Upload folder using huggingface_hub

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

@@ -0,0 +1,62 @@
+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: google-bert/bert-base-multilingual-cased
+widget:
+- source_sentence: 'search_query: i love autotrain'
+  sentences:
+  - 'search_query: huggingface auto train'
+  - 'search_query: hugging face auto train'
+  - 'search_query: i love autotrain'
+pipeline_tag: sentence-similarity
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 1.0433918237686157
+
+runtime: 63.0935
+
+samples_per_second: 2.599
+
+steps_per_second: 0.174
+
+: 3.0
+
+## 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 Hugging Face Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'search_query: autotrain',
+    'search_query: auto train',
+    'search_query: i love autotrain',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+```

checkpoint-474/1_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
+}

checkpoint-474/README.md

@@ -0,0 +1,414 @@
+---
+base_model: google-bert/bert-base-multilingual-cased
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1890
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 32세 여자가 목을 매다가 가족에게 발견되어 병원에 왔다. 임신 16주였으며 1개월 전부터 식사를 하지 않고 누워만
+    지냈다고 한다. 기분이 우울하고 아무것도 하기가 싫다고 한다. 아이를 잘 키울 자신도 없고 살고 싶지 않으니 죽게 내버려 두라고 한다. 치료는?
+  sentences:
+  - 전기경련요법
+  - 항응고제
+  - 괜찮다고 안심시킴
+- source_sentence: 59세 여자가 질분비물이 있고 외음부가 건조하고 따가워 병원에 왔다. 보습제를 사용하여도 증상이 지속되었다. 40세에
+    자궁근종으로 자궁절제술을 받았고 왼쪽 다리의 깊은정맥혈전증으로 약물을 복용 중이다. 안면홍조와 불면증이 50대 초반에 있었다가 현재는 없고
+    성교통이 있다. 골반검사에서 외음부 위축이 관찰되었고 질분비물의 젖은펴바른표본검사에서는 이상이 없다. 처치는?
+  sentences:
+  - 시상하부기능이상
+  - 경질 에스트로겐
+  - 면허 취소일부터 3년 경과
+- source_sentence: '15세 여자가 5일 전부터 열이 나고 오한이 든다며 병원에 왔다. 음식을 삼킬 때 목이 아프다고 한다. 혈압 100/60
+    mmHg, 맥박 75회/분, 호흡 18회/분, 체온 38.0℃이다. 목의 양쪽 여러 군데에서 1 cm 이하 크기의 림프절이 만져진다. 림프절은
+    압통이 있으나 주위 조직에 고정되어 있지 않다. 몸에서 발진은 보이지 않는다. 혈액검사 결과는 다음과 같다. 다음 검사는?백혈구 13,780/mm^3
+    (중성구 25%, 림프구 64%) 혈색소 13.3 g/dL, 혈소판 209,000/mm^3 혈액요소질소 7 mg/dL,  크레아티닌 0.5
+    mg/dL, 아스파르테이트아미노전달효소 266 U/L 알라닌아미노전달효소 298 U/L  총빌리루빈 0.7 mg/dL, 알칼리인산분해효소 146
+    U/L (참고치, 33～96) C-반응단백질 13 mg/L (참고치, <10) '
+  sentences:
+  - 혈청 바이러스캡시드항원(VCA) IgM 항체
+  - 측정 바이어스
+  - 날트렉손
+- source_sentence: 임신나이 27주, 출생체중 750 g으로 태어난 신생아가 생후 5일째 갑자기 청색증이 발생하였다. 출생 직후 폐표면활성제를
+    투여받았고, 이후 기계환기치료 중이다. 심박 170회/분, 호흡 80회/분, 경피산소포화도는 오른손과 왼발에서 모두 60% 이다. 앞가슴이
+    팽창되고, 오른쪽 가슴 청진에서 호흡음이 잘 들리지 않는다. 검사는?
+  sentences:
+  - 요청에 응함
+  - 비전형적 양상 동반 주요우울장애
+  - 가슴 X선사진
+- source_sentence: '58세 남자가 7시간 전부터 윗배가 아파서 병원에 왔다. 평소에 알코올간경화로 치료를 받고 있으며 소화궤양에 의한
+    천공으로 수술을 받을 예정이다. 혈압 130/90 mmHg, 맥박 95회/분, 호흡 22회/분, 체온 37.5℃이다. 배 전체가 딱딱하고 배에
+    압통과 반동압통이 있다. 혈액검사 결과는 다음과 같다. 수술 전 투여해야 할 제제는?혈색소 10.3 g/dL, 백혈구 22,000/mm^3,
+    혈소판 120,000/mm^3 프로트롬빈시간 20초(참고치, 12.7～15.4) 활성화부분트롬보플라스틴시간 30초(참고치, 26.3～39.4)
+    총단백질 6.5 g/dL, 알부민 3.0 g/dL,총빌리루빈 3.5 mg/dL, '
+  sentences:
+  - “전파 가능성이 이렇게 높은데도 다른 사람에게 전파를 매개하는 행위를 하면 형사처벌을 받을 수도 있습니다.”
+  - 신선동결혈장
+  - 면허자격 정지
+---
+
+# SentenceTransformer based on google-bert/bert-base-multilingual-cased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). 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:** [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
+- **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: BertModel 
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    '58세 남자가 7시간 전부터 윗배가 아파서 병원에 왔다. 평소에 알코올간경화로 치료를 받고 있으며 소화궤양에 의한 천공으로 수술을 받을 예정이다. 혈압 130/90 mmHg, 맥박 95회/분, 호흡 22회/분, 체온 37.5℃이다. 배 전체가 딱딱하고 배에 압통과 반동압통이 있다. 혈액검사 결과는 다음과 같다. 수술 전 투여해야 할 제제는?혈색소 10.3 g/dL, 백혈구 22,000/mm^3, 혈소판 120,000/mm^3 프로트롬빈시간 20초(참고치, 12.7～15.4) 활성화부분트롬보플라스틴시간 30초(참고치, 26.3～39.4) 총단백질 6.5 g/dL, 알부민 3.0 g/dL,총빌리루빈 3.5 mg/dL, ',
+    '신선동결혈장',
+    '면허자격 정지',
+]
+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.*
+-->
+
+<!--
+## 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: 1,890 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                | answer                                                                           |
+  |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                           |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 112.75 tokens</li><li>max: 316 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.62 tokens</li><li>max: 33 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                   | answer                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------|
