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.gitattributes +1 -0
1_Pooling/config.json +10 -0
README.md +920 -3
config.json +26 -0
config_sentence_transformers.json +10 -0
model.safetensors +3 -0
modules.json +20 -0
sentence_bert_config.json +4 -0
sentencepiece.bpe.model +3 -0
special_tokens_map.json +51 -0
tokenizer.json +3 -0
tokenizer_config.json +62 -0

.gitattributes CHANGED Viewed

@@ -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

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json ADDED Viewed

	@@ -0,0 +1,10 @@

+{
+  "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 CHANGED Viewed

@@ -1,3 +1,920 @@
----
-license: mit
----

+---
+base_model: agentlans/multilingual-e5-small-aligned
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3000000
+- loss:CoSENTLoss
+widget:
+- source_sentence: Jesus answered them.
+  sentences:
+  - ישוע ענה להם.
+  - आत्ताच नीघ.
+  - Мы надеялись, что дождь прекратится до обеда.
+- source_sentence: Foreign books are sold at the shop.
+  sentences:
+  - Tak, det er alt.
+  - Корабль бросил якорь.
+  - Les livres étrangers sont vendus à la boutique.
+- source_sentence: Cats usually hate dogs.
+  sentences:
+  - Куда вы ходили в прошлое воскресенье?
+  - The bottles of beer that I brought to the party were redundant; the host's family
+    owned a brewery.
+  - Mir tut der Arm weh.
+- source_sentence: How foolish I was not to discover that simple lie!
+  sentences:
+  - Tenho umas perguntas pra fazer, mas não quero te incomodar.
+  - Mi piacciono di più le mele.
+  - Quel idiot j'étais de n'avoir pas découvert ce simple mensonge !
+- source_sentence: Esta es mi amiga Rachel, fuimos al instituto juntos.
+  sentences:
+  - Το σχολείο μας έχει εννιά τάξεις.
+  - When applying to American universities, your TOEFL score is only one factor.
+  - Je n'ai pas encore pris ma décision.
+---
+# SentenceTransformer based on agentlans/multilingual-e5-small-aligned
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [agentlans/multilingual-e5-small-aligned](https://huggingface.co/agentlans/multilingual-e5-small-aligned). 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:** [agentlans/multilingual-e5-small-aligned](https://huggingface.co/agentlans/multilingual-e5-small-aligned) <!-- at revision 2876d21d801703ad25135704219f92d970e48971 -->
+- **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("agentlans/multilingual-e5-small-aligned-v2")
+# Run inference
+sentences = [
+    'Esta es mi amiga Rachel, fuimos al instituto juntos.',
+    "Je n'ai pas encore pris ma décision.",
+    'When applying to American universities, your TOEFL score is only one factor.',
+]
+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: 3,000,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 11.16 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.27 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                | sentence_1                               | label            |
+  |:------------------------------------------|:-----------------------------------------|:-----------------|
+  | <code>Bring your friends with you.</code> | <code>Traga seus amigos com você.</code> | <code>1.0</code> |
+  | <code>I've been there already.</code>     | <code>Você tem algo mais barato?</code>  | <code>0.0</code> |
+  | <code>All my homework is done.</code>     | <code>माझा सगळा होमवर्क झाला आहे.</code> | <code>1.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 3
+- `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 |
+|:------:|:------:|:-------------:|
+| 0.0053 | 500    | 0.835         |
