950 | 97000  | 0.0009        |
+| 0.8996 | 97500  | 0.0007        |
+| 0.9042 | 98000  | 0.0015        |
+| 0.9088 | 98500  | 0.0006        |
+| 0.9134 | 99000  | 0.0004        |
+| 0.9181 | 99500  | 0.0006        |
+| 0.9227 | 100000 | 0.0031        |
+| 0.9273 | 100500 | 0.0013        |
+| 0.9319 | 101000 | 0.0024        |
+| 0.9365 | 101500 | 0.0006        |
+| 0.9411 | 102000 | 0.0017        |
+| 0.9457 | 102500 | 0.0007        |
+| 0.9504 | 103000 | 0.0012        |
+| 0.9550 | 103500 | 0.0011        |
+| 0.9596 | 104000 | 0.0007        |
+| 0.9642 | 104500 | 0.0004        |
+| 0.9688 | 105000 | 0.0021        |
+| 0.9734 | 105500 | 0.0027        |
+| 0.9780 | 106000 | 0.0016        |
+| 0.9826 | 106500 | 0.0022        |
+| 0.9873 | 107000 | 0.0017        |
+| 0.9919 | 107500 | 0.0009        |
+| 0.9965 | 108000 | 0.0008        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.0
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.1.1
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "intfloat/multilingual-e5-small",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.0",
+    "transformers": "4.46.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 470637416

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+{
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+    "rstrip": false,
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+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
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+}

tokenizer.json

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+oid sha256:ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
+size 17083053

tokenizer_config.json

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+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}