+  | <code>항문압 측정 검사에서 항문 압력이 증가하는 경우는?</code>                                                                                                                                                                                                                                                                                                                                                               | <code>항문열창(anal fissure)</code>        |
+  | <code>복부대동맥(abdominal aorta) 에서 처음 분지(first branch) 되는 동맥은?</code>                                                                                                                                                                                                                                                                                                                                      | <code>돌잘록창자동맥(ileocolic artery)</code> |
+  | <code>58세 남자가 대량 장절제 후 짧은창자증후군(short bowel syndrome) 으로 4개월 간 완전비경구<br>영양요법을 받고 있는 중이다. 채혈 후 피가 잘 멎지 않았다. 혈액검사 결과는 다음과 같다.<br>결핍이 의심되는 것은?<br>혈색소 13.5 g/dL, 백혈구 4,500/mm^3, 혈소판 220,000/mm^3 <br>알부민 3.7 g/dL, 총 빌리루빈 1.0 mg/dL,  알칼리 인산분해효소(ALP) 90 U/L,<br>아스파르테이트 아미노전달효소(AST) 22 U/L, 알라닌 아미노전달효소(ALT) 16 U/L,<br>프로트롬빈시간 30.5초 (참고치, 12.7~15.4),<br>활성화부분트롬보플라스틴시간 34.5초 (참고치, 26.3~39.4) </code> | <code>트롬빈</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: 164 evaluation samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                | answer                                                                           |
+  |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                           |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 153.24 tokens</li><li>max: 369 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.71 tokens</li><li>max: 40 tokens</li></ul> |
+* Samples:
+  | query                                                                                                      | answer                |
+  |:-----------------------------------------------------------------------------------------------------------|:----------------------|
+  | <code>광역시 소재 대학병원에 소속된 내과 전문의 A가 콜레라 환자를 진단했다. A가 할 조치는?</code>                                            | <code>병원장에게 보고</code> |
+  | <code>A는 제1급 감염병으로 진단을 받았다. B는 마스크를 착용하지 않은 채 A와 밀접하게 접촉했다. B는 증상이 없다. 역학조사관은 이 단계에서 B를 무엇으로 분류하는가?</code> | <code>감염병 의심자</code>  |
+  | <code>검역소 내 격리병동에 격리되어 있던 콜레라 환자 A의 감염력이 없어진 것이 확인되었다. A에 대한 조치는?</code>                                   | <code>격리 해제</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`: epoch
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 3e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `ddp_find_unused_parameters`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `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
+- `learning_rate`: 3e-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`: 3
+- `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`: 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`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   |
+|:------:|:----:|:-------------:|:------:|
+| 0.1055 | 25   | 2.4397        | -      |
+| 0.2110 | 50   | 1.986         | -      |
+| 0.3165 | 75   | 1.881         | -      |
+| 0.4219 | 100  | 1.8105        | -      |
+| 0.5274 | 125  | 1.7378        | -      |
+| 0.6329 | 150  | 1.5942        | -      |
+| 0.7384 | 175  | 1.4586        | -      |
+| 0.8439 | 200  | 1.3904        | -      |
+| 0.9494 | 225  | 1.4707        | -      |
+| 1.0    | 237  | -             | 1.3109 |
+| 1.0549 | 250  | 1.234         | -      |
+| 1.1603 | 275  | 1.1867        | -      |
+| 1.2658 | 300  | 1.0103        | -      |
+| 1.3713 | 325  | 1.088         | -      |
+| 1.4768 | 350  | 1.1066        | -      |
+| 1.5823 | 375  | 1.049         | -      |
+| 1.6878 | 400  | 1.0639        | -      |
+| 1.7932 | 425  | 1.1133        | -      |
+| 1.8987 | 450  | 0.9188        | -      |
+| 2.0    | 474  | -             | 1.0434 |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.2
+- PyTorch: 2.3.0
+- Accelerate: 0.31.0
+- Datasets: 2.19.1
+- 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.*
+-->

