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": 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
+}

2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 1024, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:974b3f473305126ec5c65b1bb2397dabfb5c1c727c3a2e888ddc29ad16b2677a
+size 3149984

README.md

@@ -0,0 +1,421 @@
+---
+base_model: kornwtp/ConGen-BGE_M3-model-phayathaibert
+datasets:
+- sentence-transformers/all-nli
+language:
+- en
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:557850
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: A man is jumping unto his filthy bed.
+  sentences:
+  - A young male is looking at a newspaper while 2 females walks past him.
+  - The bed is dirty.
+  - The man is on the moon.
+- source_sentence: A carefully balanced male stands on one foot near a clean ocean
+    beach area.
+  sentences:
+  - A man is ouside near the beach.
+  - Three policemen patrol the streets on bikes
+  - A man is sitting on his couch.
+- source_sentence: The man is wearing a blue shirt.
+  sentences:
+  - Near the trashcan the man stood and smoked
+  - A man in a blue shirt leans on a wall beside a road with a blue van and red car
+    with water in the background.
+  - A man in a black shirt is playing a guitar.
+- source_sentence: The girls are outdoors.
+  sentences:
+  - Two girls riding on an amusement part ride.
+  - a guy laughs while doing laundry
+  - Three girls are standing together in a room, one is listening, one is writing
+    on a wall and the third is talking to them.
+- source_sentence: A construction worker peeking out of a manhole while his coworker
+    sits on the sidewalk smiling.
+  sentences:
+  - A worker is looking out of a manhole.
+  - A man is giving a presentation.
+  - The workers are both inside the manhole.
+model-index:
+- name: SentenceTransformer based on kornwtp/ConGen-BGE_M3-model-phayathaibert
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8217582085035557
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on kornwtp/ConGen-BGE_M3-model-phayathaibert
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [kornwtp/ConGen-BGE_M3-model-phayathaibert](https://huggingface.co/kornwtp/ConGen-BGE_M3-model-phayathaibert) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** [kornwtp/ConGen-BGE_M3-model-phayathaibert](https://huggingface.co/kornwtp/ConGen-BGE_M3-model-phayathaibert) <!-- at revision ace614ebc26a7232b5512651a25ede1aeec2562e -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: CamembertModel 
+  (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})
+  (2): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+
+## 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("FlukeTJ/test-model-congen-mpnet-base-all-nli-triplet")
+# Run inference
+sentences = [
+    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
+    'A worker is looking out of a manhole.',
+    'The workers are both inside the manhole.',
+]
+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-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.8218** |
+
+<!--
+## 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
