Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: huudan123/stage1
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:254546
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: em_gái grany người da trắng cô ấy muốn đi học
+  sentences:
+  - bà thường kể câu_chuyện về chị_gái bà người chồng quyết_định chuyển đến thành_phố
+    augusta chuyển sang màu trắng
+  - thêm thời_gian thông_thường thêm phát_triển kế_hoạch hành_động
+  - em_gái grany người da trắng
+- source_sentence: hãy họ biết họ cố_gắng cản_trở_việc chèo thuyền chúng_tôi chúng_tôi
+    treo đầu_tiên doxy chiến_đấu nó
+  sentences:
+  - tôi biết mình hướng tới mục_đích báo_cáo một địa_chỉ ở washington
+  - chúng_ta cố_gắng chiến_đấu nó một_khi chúng_ta bắt_đầu_ra khơi
+  - cách nào biết liệu con thuyền đi thẳng
+- source_sentence: louisa may alcot nathaniel hawthorne sống phố pinckney trong phố
+    beacon oliver wendel holmes gọi con đường đầy nắng nhà sử_học wiliam prescot
+  sentences:
+  - hawthorne sống phố pinckney trong 7 năm
+  - hawthorne sống phố pinckney
+  - dùng tất_cả hiệu_quả trong phòng_chống thói_quen xấu nó hiệu_quả trong điều_trị
+    nói_chung
+- source_sentence: hình 6 hiển_thị chi_phí đơn_vị trung_bình tạo hàm chi_phí usps
+  sentences:
+  - chi_phí trung_bình usps thể_hiện trong hình 6
+  - chi_phí trung_bình usps thể_hiện trong hình 6 thấy tất_cả lợi_nhuận
+  - cấp đại_úy blod một khoản hoa_hồng một sai_lầm sai_lầm đấy tôi
+- source_sentence: bạn tiếp_tục nhập thông_tin cơ_sở dữ_liệu
+  sentences:
+  - mặc_dù dứa hương_vị tuyệt_vời chi_phí vận_chuyển quá cao đưa chúng thị_trường
+  - bạn tiếp_tục bạn nhập mọi thứ
+  - bạn mọi thứ bạn bắt_đầu_từ
+model-index:
+- name: SentenceTransformer based on huudan123/stage1
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.7132925999347621
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7139908860784119
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.6924068767142901
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.6987187512790664
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.6927853521211202
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.6988256048265301
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.6562289766339777
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6552808237632588
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7132925999347621
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.7139908860784119
+      name: Spearman Max
+---
+
+# SentenceTransformer based on huudan123/stage1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/stage1](https://huggingface.co/huudan123/stage1). It maps sentences & paragraphs to a 768-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:** [huudan123/stage1](https://huggingface.co/huudan123/stage1) <!-- at revision 2af9d99bbe23c419d648a4eef0dd24d5b788921d -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, '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("huudan123/stage2")
+# Run inference
+sentences = [
+    'bạn tiếp_tục nhập thông_tin cơ_sở dữ_liệu',
+    'bạn mọi thứ bạn bắt_đầu_từ',
+    'bạn tiếp_tục bạn nhập mọi thứ',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.7133    |
+| **spearman_cosine** | **0.714** |
+| pearson_manhattan   | 0.6924    |
+| spearman_manhattan  | 0.6987    |
+| pearson_euclidean   | 0.6928    |
+| spearman_euclidean  | 0.6988    |
+| pearson_dot         | 0.6562    |
+| spearman_dot        | 0.6553    |
+| pearson_max         | 0.7133    |
+| spearman_max        | 0.714     |
+
+<!--
+## 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: 254,546 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: 3 tokens</li><li>mean: 14.78 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.78 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.19 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                        | positive                                                                   | negative                                               |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-------------------------------------------------------|
+  | <code>conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý</code>                                                                                                                                                                                    | <code>sản_phẩm địa_lý làm kem skiming làm_việc</code>                      | <code>kem skiming hai tập_trung sản_phẩm địa_lý</code> |
+  | <code>sản_phẩm địa_lý làm kem skiming làm_việc</code>                                                                                                                                                                                                         | <code>conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý</code> | <code>kem skiming hai tập_trung sản_phẩm địa_lý</code> |
+  | <code>bạn biết trong mùa giải tôi đoán ở mức_độ bạn bạn mất chúng đến mức_độ tiếp_theo họ quyết_định nhớ đội_ngũ cha_mẹ chiến_binh quyết_định gọi nhớ một người ba a một người đàn_ông đi đến thay_thế anh ta một người đàn_ông nào đi thay_thế anh ta</code> | <code>recals thực_hiện thứ sáu</code>                                      | <code>anh mất mọi thứ ở mức_độ người dân nhớ</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
+
+#### Unnamed Dataset
+
+
+* Size: 1,660 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: 4 tokens</li><li>mean: 13.54 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.54 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.78 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | anchor                                                                         | positive                                                                   | negative                                                                            |
+  |:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | <code>anh ấy nói mẹ con về nhà</code>                                          | <code>xuống xe_buýt trường anh ấy gọi mẹ</code>                            | <code>anh nói mẹ anh về nhà</code>                                                  |
+  | <code>xuống xe_buýt trường anh ấy gọi mẹ</code>                                | <code>anh ấy nói mẹ con về nhà</code>                                      | <code>anh nói mẹ anh về nhà</code>                                                  |
+  | <code>tôi biết mình hướng tới mục_đích báo_cáo một địa_chỉ ở washington</code> | <code>tôi bao_giờ đến washington tôi chỉ_định ở tôi lạc cố_gắng tìm</code> | <code>tôi hoàn_toàn chắc_chắn tôi làm tôi đi đến washington tôi giao báo_cáo</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