checkpoint-474/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "google-bert/bert-base-multilingual-cased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

checkpoint-474/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.2",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

checkpoint-474/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c98dd888e29d9fa10c08e67eeb3c0be1034350309c4ed2d18082c0ebb168ebe9
+size 711436136

checkpoint-474/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"
+  }
+]

checkpoint-474/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bcd0591cf80220cc8de4814d7cd19e9636f9c0452f11f2cf4cf193e021a26db
+size 1418262074

checkpoint-474/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caf2bfebfe820596adc11776ed6db4c892c0efa9a655a7ffeaa1618a8b8a06c3
+size 13990

checkpoint-474/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d450dfea6f4c5a7f460e6c81b97527fa43938d4e7bb4f1fd28c28e1facc75110
+size 1064

checkpoint-474/sentence_bert_config.json

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

checkpoint-474/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

checkpoint-474/tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

checkpoint-474/trainer_state.json

@@ -0,0 +1,184 @@
+{
+  "best_metric": 1.0433918237686157,
+  "best_model_checkpoint": "autotrain-khjb3-w8jbx/checkpoint-474",
+  "epoch": 2.0,
+  "eval_steps": 500,
+  "global_step": 474,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.10548523206751055,
+      "grad_norm": 34.03109359741211,
+      "learning_rate": 1.0416666666666666e-05,
+      "loss": 2.4397,
+      "step": 25
+    },
+    {
+      "epoch": 0.2109704641350211,
+      "grad_norm": 90.00489807128906,
+      "learning_rate": 2.0833333333333333e-05,
+      "loss": 1.986,
+      "step": 50
+    },
+    {
+      "epoch": 0.31645569620253167,
+      "grad_norm": 26.834562301635742,
+      "learning_rate": 2.9859154929577468e-05,
+      "loss": 1.881,
+      "step": 75
+    },
+    {
+      "epoch": 0.4219409282700422,
+      "grad_norm": 19.74114418029785,
+      "learning_rate": 2.8685446009389673e-05,
+      "loss": 1.8105,
+      "step": 100
+    },
+    {
+      "epoch": 0.5274261603375527,
+      "grad_norm": 22.13190460205078,
+      "learning_rate": 2.751173708920188e-05,
+      "loss": 1.7378,
+      "step": 125
+    },
+    {
+      "epoch": 0.6329113924050633,
+      "grad_norm": 20.573028564453125,
+      "learning_rate": 2.6338028169014085e-05,
+      "loss": 1.5942,
+      "step": 150
+    },
+    {
+      "epoch": 0.7383966244725738,
+      "grad_norm": 30.517986297607422,
+      "learning_rate": 2.5164319248826293e-05,
+      "loss": 1.4586,
+      "step": 175
+    },
+    {
+      "epoch": 0.8438818565400844,
+      "grad_norm": 23.457304000854492,
+      "learning_rate": 2.3990610328638498e-05,
+      "loss": 1.3904,
+      "step": 200
+    },
+    {
+      "epoch": 0.9493670886075949,
+      "grad_norm": 31.64182472229004,
+      "learning_rate": 2.2816901408450703e-05,
+      "loss": 1.4707,
+      "step": 225
+    },
+    {
+      "epoch": 1.0,
+      "eval_loss": 1.3109228610992432,
+      "eval_runtime": 60.4201,
+      "eval_samples_per_second": 2.714,
+      "eval_steps_per_second": 0.182,
+      "step": 237
+    },
+    {
+      "epoch": 1.0548523206751055,
+      "grad_norm": 24.206829071044922,
+      "learning_rate": 2.164319248826291e-05,
+      "loss": 1.234,
+      "step": 250
+    },
+    {
+      "epoch": 1.160337552742616,
+      "grad_norm": 15.992789268493652,
+      "learning_rate": 2.0469483568075116e-05,
+      "loss": 1.1867,
+      "step": 275
+    },
+    {
+      "epoch": 1.2658227848101267,
+      "grad_norm": 38.054229736328125,
+      "learning_rate": 1.9295774647887324e-05,
+      "loss": 1.0103,
+      "step": 300
+    },
+    {
+      "epoch": 1.371308016877637,
+      "grad_norm": 23.401416778564453,
+      "learning_rate": 1.812206572769953e-05,
+      "loss": 1.088,
+      "step": 325
+    },
+    {
+      "epoch": 1.4767932489451476,
+      "grad_norm": 16.83168601989746,
+      "learning_rate": 1.694835680751174e-05,
+      "loss": 1.1066,
+      "step": 350
+    },
+    {
+      "epoch": 1.5822784810126582,
+      "grad_norm": 25.251529693603516,
+      "learning_rate": 1.5774647887323945e-05,
+      "loss": 1.049,
+      "step": 375
+    },
+    {
+      "epoch": 1.6877637130801688,
+      "grad_norm": 14.522319793701172,
+      "learning_rate": 1.460093896713615e-05,
+      "loss": 1.0639,
+      "step": 400
+    },
+    {
+      "epoch": 1.7932489451476794,
+      "grad_norm": 28.719419479370117,
+      "learning_rate": 1.3427230046948357e-05,
+      "loss": 1.1133,
+      "step": 425
+    },
+    {
+      "epoch": 1.8987341772151898,
+      "grad_norm": 23.55721664428711,
+      "learning_rate": 1.2253521126760564e-05,
+      "loss": 0.9188,
+      "step": 450
+    },
+    {
+      "epoch": 2.0,
+      "eval_loss": 1.0433918237686157,
+      "eval_runtime": 63.4764,
+      "eval_samples_per_second": 2.584,
+      "eval_steps_per_second": 0.173,
+      "step": 474
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 711,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "EarlyStoppingCallback": {
+      "args": {
+        "early_stopping_patience": 5,
+        "early_stopping_threshold": 0.01
+      },
+      "attributes": {
+        "early_stopping_patience_counter": 0
+      }
+    },
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 8,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-474/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0770d98336ee4ff0dc7e1a9ffa95659023457f080d7a2f628b395701143e049f
+size 5432