+| 0.0107 | 1000   | 0.7012        |
+| 0.016  | 1500   | 0.6765        |
+| 0.0213 | 2000   | 0.4654        |
+| 0.0267 | 2500   | 0.7546        |
+| 0.032  | 3000   | 0.6098        |
+| 0.0373 | 3500   | 0.644         |
+| 0.0427 | 4000   | 0.5318        |
+| 0.048  | 4500   | 0.5638        |
+| 0.0533 | 5000   | 0.5556        |
+| 0.0587 | 5500   | 0.5165        |
+| 0.064  | 6000   | 0.4083        |
+| 0.0693 | 6500   | 0.4683        |
+| 0.0747 | 7000   | 0.5414        |
+| 0.08   | 7500   | 0.4678        |
+| 0.0853 | 8000   | 0.4225        |
+| 0.0907 | 8500   | 0.4552        |
+| 0.096  | 9000   | 0.4551        |
+| 0.1013 | 9500   | 0.4347        |
+| 0.1067 | 10000  | 0.292         |
+| 0.112  | 10500  | 0.4677        |
+| 0.1173 | 11000  | 0.3567        |
+| 0.1227 | 11500  | 0.4663        |
+| 0.128  | 12000  | 0.4333        |
+| 0.1333 | 12500  | 0.375         |
+| 0.1387 | 13000  | 0.4183        |
+| 0.144  | 13500  | 0.5745        |
+| 0.1493 | 14000  | 0.4569        |
+| 0.1547 | 14500  | 0.426         |
+| 0.16   | 15000  | 0.4903        |
+| 0.1653 | 15500  | 0.4287        |
+| 0.1707 | 16000  | 0.4375        |
+| 0.176  | 16500  | 0.377         |
+| 0.1813 | 17000  | 0.3848        |
+| 0.1867 | 17500  | 0.3366        |
+| 0.192  | 18000  | 0.3784        |
+| 0.1973 | 18500  | 0.399         |
+| 0.2027 | 19000  | 0.3798        |
+| 0.208  | 19500  | 0.3275        |
+| 0.2133 | 20000  | 0.3594        |
+| 0.2187 | 20500  | 0.3555        |
+| 0.224  | 21000  | 0.3565        |
+| 0.2293 | 21500  | 0.4264        |
+| 0.2347 | 22000  | 0.4138        |
+| 0.24   | 22500  | 0.3149        |
+| 0.2453 | 23000  | 0.3397        |
+| 0.2507 | 23500  | 0.359         |
+| 0.256  | 24000  | 0.3311        |
+| 0.2613 | 24500  | 0.3632        |
+| 0.2667 | 25000  | 0.366         |
+| 0.272  | 25500  | 0.2899        |
+| 0.2773 | 26000  | 0.2611        |
+| 0.2827 | 26500  | 0.3497        |
+| 0.288  | 27000  | 0.3534        |
+| 0.2933 | 27500  | 0.273         |
+| 0.2987 | 28000  | 0.3199        |
+| 0.304  | 28500  | 0.2527        |
+| 0.3093 | 29000  | 0.2755        |
+| 0.3147 | 29500  | 0.3684        |
+| 0.32   | 30000  | 0.347         |
+| 0.3253 | 30500  | 0.2537        |
+| 0.3307 | 31000  | 0.3665        |
+| 0.336  | 31500  | 0.2512        |
+| 0.3413 | 32000  | 0.2913        |
+| 0.3467 | 32500  | 0.2619        |
+| 0.352  | 33000  | 0.2573        |
+| 0.3573 | 33500  | 0.3036        |
+| 0.3627 | 34000  | 0.3388        |
+| 0.368  | 34500  | 0.2384        |
+| 0.3733 | 35000  | 0.31          |
+| 0.3787 | 35500  | 0.3461        |
+| 0.384  | 36000  | 0.378         |
+| 0.3893 | 36500  | 0.2409        |
+| 0.3947 | 37000  | 0.2969        |
+| 0.4    | 37500  | 0.2881        |
+| 0.4053 | 38000  | 0.3612        |
+| 0.4107 | 38500  | 0.2662        |
+| 0.416  | 39000  | 0.2796        |
+| 0.4213 | 39500  | 0.3298        |
+| 0.4267 | 40000  | 0.2828        |
+| 0.432  | 40500  | 0.2367        |
+| 0.4373 | 41000  | 0.2661        |
+| 0.4427 | 41500  | 0.393         |
+| 0.448  | 42000  | 0.2875        |
+| 0.4533 | 42500  | 0.203         |
+| 0.4587 | 43000  | 0.3211        |
+| 0.464  | 43500  | 0.3404        |
+| 0.4693 | 44000  | 0.315         |
+| 0.4747 | 44500  | 0.3018        |
+| 0.48   | 45000  | 0.2491        |
+| 0.4853 | 45500  | 0.2584        |
+| 0.4907 | 46000  | 0.2583        |
+| 0.496  | 46500  | 0.3447        |
+| 0.5013 | 47000  | 0.4332        |
+| 0.5067 | 47500  | 0.297         |
+| 0.512  | 48000  | 0.2697        |
+| 0.5173 | 48500  | 0.2349        |