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 557,850 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: 8 tokens</li><li>mean: 12.43 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.87 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 16.17 tokens</li><li>max: 55 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                         | negative                                                   |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 6,584 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: 21.73 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.18 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.85 tokens</li><li>max: 41 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
+  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: 30
+- `per_device_eval_batch_size`: 30
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 30
+- `per_device_eval_batch_size`: 30
+- `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`: 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`: 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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | all-nli-test_cosine_accuracy |
+|:-----:|:----:|:----------------------------:|
+| 1.0   | 84   | 0.8218                       |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.3.1
+- Transformers: 4.45.1
+- PyTorch: 2.4.0
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,837 @@
+{
+  "‼": 248733,
+  "⁉": 248850,
+  "⌛": 248707,
+  "⌨": 248450,
+  "⏏": 248572,
+  "⏫": 248890,
+  "⏭": 249212,
+  "⏮": 248920,
+  "⏯": 248576,
+  "⏲": 248922,
+  "⏸": 249193,
+  "⏹": 249213,
+  "Ⓜ": 248957,
+  "♉": 248638,
+  "♊": 248741,
+  "♌": 249062,
+  "♍": 248609,
+  "♎": 248611,
+  "♏": 249129,
+  "♑": 248994,
+  "♓": 248790,
+  "♾": 248725,
+  "♿": 248454,
+  "⚒": 248845,
+  "⚖": 248534,
+  "⚗": 248943,
+  "⚙": 248676,
+  "⚛": 248761,
+  "⚧": 248971,
+  "⚰": 248930,
+  "⚱": 249010,
+  "⛈": 249232,
+  "⛎": 248516,
+  "⛏": 248490,
+  "⛑": 248575,
+  "⛩": 248953,
+  "⛪": 249120,
+  "⛲": 249141,
+  "⛴": 248779,
+  "⛷": 248649,
+  "⛸": 248693,
+  "⛹": 249260,
+  "⛽": 249248,
+  "✝": 248664,
+  "➗": 248803,
+  "➰": 248665,
+  "➿": 248458,
+  "⬛": 249000,
+  "⬜": 248821,
+  "〽": 248791,
+  "㊗": 248521,
+  "㊙": 249060,
+  "🀄": 248929,
+  "🃏": 248468,
+  "🅿": 248755,
+  "🆎": 248619,
+  "🆒": 248798,
+  "🆕": 248655,
+  "🆖": 248837,
+  "🆗": 248917,
+  "🆙": 248978,
+  "🆚": 248972,
+  "🈁": 248495,
+  "🈂": 248844,
+  "🈚": 249053,
+  "🈯": 248568,
+  "🈲": 248512,
+  "🈳": 248785,
+  "🈴": 249003,
+  "🈵": 248752,
+  "🈶": 248502,
+  "🈷": 249153,
+  "🈸": 249079,
+  "🈹": 248690,
+  "🈺": 248472,
+  "🉐": 249070,
+  "🉑": 249224,
+  "🌁": 248694,
+  "🌂": 248584,
+  "🌃": 248739,
+  "🌇": 248528,
+  "🌉": 248964,
+  "🌌": 248587,
+  "🌑": 249069,
+  "🌒": 249187,
+  "🌓": 248598,
+  "🌔": 248514,
+  "🌕": 248452,
+  "🌖": 249242,
+  "🌗": 248478,
+  "🌘": 248782,
+  "🌚": 249190,
+  "🌛": 248984,
+  "🌜": 248635,
+  "🌠": 248623,
+  "🌡": 248904,
+  "🌤": 248812,
+  "🌥": 248881,
+  "🌦": 249172,
+  "🌧": 248666,
+  "🌨": 248730,
+  "🌩": 248795,
+  "🌪": 249022,
+  "🌫": 248734,
+  "🌬": 248530,
+  "🌭": 248868,
+  "🌮": 248976,
+  "🌯": 248716,
+  "🌰": 248872,
+  "🌵": 249081,
+  "🍈": 249173,
+  "🍐": 248933,
+  "🍖": 248742,
+  "🍗": 248433,
+  "🍘": 248647,
+  "🍙": 249057,
+  "🍛": 248869,
+  "🍜": 248979,
+  "🍝": 249087,
+  "🍞": 248697,
+  "🍟": 248748,
+  "🍠": 248735,
+  "🍡": 248591,
+  "🍢": 248833,
+  "🍣": 248744,
+  "🍤": 248777,
+  "🍥": 248722,
+  "🍧": 248692,
+  "🍩": 248935,
+  "🍭": 248923,
+  "🍮": 249067,
+  "🍱": 248715,
+  "🍵": 248873,
+  "🍶": 248814,
