Add new SentenceTransformer model.

.gitattributes

@@ -33,3 +33,5 @@ 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
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -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

@@ -0,0 +1,552 @@
+---
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+language:
+- hu
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1044013
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Emberek várnak a lámpánál kerékpárral.
+  sentences:
+  - Az emberek piros lámpánál haladnak.
+  - Az emberek a kerékpárjukon vannak.
+  - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában
+- source_sentence: A kutya a vízben van.
+  sentences:
+  - Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig
+    a tetőn.
+  - A macska a vízben van, és dühös.
+  - Egy kutya van a vízben, a szájában egy faág.
+- source_sentence: A nő feketét visel.
+  sentences:
+  - Egy barna kutya fröcsköl, ahogy úszik a vízben.
+  - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre.
+  - 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:'
+- source_sentence: Az emberek alszanak.
+  sentences:
+  - Három ember beszélget egy városi utcán.
+  - A nő fehéret visel.
+  - Egy apa és a fia ölelgeti alvás közben.
+- source_sentence: Az emberek alszanak.
+  sentences:
+  - Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben
+    egy idősebb nő átmegy az utcán.
+  - Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy
+    sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős
+    elmosódás tesz kivehetetlenné.
+  - Egy apa és a fia ölelgeti alvás közben.
+model-index:
+- name: paraphrase-multilingual-MiniLM-L12-v2-hu
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9918
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.0102
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.99
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.99
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9918
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9937878787878788
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.00803030303030303
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9928787878787879
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9924242424242424
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9937878787878788
+      name: Max Accuracy
+---
+
+# paraphrase-multilingual-MiniLM-L12-v2-hu
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the train dataset. 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision ae06c001a2546bef168b9bf8f570ccb1a16aaa27 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - train
+- **Language:** hu
+- **License:** apache-2.0
+
+### 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': 128, '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})
+)
+```
+
+## 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("karsar/paraphrase-multilingual-MiniLM-L12-hu-v2")
+# Run inference
+sentences = [
+    'Az emberek alszanak.',
+    'Egy apa és a fia ölelgeti alvás közben.',
+    'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+* Dataset: `all-nli-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9918     |
+| dot_accuracy       | 0.0102     |
+| manhattan_accuracy | 0.99       |
+| euclidean_accuracy | 0.99       |
+| **max_accuracy**   | **0.9918** |
+
+#### Triplet
+* Dataset: `all-nli-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9938     |
+| dot_accuracy       | 0.008      |
+| manhattan_accuracy | 0.9929     |
+| euclidean_accuracy | 0.9924     |
+| **max_accuracy**   | **0.9938** |
+
+<!--
+## 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
+
+#### train
+
+* Dataset: train
+* Size: 1,044,013 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                      | negative                                                       |
+  |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------|
+  | <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code> | <code>Egy ember a szabadban, lóháton.</code>  | <code>Egy ember egy étteremben van, és omlettet rendel.</code> |
+  | <code>Gyerekek mosolyogva és integetett a kamera</code>                    | <code>Gyermekek vannak jelen</code>           | <code>A gyerekek homlokot rántanak</code>                      |
+  | <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code>           | <code>A fiú gördeszkás trükköt csinál.</code> | <code>A fiú korcsolyázik a járdán.</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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### train
+
+* Dataset: train
+* Size: 5,000 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                      | negative                                                       |
+  |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------|
+  | <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code> | <code>Egy ember a szabadban, lóháton.</code>  | <code>Egy ember egy étteremben van, és omlettet rendel.</code> |
+  | <code>Gyerekek mosolyogva és integetett a kamera</code>                    | <code>Gyermekek vannak jelen</code>           | <code>A gyerekek homlokot rántanak</code>                      |
+  | <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code>           | <code>A fiú gördeszkás trükköt csinál.</code> | <code>A fiú korcsolyázik a járdán.</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
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `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
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
+|:------:|:----:|:-------------:|:----------:|:------------------------:|:-------------------------:|
+| 0      | 0    | -             | -          | 0.7574                   | -                         |
+| 0.0123 | 100  | 2.5472        | -          | -                        | -                         |
+| 0.0245 | 200  | 2.0478        | -          | -                        | -                         |
+| 0.0368 | 300  | 1.8426        | -          | -                        | -                         |
+| 0.0490 | 400  | 1.698         | -          | -                        | -                         |
+| 0.0613 | 500  | 1.5715        | -          | -                        | -                         |
+| 0.0736 | 600  | 1.4616        | -          | -                        | -                         |
+| 0.0858 | 700  | 1.6106        | -          | -                        | -                         |
+| 0.0981 | 800  | 1.5849        | -          | -                        | -                         |
+| 0.1103 | 900  | 1.5374        | -          | -                        | -                         |
+| 0.1226 | 1000 | 1.3653        | -          | -                        | -                         |
+| 0.1349 | 1100 | 1.274         | -          | -                        | -                         |
+| 0.1471 | 1200 | 1.1907        | -          | -                        | -                         |
+| 0.1594 | 1300 | 1.2155        | -          | -                        | -                         |
+| 0.1716 | 1400 | 1.2786        | -          | -                        | -                         |
+| 0.1839 | 1500 | 1.1062        | -          | -                        | -                         |
+| 0.1962 | 1600 | 1.0289        | -          | -                        | -                         |
+| 0.2084 | 1700 | 1.0013        | -          | -                        | -                         |