+| 0.5227 | 49000  | 0.2176        |
+| 0.528  | 49500  | 0.2775        |
+| 0.5333 | 50000  | 0.2508        |
+| 0.5387 | 50500  | 0.291         |
+| 0.544  | 51000  | 0.2672        |
+| 0.5493 | 51500  | 0.2638        |
+| 0.5547 | 52000  | 0.2877        |
+| 0.56   | 52500  | 0.2758        |
+| 0.5653 | 53000  | 0.264         |
+| 0.5707 | 53500  | 0.2372        |
+| 0.576  | 54000  | 0.3384        |
+| 0.5813 | 54500  | 0.2459        |
+| 0.5867 | 55000  | 0.3047        |
+| 0.592  | 55500  | 0.1926        |
+| 0.5973 | 56000  | 0.2573        |
+| 0.6027 | 56500  | 0.2816        |
+| 0.608  | 57000  | 0.285         |
+| 0.6133 | 57500  | 0.2397        |
+| 0.6187 | 58000  | 0.1935        |
+| 0.624  | 58500  | 0.3281        |
+| 0.6293 | 59000  | 0.3306        |
+| 0.6347 | 59500  | 0.2067        |
+| 0.64   | 60000  | 0.2483        |
+| 0.6453 | 60500  | 0.2719        |
+| 0.6507 | 61000  | 0.2585        |
+| 0.656  | 61500  | 0.2385        |
+| 0.6613 | 62000  | 0.2229        |
+| 0.6667 | 62500  | 0.2311        |
+| 0.672  | 63000  | 0.2664        |
+| 0.6773 | 63500  | 0.209         |
+| 0.6827 | 64000  | 0.2643        |
+| 0.688  | 64500  | 0.2108        |
+| 0.6933 | 65000  | 0.3063        |
+| 0.6987 | 65500  | 0.1802        |
+| 0.704  | 66000  | 0.2285        |
+| 0.7093 | 66500  | 0.2065        |
+| 0.7147 | 67000  | 0.2467        |
+| 0.72   | 67500  | 0.2178        |
+| 0.7253 | 68000  | 0.2217        |
+| 0.7307 | 68500  | 0.2549        |
+| 0.736  | 69000  | 0.2026        |
+| 0.7413 | 69500  | 0.2609        |
+| 0.7467 | 70000  | 0.2393        |
+| 0.752  | 70500  | 0.1958        |
+| 0.7573 | 71000  | 0.2214        |
+| 0.7627 | 71500  | 0.2079        |
+| 0.768  | 72000  | 0.1574        |
+| 0.7733 | 72500  | 0.2356        |
+| 0.7787 | 73000  | 0.1864        |
+| 0.784  | 73500  | 0.257         |
+| 0.7893 | 74000  | 0.2149        |
+| 0.7947 | 74500  | 0.2519        |
+| 0.8    | 75000  | 0.2746        |
+| 0.8053 | 75500  | 0.2145        |
+| 0.8107 | 76000  | 0.2732        |
+| 0.816  | 76500  | 0.2456        |
+| 0.8213 | 77000  | 0.1841        |
+| 0.8267 | 77500  | 0.1876        |
+| 0.832  | 78000  | 0.2661        |
+| 0.8373 | 78500  | 0.1293        |
+| 0.8427 | 79000  | 0.2018        |
+| 0.848  | 79500  | 0.1854        |
+| 0.8533 | 80000  | 0.1644        |
+| 0.8587 | 80500  | 0.1844        |
+| 0.864  | 81000  | 0.1937        |
+| 0.8693 | 81500  | 0.1486        |
+| 0.8747 | 82000  | 0.244         |
+| 0.88   | 82500  | 0.131         |
+| 0.8853 | 83000  | 0.215         |
+| 0.8907 | 83500  | 0.2398        |
+| 0.896  | 84000  | 0.2014        |
+| 0.9013 | 84500  | 0.1703        |
+| 0.9067 | 85000  | 0.2009        |
+| 0.912  | 85500  | 0.1712        |
+| 0.9173 | 86000  | 0.2649        |
+| 0.9227 | 86500  | 0.2149        |
+| 0.928  | 87000  | 0.1912        |
+| 0.9333 | 87500  | 0.1902        |
+| 0.9387 | 88000  | 0.2609        |
+| 0.944  | 88500  | 0.1846        |
+| 0.9493 | 89000  | 0.1485        |
+| 0.9547 | 89500  | 0.2076        |
+| 0.96   | 90000  | 0.2449        |
+| 0.9653 | 90500  | 0.2025        |
+| 0.9707 | 91000  | 0.2635        |
+| 0.976  | 91500  | 0.2596        |
+| 0.9813 | 92000  | 0.2221        |
+| 0.9867 | 92500  | 0.2168        |
+| 0.992  | 93000  | 0.192         |
+| 0.9973 | 93500  | 0.1966        |
+| 1.0027 | 94000  | 0.2112        |
+| 1.008  | 94500  | 0.1628        |
+| 1.0133 | 95000  | 0.1059        |
+| 1.0187 | 95500  | 0.1403        |
+| 1.024  | 96000  | 0.1726        |
+| 1.0293 | 96500  | 0.1973        |
+| 1.0347 | 97000  | 0.1682        |
