Add new SentenceTransformer model.

.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

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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 +1,599 @@
+---
+base_model: BAAI/bge-m3
+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:200000
+- 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: gte_hun
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.979
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.021
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9804
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.979
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9804
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.979
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.021
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9804
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.979
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9804
+      name: Max Accuracy
+---
+
+# gte_hun
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the train dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("karsar/bge-m3-hu")
+# 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, 1024]
+
+# 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    | 0.979      |
+| dot_accuracy       | 0.021      |
+| manhattan_accuracy | 0.9804     |
+| euclidean_accuracy | 0.979      |
+| **max_accuracy**   | **0.9804** |
+
+#### 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    | 0.979      |
+| dot_accuracy       | 0.021      |
+| manhattan_accuracy | 0.9804     |
+| euclidean_accuracy | 0.979      |
+| **max_accuracy**   | **0.9804** |
+
+<!--
+## 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: 200,000 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `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
+<details><summary>Click to expand</summary>
+
+| Epoch | Step  | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
+|:-----:|:-----:|:-------------:|:----------:|:------------------------:|:-------------------------:|
+| 0     | 0     | -             | -          | 0.7176                   | -                         |
+| 0.008 | 100   | 1.0753        | -          | -                        | -                         |
+| 0.016 | 200   | 0.7611        | -          | -                        | -                         |
+| 0.024 | 300   | 1.0113        | -          | -                        | -                         |
+| 0.032 | 400   | 0.6224        | -          | -                        | -                         |
+| 0.04  | 500   | 0.8465        | 0.6159     | 0.8938                   | -                         |
+| 0.048 | 600   | 0.7761        | -          | -                        | -                         |
+| 0.056 | 700   | 0.8738        | -          | -                        | -                         |
+| 0.064 | 800   | 0.9393        | -          | -                        | -                         |
+| 0.072 | 900   | 0.9743        | -          | -                        | -                         |
+| 0.08  | 1000  | 0.8445        | 0.4556     | 0.8916                   | -                         |
+| 0.088 | 1100  | 0.7237        | -          | -                        | -                         |
+| 0.096 | 1200  | 0.8064        | -          | -                        | -                         |
+| 0.104 | 1300  | 0.607         | -          | -                        | -                         |
+| 0.112 | 1400  | 0.7632        | -          | -                        | -                         |
+| 0.12  | 1500  | 0.7477        | 1.6880     | 0.6748                   | -                         |
+| 0.128 | 1600  | 1.018         | -          | -                        | -                         |
+| 0.136 | 1700  | 0.9046        | -          | -                        | -                         |
+| 0.144 | 1800  | 0.728         | -          | -                        | -                         |
+| 0.152 | 1900  | 0.7219        | -          | -                        | -                         |
+| 0.16  | 2000  | 0.632         | 0.6459     | 0.8622                   | -                         |
+| 0.168 | 2100  | 0.6067        | -          | -                        | -                         |
+| 0.176 | 2200  | 0.7267        | -          | -                        | -                         |
+| 0.184 | 2300  | 0.781         | -          | -                        | -                         |
+| 0.192 | 2400  | 0.662         | -          | -                        | -                         |
+| 0.2   | 2500  | 0.6192        | 1.0124     | 0.8328                   | -                         |
+| 0.208 | 2600  | 0.7943        | -          | -                        | -                         |
+| 0.216 | 2700  | 0.8762        | -          | -                        | -                         |
+| 0.224 | 2800  | 0.7913        | -          | -                        | -                         |
+| 0.232 | 2900  | 0.8049        | -          | -                        | -                         |
+| 0.24  | 3000  | 0.858         | 0.6378     | 0.8046                   | -                         |
+| 0.248 | 3100  | 0.679         | -          | -                        | -                         |
+| 0.256 | 3200  | 0.7213        | -          | -                        | -                         |