+| 0.2207 | 1800 | 0.9209        | -          | -                        | -                         |
+| 0.2329 | 1900 | 0.8095        | -          | -                        | -                         |
+| 0.2452 | 2000 | 0.9753        | 0.1916     | 0.9558                   | -                         |
+| 0.2574 | 2100 | 0.8728        | -          | -                        | -                         |
+| 0.2697 | 2200 | 0.8343        | -          | -                        | -                         |
+| 0.2820 | 2300 | 0.7203        | -          | -                        | -                         |
+| 0.2942 | 2400 | 0.6901        | -          | -                        | -                         |
+| 0.3065 | 2500 | 0.6606        | -          | -                        | -                         |
+| 0.3187 | 2600 | 0.7205        | -          | -                        | -                         |
+| 0.3310 | 2700 | 0.7479        | -          | -                        | -                         |
+| 0.3433 | 2800 | 0.6677        | -          | -                        | -                         |
+| 0.3555 | 2900 | 1.2531        | -          | -                        | -                         |
+| 0.3678 | 3000 | 1.3619        | -          | -                        | -                         |
+| 0.3800 | 3100 | 1.3923        | -          | -                        | -                         |
+| 0.3923 | 3200 | 1.412         | -          | -                        | -                         |
+| 0.4046 | 3300 | 1.3904        | -          | -                        | -                         |
+| 0.4168 | 3400 | 1.3782        | -          | -                        | -                         |
+| 0.4291 | 3500 | 1.3601        | -          | -                        | -                         |
+| 0.4413 | 3600 | 1.3582        | -          | -                        | -                         |
+| 0.4536 | 3700 | 1.3402        | -          | -                        | -                         |
+| 0.4659 | 3800 | 1.32          | -          | -                        | -                         |
+| 0.4781 | 3900 | 1.3277        | -          | -                        | -                         |
+| 0.4904 | 4000 | 1.3112        | 0.0699     | 0.987                    | -                         |
+| 0.5026 | 4100 | 1.2992        | -          | -                        | -                         |
+| 0.5149 | 4200 | 1.3005        | -          | -                        | -                         |
+| 0.5272 | 4300 | 1.2978        | -          | -                        | -                         |
+| 0.5394 | 4400 | 1.272         | -          | -                        | -                         |
+| 0.5517 | 4500 | 1.2864        | -          | -                        | -                         |
+| 0.5639 | 4600 | 1.2519        | -          | -                        | -                         |
+| 0.5762 | 4700 | 1.1924        | -          | -                        | -                         |
+| 0.5885 | 4800 | 1.1778        | -          | -                        | -                         |
+| 0.6007 | 4900 | 1.1801        | -          | -                        | -                         |
+| 0.6130 | 5000 | 1.1666        | -          | -                        | -                         |
+| 0.6252 | 5100 | 1.1682        | -          | -                        | -                         |
+| 0.6375 | 5200 | 1.1518        | -          | -                        | -                         |
+| 0.6497 | 5300 | 1.1606        | -          | -                        | -                         |
+| 0.6620 | 5400 | 1.1534        | -          | -                        | -                         |
+| 0.6743 | 5500 | 1.1473        | -          | -                        | -                         |
+| 0.6865 | 5600 | 1.1596        | -          | -                        | -                         |
+| 0.6988 | 5700 | 1.1536        | -          | -                        | -                         |
+| 0.7110 | 5800 | 1.1517        | -          | -                        | -                         |
+| 0.7233 | 5900 | 1.1517        | -          | -                        | -                         |
+| 0.7356 | 6000 | 1.153         | 0.0359     | 0.9896                   | -                         |
+| 0.7478 | 6100 | 1.142         | -          | -                        | -                         |
+| 0.7601 | 6200 | 1.093         | -          | -                        | -                         |
+| 0.7723 | 6300 | 1.1764        | -          | -                        | -                         |
+| 0.7846 | 6400 | 1.1868        | -          | -                        | -                         |
+| 0.7969 | 6500 | 1.0308        | -          | -                        | -                         |
+| 0.8091 | 6600 | 1.0122        | -          | -                        | -                         |
+| 0.8214 | 6700 | 1.0084        | -          | -                        | -                         |
+| 0.8336 | 6800 | 1.0151        | -          | -                        | -                         |
+| 0.8459 | 6900 | 1.0121        | -          | -                        | -                         |
+| 0.8582 | 7000 | 1.0071        | -          | -                        | -                         |
+| 0.8704 | 7100 | 1.1543        | -          | -                        | -                         |
+| 0.8827 | 7200 | 1.1915        | -          | -                        | -                         |
+| 0.8949 | 7300 | 1.2224        | -          | -                        | -                         |
+| 0.9072 | 7400 | 1.1463        | -          | -                        | -                         |
+| 0.9195 | 7500 | 1.0254        | -          | -                        | -                         |
+| 0.9317 | 7600 | 1.2396        | -          | -                        | -                         |
+| 0.9440 | 7700 | 1.1225        | -          | -                        | -                         |
+| 0.9562 | 7800 | 0.7177        | -          | -                        | -                         |
+| 0.9685 | 7900 | 0.0681        | -          | -                        | -                         |
+| 0.9808 | 8000 | 0.0264        | 0.0317     | 0.9918                   | -                         |
+| 0.9930 | 8100 | 0.078         | -          | -                        | -                         |
+| 1.0    | 8157 | -             | -          | -                        | 0.9938                    |
+
+
+### Framework Versions
+- Python: 3.11.8
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.0
+- PyTorch: 2.3.0.post101
+- Accelerate: 0.33.0
+- Datasets: 3.0.2
+- Tokenizers: 0.19.0
+
+## 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

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.3.0.post101"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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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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+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+  "clean_up_tokenization_spaces": true,
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+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

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