+| 1.04   | 97500  | 0.1319        |
+| 1.0453 | 98000  | 0.1427        |
+| 1.0507 | 98500  | 0.1448        |
+| 1.056  | 99000  | 0.1215        |
+| 1.0613 | 99500  | 0.1064        |
+| 1.0667 | 100000 | 0.0856        |
+| 1.072  | 100500 | 0.1046        |
+| 1.0773 | 101000 | 0.1127        |
+| 1.0827 | 101500 | 0.0988        |
+| 1.088  | 102000 | 0.1598        |
+| 1.0933 | 102500 | 0.1592        |
+| 1.0987 | 103000 | 0.1122        |
+| 1.104  | 103500 | 0.0771        |
+| 1.1093 | 104000 | 0.1355        |
+| 1.1147 | 104500 | 0.1265        |
+| 1.12   | 105000 | 0.1464        |
+| 1.1253 | 105500 | 0.1578        |
+| 1.1307 | 106000 | 0.1017        |
+| 1.1360 | 106500 | 0.1047        |
+| 1.1413 | 107000 | 0.1865        |
+| 1.1467 | 107500 | 0.1721        |
+| 1.152  | 108000 | 0.1096        |
+| 1.1573 | 108500 | 0.181         |
+| 1.1627 | 109000 | 0.1261        |
+| 1.168  | 109500 | 0.1111        |
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+| 1.1787 | 110500 | 0.1014        |
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+| 1.2053 | 113000 | 0.095         |
+| 1.2107 | 113500 | 0.1074        |
+| 1.216  | 114000 | 0.1183        |
+| 1.2213 | 114500 | 0.1219        |
+| 1.2267 | 115000 | 0.1264        |
+| 1.232  | 115500 | 0.1339        |
+| 1.2373 | 116000 | 0.0903        |
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+| 1.2747 | 119500 | 0.0469        |
+| 1.28   | 120000 | 0.107         |
+| 1.2853 | 120500 | 0.1578        |
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+| 1.3493 | 126500 | 0.0887        |
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+| 1.3653 | 128000 | 0.087         |
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+| 1.6800 | 157500 | 0.0914        |
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+| 1.7173 | 161000 | 0.0878        |
+| 1.7227 | 161500 | 0.0625        |
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+| 1.7333 | 162500 | 0.1121        |
+| 1.7387 | 163000 | 0.0794        |
+| 1.744  | 163500 | 0.0969        |
+| 1.7493 | 164000 | 0.0696        |
+| 1.7547 | 164500 | 0.083         |
+| 1.76   | 165000 | 0.0702        |
+| 1.7653 | 165500 | 0.0768        |
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+| 1.776  | 166500 | 0.0714        |
+| 1.7813 | 167000 | 0.1           |
+| 1.7867 | 167500 | 0.0665        |
+| 1.792  | 168000 | 0.1139        |
+| 1.7973 | 168500 | 0.1032        |
+| 1.8027 | 169000 | 0.0983        |
+| 1.808  | 169500 | 0.0812        |
+| 1.8133 | 170000 | 0.0996        |
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+| 1.8293 | 171500 | 0.1038        |
+| 1.8347 | 172000 | 0.0558        |
+| 1.8400 | 172500 | 0.0595        |
+| 1.8453 | 173000 | 0.0558        |
+| 1.8507 | 173500 | 0.0717        |
+| 1.8560 | 174000 | 0.058         |
+| 1.8613 | 174500 | 0.0745        |
+| 1.8667 | 175000 | 0.0749        |
+| 1.8720 | 175500 | 0.074         |
+| 1.8773 | 176000 | 0.0792        |
+| 1.8827 | 176500 | 0.0574        |
+| 1.888  | 177000 | 0.0968        |
+| 1.8933 | 177500 | 0.0755        |
+| 1.8987 | 178000 | 0.0852        |
+| 1.904  | 178500 | 0.0502        |
+| 1.9093 | 179000 | 0.0699        |
+| 1.9147 | 179500 | 0.0793        |
+| 1.92   | 180000 | 0.113         |
+| 1.9253 | 180500 | 0.0708        |
+| 1.9307 | 181000 | 0.0815        |
+| 1.936  | 181500 | 0.0962        |
+| 1.9413 | 182000 | 0.083         |
+| 1.9467 | 182500 | 0.0761        |
+| 1.952  | 183000 | 0.0776        |
+| 1.9573 | 183500 | 0.0811        |
+| 1.9627 | 184000 | 0.1159        |