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "google-bert/bert-base-multilingual-cased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.2",
+    "pytorch": "2.3.0"
+  },
+  "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:c98dd888e29d9fa10c08e67eeb3c0be1034350309c4ed2d18082c0ebb168ebe9
+size 711436136

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"
+  }
+]

runs/Jun30_05-38-36_r-dbswldnjs-testing-space-f9oh3kzl-8f168-m4v0x/events.out.tfevents.1719725918.r-dbswldnjs-testing-space-f9oh3kzl-8f168-m4v0x.97.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f6fd3ad41cfd9be1e5f178f3ed16fe80685af4b48af7d7104aeb18aa7b5a1b8f
-size 10245
+oid sha256:efa5fd6d42b01705066bd11f9c3ab5fc5a40d9456504a114ac238a41d90c47a2
+size 11292

runs/Jun30_05-38-36_r-dbswldnjs-testing-space-f9oh3kzl-8f168-m4v0x/events.out.tfevents.1719733508.r-dbswldnjs-testing-space-f9oh3kzl-8f168-m4v0x.97.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b780d0580e6b34352a7b3c5d45b904aa9581c933031fbd1c6d7ff3d6a8c50126
+size 359

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0770d98336ee4ff0dc7e1a9ffa95659023457f080d7a2f628b395701143e049f
+size 5432

training_params.json

@@ -0,0 +1,33 @@
+{
+    "data_path": "autotrain-khjb3-w8jbx/autotrain-data",
+    "model": "google-bert/bert-base-multilingual-cased",
+    "lr": 3e-05,
+    "epochs": 3,
+    "max_seq_length": 128,
+    "batch_size": 8,
+    "warmup_ratio": 0.1,
+    "gradient_accumulation": 1,
+    "optimizer": "adamw_torch",
+    "scheduler": "linear",
+    "weight_decay": 0.0,
+    "max_grad_norm": 1.0,
+    "seed": 42,
+    "train_split": "train",
+    "valid_split": "validation",
+    "logging_steps": -1,
+    "project_name": "autotrain-khjb3-w8jbx",
+    "auto_find_batch_size": false,
+    "mixed_precision": "fp16",
+    "save_total_limit": 1,
+    "push_to_hub": true,
+    "eval_strategy": "epoch",
+    "username": "dbswldnjs",
+    "log": "tensorboard",
+    "early_stopping_patience": 5,
+    "early_stopping_threshold": 0.01,
+    "trainer": "qa",
+    "sentence1_column": "autotrain_sentence1",
+    "sentence2_column": "autotrain_sentence2",
+    "sentence3_column": "autotrain_sentence3",
+    "target_column": "autotrain_target"
+}