+| 0.264 | 3300  | 0.6028        | -          | -                        | -                         |
+| 0.272 | 3400  | 0.5778        | -          | -                        | -                         |
+| 0.28  | 3500  | 0.5434        | 0.6784     | 0.8496                   | -                         |
+| 0.288 | 3600  | 0.6726        | -          | -                        | -                         |
+| 0.296 | 3700  | 0.7347        | -          | -                        | -                         |
+| 0.304 | 3800  | 0.8413        | -          | -                        | -                         |
+| 0.312 | 3900  | 0.7993        | -          | -                        | -                         |
+| 0.32  | 4000  | 0.8899        | 0.7732     | 0.8092                   | -                         |
+| 0.328 | 4100  | 1.1505        | -          | -                        | -                         |
+| 0.336 | 4200  | 0.8871        | -          | -                        | -                         |
+| 0.344 | 4300  | 0.8423        | -          | -                        | -                         |
+| 0.352 | 4400  | 0.8288        | -          | -                        | -                         |
+| 0.36  | 4500  | 0.6728        | 0.6341     | 0.8436                   | -                         |
+| 0.368 | 4600  | 0.7534        | -          | -                        | -                         |
+| 0.376 | 4700  | 0.8276        | -          | -                        | -                         |
+| 0.384 | 4800  | 0.7677        | -          | -                        | -                         |
+| 0.392 | 4900  | 0.588         | -          | -                        | -                         |
+| 0.4   | 5000  | 0.7742        | 0.4389     | 0.8808                   | -                         |
+| 0.408 | 5100  | 0.6782        | -          | -                        | -                         |
+| 0.416 | 5200  | 0.6688        | -          | -                        | -                         |
+| 0.424 | 5300  | 0.5579        | -          | -                        | -                         |
+| 0.432 | 5400  | 0.6891        | -          | -                        | -                         |
+| 0.44  | 5500  | 0.5764        | 0.4192     | 0.902                    | -                         |
+| 0.448 | 5600  | 0.6152        | -          | -                        | -                         |
+| 0.456 | 5700  | 0.6864        | -          | -                        | -                         |
+| 0.464 | 5800  | 0.6429        | -          | -                        | -                         |
+| 0.472 | 5900  | 0.9379        | -          | -                        | -                         |
+| 0.48  | 6000  | 0.7607        | 0.4744     | 0.8736                   | -                         |
+| 0.488 | 6100  | 0.819         | -          | -                        | -                         |
+| 0.496 | 6200  | 0.6316        | -          | -                        | -                         |
+| 0.504 | 6300  | 0.8175        | -          | -                        | -                         |
+| 0.512 | 6400  | 0.8485        | -          | -                        | -                         |
+| 0.52  | 6500  | 0.5374        | 0.4860     | 0.916                    | -                         |
+| 0.528 | 6600  | 0.781         | -          | -                        | -                         |
+| 0.536 | 6700  | 0.7722        | -          | -                        | -                         |
+| 0.544 | 6800  | 0.7281        | -          | -                        | -                         |
+| 0.552 | 6900  | 0.8453        | -          | -                        | -                         |
+| 0.56  | 7000  | 0.8541        | 0.2612     | 0.9322                   | -                         |
+| 0.568 | 7100  | 0.9698        | -          | -                        | -                         |
+| 0.576 | 7200  | 0.7184        | -          | -                        | -                         |
+| 0.584 | 7300  | 0.699         | -          | -                        | -                         |
+| 0.592 | 7400  | 0.5574        | -          | -                        | -                         |
+| 0.6   | 7500  | 0.5374        | 0.1939     | 0.9472                   | -                         |
+| 0.608 | 7600  | 0.6485        | -          | -                        | -                         |
+| 0.616 | 7700  | 0.5177        | -          | -                        | -                         |
+| 0.624 | 7800  | 0.814         | -          | -                        | -                         |
+| 0.632 | 7900  | 0.6442        | -          | -                        | -                         |
+| 0.64  | 8000  | 0.5301        | 0.1192     | 0.9616                   | -                         |
+| 0.648 | 8100  | 0.4948        | -          | -                        | -                         |
+| 0.656 | 8200  | 0.426         | -          | -                        | -                         |
+| 0.664 | 8300  | 0.4781        | -          | -                        | -                         |
+| 0.672 | 8400  | 0.4188        | -          | -                        | -                         |
+| 0.68  | 8500  | 0.5695        | 0.1523     | 0.9492                   | -                         |