+
+- `overwrite_output_dir`: True
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `num_train_epochs`: 20
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.05
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `gradient_checkpointing`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: True
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 20
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: True
+- `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`: True
+- `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`: True
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step    | Training Loss | loss       | sts-dev_spearman_cosine |
+|:-------:|:-------:|:-------------:|:----------:|:-----------------------:|
+| 0       | 0       | -             | -          | 0.5307                  |
+| 0.0503  | 50      | 9.1742        | -          | -                       |
+| 0.1005  | 100     | 5.9716        | -          | -                       |
+| 0.1508  | 150     | 4.6737        | -          | -                       |
+| 0.2010  | 200     | 3.2819        | -          | -                       |
+| 0.2513  | 250     | 2.8832        | -          | -                       |
+| 0.3015  | 300     | 2.7327        | -          | -                       |
+| 0.3518  | 350     | 2.6305        | -          | -                       |
+| 0.4020  | 400     | 2.6239        | -          | -                       |
+| 0.4523  | 450     | 2.5527        | -          | -                       |
+| 0.5025  | 500     | 2.5271        | -          | -                       |
+| 0.5528  | 550     | 2.4904        | -          | -                       |
+| 0.6030  | 600     | 2.4987        | -          | -                       |
+| 0.6533  | 650     | 2.4009        | -          | -                       |
+| 0.7035  | 700     | 2.3944        | -          | -                       |
+| 0.7538  | 750     | 2.5054        | -          | -                       |
+| 0.8040  | 800     | 2.3989        | -          | -                       |
+| 0.8543  | 850     | 2.4019        | -          | -                       |
+| 0.9045  | 900     | 2.3638        | -          | -                       |
+| 0.9548  | 950     | 2.3478        | -          | -                       |
+| **1.0** | **995** | **-**         | **3.0169** | **0.7322**              |
+| 1.0050  | 1000    | 2.4424        | -          | -                       |
+| 1.0553  | 1050    | 2.2478        | -          | -                       |
+| 1.1055  | 1100    | 2.2448        | -          | -                       |
+| 1.1558  | 1150    | 2.205         | -          | -                       |
+| 1.2060  | 1200    | 2.1811        | -          | -                       |
+| 1.2563  | 1250    | 2.1794        | -          | -                       |
+| 1.3065  | 1300    | 2.1495        | -          | -                       |
+| 1.3568  | 1350    | 2.1548        | -          | -                       |
+| 1.4070  | 1400    | 2.1299        | -          | -                       |
+| 1.4573  | 1450    | 2.1335        | -          | -                       |
+| 1.5075  | 1500    | 2.1388        | -          | -                       |
+| 1.5578  | 1550    | 2.0999        | -          | -                       |
+| 1.6080  | 1600    | 2.0859        | -          | -                       |
+| 1.6583  | 1650    | 2.0959        | -          | -                       |
+| 1.7085  | 1700    | 2.0334        | -          | -                       |
+| 1.7588  | 1750    | 2.0647        | -          | -                       |
+| 1.8090  | 1800    | 2.0261        | -          | -                       |
+| 1.8593  | 1850    | 2.0133        | -          | -                       |
+| 1.9095  | 1900    | 2.0517        | -          | -                       |
+| 1.9598  | 1950    | 2.0152        | -          | -                       |
+| 2.0     | 1990    | -             | 3.1210     | 0.7187                  |
+| 2.0101  | 2000    | 1.924         | -          | -                       |
+| 2.0603  | 2050    | 1.7472        | -          | -                       |
+| 2.1106  | 2100    | 1.7485        | -          | -                       |
+| 2.1608  | 2150    | 1.7536        | -          | -                       |
+| 2.2111  | 2200    | 1.751         | -          | -                       |
+| 2.2613  | 2250    | 1.7172        | -          | -                       |
+| 2.3116  | 2300    | 1.7269        | -          | -                       |
+| 2.3618  | 2350    | 1.7352        | -          | -                       |
+| 2.4121  | 2400    | 1.7019        | -          | -                       |
+| 2.4623  | 2450    | 1.7278        | -          | -                       |
+| 2.5126  | 2500    | 1.7046        | -          | -                       |
+| 2.5628  | 2550    | 1.6962        | -          | -                       |
+| 2.6131  | 2600    | 1.6881        | -          | -                       |
+| 2.6633  | 2650    | 1.6806        | -          | -                       |
+| 2.7136  | 2700    | 1.6614        | -          | -                       |
+| 2.7638  | 2750    | 1.6918        | -          | -                       |
+| 2.8141  | 2800    | 1.6794        | -          | -                       |
+| 2.8643  | 2850    | 1.6708        | -          | -                       |
+| 2.9146  | 2900    | 1.6531        | -          | -                       |
+| 2.9648  | 2950    | 1.6236        | -          | -                       |
+| 3.0     | 2985    | -             | 3.2556     | 0.7140                  |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<mask>": 64000
+}

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "./output/final_model",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "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": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 258,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a55ac6a99ec9219cba51bf6db6e38a7e2b9c226366c07e0adc0d4b1365ff8ba1
+size 540015464

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -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_config.json

@@ -0,0 +1,54 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "64000": {
+      "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>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "PhobertTokenizer",
+  "unk_token": "<unk>"
+}