+| 1.968  | 184500 | 0.081         |
+| 1.9733 | 185000 | 0.146         |
+| 1.9787 | 185500 | 0.0715        |
+| 1.984  | 186000 | 0.12          |
+| 1.9893 | 186500 | 0.0692        |
+| 1.9947 | 187000 | 0.07          |
+| 2.0    | 187500 | 0.0935        |
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+| 2.0107 | 188500 | 0.0474        |
+| 2.016  | 189000 | 0.0417        |
+| 2.0213 | 189500 | 0.04          |
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+| 2.0747 | 194500 | 0.043         |
+| 2.08   | 195000 | 0.051         |
+| 2.0853 | 195500 | 0.0339        |
+| 2.0907 | 196000 | 0.0519        |
+| 2.096  | 196500 | 0.0878        |
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+| 2.2613 | 212000 | 0.031         |
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+| 2.4213 | 227000 | 0.0445        |
+| 2.4267 | 227500 | 0.0286        |
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+| 2.7200 | 255000 | 0.0375        |
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+| 2.7467 | 257500 | 0.0333        |
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+| 2.7573 | 258500 | 0.0458        |
+| 2.7627 | 259000 | 0.048         |
+| 2.768  | 259500 | 0.0287        |
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+| 2.8107 | 263500 | 0.0394        |
+| 2.816  | 264000 | 0.0531        |
+| 2.8213 | 264500 | 0.0294        |
+| 2.8267 | 265000 | 0.039         |
+| 2.832  | 265500 | 0.0505        |
+| 2.8373 | 266000 | 0.0167        |
+| 2.8427 | 266500 | 0.031         |
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+| 2.8533 | 267500 | 0.0246        |
+| 2.8587 | 268000 | 0.0317        |
+| 2.864  | 268500 | 0.0296        |
+| 2.8693 | 269000 | 0.0297        |
+| 2.8747 | 269500 | 0.0517        |
+| 2.88   | 270000 | 0.019         |
+| 2.8853 | 270500 | 0.0358        |
+| 2.8907 | 271000 | 0.0589        |
+| 2.896  | 271500 | 0.031         |
+| 2.9013 | 272000 | 0.0421        |
+| 2.9067 | 272500 | 0.0422        |
+| 2.912  | 273000 | 0.016         |
+| 2.9173 | 273500 | 0.0645        |
+| 2.9227 | 274000 | 0.0514        |
+| 2.928  | 274500 | 0.0173        |
+| 2.9333 | 275000 | 0.0432        |
+| 2.9387 | 275500 | 0.0594        |
+| 2.944  | 276000 | 0.0228        |
+| 2.9493 | 276500 | 0.0152        |
+| 2.9547 | 277000 | 0.0579        |
+| 2.96   | 277500 | 0.0578        |
+| 2.9653 | 278000 | 0.0246        |
+| 2.9707 | 278500 | 0.0609        |
+| 2.976  | 279000 | 0.0613        |
+| 2.9813 | 279500 | 0.0589        |
+| 2.9867 | 280000 | 0.047         |
+| 2.992  | 280500 | 0.0264        |
+| 2.9973 | 281000 | 0.0464        |
+</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: 3.2.0
+- 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",
+}
+```
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+<!--
+## 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 ADDED Viewed

	@@ -0,0 +1,26 @@

+{
+  "_name_or_path": "agentlans/multilingual-e5-small-aligned",
+  "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 ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:58a5060c9e53df86bebcc6edca2ace1f23d8712da538a83e609eb25b1dc1d8a3
+size 470637416

modules.json ADDED Viewed

	@@ -0,0 +1,20 @@

+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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 ADDED Viewed

	@@ -0,0 +1,4 @@

+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,51 @@

+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
+size 17083053

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,62 @@

+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}