+| 0.688 | 8600  | 0.3895        | -          | -                        | -                         |
+| 0.696 | 8700  | 0.5041        | -          | -                        | -                         |
+| 0.704 | 8800  | 0.7599        | -          | -                        | -                         |
+| 0.712 | 8900  | 0.5893        | -          | -                        | -                         |
+| 0.72  | 9000  | 0.6678        | 0.1363     | 0.9588                   | -                         |
+| 0.728 | 9100  | 0.5917        | -          | -                        | -                         |
+| 0.736 | 9200  | 0.6201        | -          | -                        | -                         |
+| 0.744 | 9300  | 0.5072        | -          | -                        | -                         |
+| 0.752 | 9400  | 0.4233        | -          | -                        | -                         |
+| 0.76  | 9500  | 0.396         | 0.2490     | 0.937                    | -                         |
+| 0.768 | 9600  | 0.3699        | -          | -                        | -                         |
+| 0.776 | 9700  | 0.3734        | -          | -                        | -                         |
+| 0.784 | 9800  | 0.4145        | -          | -                        | -                         |
+| 0.792 | 9900  | 0.4422        | -          | -                        | -                         |
+| 0.8   | 10000 | 0.4427        | 0.1394     | 0.9634                   | -                         |
+| 0.808 | 10100 | 0.678         | -          | -                        | -                         |
+| 0.816 | 10200 | 0.6771        | -          | -                        | -                         |
+| 0.824 | 10300 | 0.8249        | -          | -                        | -                         |
+| 0.832 | 10400 | 0.5003        | -          | -                        | -                         |
+| 0.84  | 10500 | 0.5586        | 0.1006     | 0.9726                   | -                         |
+| 0.848 | 10600 | 0.4649        | -          | -                        | -                         |
+| 0.856 | 10700 | 0.5322        | -          | -                        | -                         |
+| 0.864 | 10800 | 0.4837        | -          | -                        | -                         |
+| 0.872 | 10900 | 0.5717        | -          | -                        | -                         |
+| 0.88  | 11000 | 0.4403        | 0.1009     | 0.9688                   | -                         |
+| 0.888 | 11100 | 0.5044        | -          | -                        | -                         |
+| 0.896 | 11200 | 0.4771        | -          | -                        | -                         |
+| 0.904 | 11300 | 0.4426        | -          | -                        | -                         |
+| 0.912 | 11400 | 0.3705        | -          | -                        | -                         |
+| 0.92  | 11500 | 0.4445        | 0.0992     | 0.978                    | -                         |
+| 0.928 | 11600 | 0.3707        | -          | -                        | -                         |
+| 0.936 | 11700 | 0.4322        | -          | -                        | -                         |
+| 0.944 | 11800 | 0.4619        | -          | -                        | -                         |
+| 0.952 | 11900 | 0.4772        | -          | -                        | -                         |
+| 0.96  | 12000 | 0.5756        | 0.0950     | 0.9804                   | -                         |
+| 0.968 | 12100 | 0.5649        | -          | -                        | -                         |
+| 0.976 | 12200 | 0.5037        | -          | -                        | -                         |
+| 0.984 | 12300 | 0.0317        | -          | -                        | -                         |
+| 0.992 | 12400 | 0.0001        | -          | -                        | -                         |
+| 1.0   | 12500 | 0.0001        | 0.0948     | 0.9804                   | 0.9804                    |
+
+</details>
+
+### 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: 2.18.0
+- 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

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+{
+  "_name_or_path": "BAAI/bge-m3",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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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modules.json

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

sentence_bert_config.json

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+{
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+    "rstrip": false,
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+    "lstrip": false,
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+  },
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+    "content": "<mask>",
+    "lstrip": true,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
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+    "single_word": false
+  }
+}

tokenizer.json

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+{
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+    "0": {
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+  "model_max_length": 8192,
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}