+  "🍸": 249251,
+  "🍼": 248650,
+  "🎃": 248736,
+  "🎆": 248678,
+  "🎇": 248589,
+  "🎌": 248805,
+  "🎍": 248892,
+  "🎎": 249231,
+  "🎏": 248670,
+  "🎐": 249014,
+  "🎑": 248954,
+  "🎒": 249072,
+  "🎚": 249103,
+  "🎛": 248704,
+  "🎟": 248802,
+  "🎠": 249005,
+  "🎡": 248932,
+  "🎣": 249137,
+  "🎦": 248858,
+  "🎪": 249086,
+  "🎫": 249258,
+  "🎰": 248683,
+  "🎱": 248961,
+  "🎲": 248455,
+  "🎳": 249073,
+  "🎴": 248769,
+  "🎷": 248451,
+  "🎹": 248503,
+  "🎺": 248586,
+  "🎻": 249148,
+  "🎽": 248577,
+  "🎾": 249108,
+  "🎿": 248915,
+  "🏁": 249031,
+  "🏂": 248792,
+  "🏅": 249063,
+  "🏇": 248612,
+  "🏈": 248891,
+  "🏉": 248434,
+  "🏊": 249052,
+  "🏍": 248525,
+  "🏎": 248992,
+  "🏏": 248515,
+  "🏐": 248660,
+  "🏑": 249144,
+  "🏒": 248866,
+  "🏓": 248459,
+  "🏔": 248588,
+  "🏕": 248916,
+  "🏗": 249098,
+  "🏘": 249199,
+  "🏙": 248567,
+  "🏚": 248888,
+  "🏛": 248914,
+  "🏜": 248685,
+  "🏞": 249200,
+  "🏟": 248815,
+  "🏣": 248828,
+  "🏤": 248773,
+  "🏥": 248835,
+  "🏦": 248553,
+  "🏧": 248642,
+  "🏨": 248561,
+  "🏩": 249182,
+  "🏪": 248999,
+  "🏬": 248996,
+  "🏭": 248934,
+  "🏮": 248644,
+  "🏯": 249175,
+  "🏰": 248774,
+  "🏴": 248501,
+  "🏷": 248427,
+  "🏸": 248804,
+  "🏹": 249130,
+  "🏺": 249250,
+  "🐀": 249257,
+  "🐁": 249252,
+  "🐂": 248621,
+  "🐃": 248695,
+  "🐄": 249194,
+  "🐅": 249117,
+  "🐆": 249026,
+  "🐇": 249015,
+  "🐈": 248527,
+  "🐉": 248462,
+  "🐊": 248574,
+  "🐋": 249208,
+  "🐌": 248880,
+  "🐎": 248479,
+  "🐏": 248865,
+  "🐐": 249198,
+  "🐑": 249122,
+  "🐓": 248680,
+  "🐔": 249068,
+  "🐕": 248756,
+  "🐖": 248603,
+  "🐗": 248750,
+  "🐙": 248701,
+  "🐚": 248886,
+  "🐛": 248540,
+  "🐜": 249249,
+  "🐟": 248912,
+  "🐠": 249253,
+  "🐡": 248480,
+  "🐢": 249210,
+  "🐧": 249254,
+  "🐨": 249019,
+  "🐩": 248580,
+  "🐪": 248775,
+  "🐫": 248505,
+  "🐬": 249002,
+  "🐭": 248615,
+  "🐮": 249074,
+  "🐵": 248533,
+  "🐹": 248441,
+  "🐺": 249055,
+  "🐽": 248602,
+  "🐿": 248823,
+  "👂": 249140,
+  "👓": 249170,
+  "👘": 248542,
+  "👛": 249217,
+  "👝": 248709,
+  "👞": 248728,
+  "👡": 248549,
+  "👢": 248910,
+  "👲": 249001,
+  "👴": 248820,
+  "👵": 248960,
+  "👷": 249146,
+  "👹": 249099,
+  "👺": 249106,
+  "👽": 248883,
+  "👾": 249059,
+  "💂": 248778,
+  "💈": 248554,
+  "💒": 248594,
+  "💤": 248466,
+  "💨": 248913,
+  "💩": 248508,
+  "💱": 249100,
+  "💴": 248931,
+  "💷": 248711,
+  "💹": 249158,
+  "💺": 248831,
+  "💽": 249021,
+  "💾": 249215,
+  "💿": 248604,
+  "📀": 248518,
+  "📁": 248444,
+  "📂": 248496,
+  "📄": 248801,
+  "📆": 248487,
+  "📇": 249007,
+  "📈": 248438,
+  "📉": 248617,
+  "📊": 248473,
+  "📏": 248556,
+  "📐": 248679,
+  "📑": 249256,
+  "📒": 248710,
+  "📓": 248632,
+  "📔": 248909,
+  "📗": 248807,
+  "📘": 248708,
+  "📙": 249160,
+  "📛": 248484,
+  "📟": 248727,
+  "📠": 249065,
+  "📡": 248532,
+  "📤": 248718,
+  "📨": 249196,
+  "📪": 248565,
+  "📫": 248469,
+  "📭": 248998,
+  "📯": 249220,
+  "📳": 248471,
+  "📴": 249004,
+  "📵": 249013,
+  "📶": 248969,
+  "📻": 249084,
+  "📼": 249230,
+  "📿": 249181,
+  "🔀": 248946,
+  "🔂": 248863,
+  "🔃": 249050,
+  "🔄": 248995,
+  "🔆": 249046,
+  "🔇": 249009,
+  "🔈": 248559,
+  "🔉": 248590,
+  "🔋": 248907,
+  "🔌": 248842,
+  "🔏": 248622,
+  "🔐": 248546,
+  "🔑": 248937,
+  "🔒": 248498,
+  "🔓": 249058,
+  "🔕": 248780,
+  "🔙": 249206,
+  "🔚": 248903,
+  "🔛": 248671,
+  "🔟": 249189,
+  "🔠": 249075,
+  "🔡": 248600,
+  "🔢": 249142,
+  "🔣": 248579,
+  "🔤": 248997,
+  "🔦": 248813,
+  "🔧": 248618,
+  "🔨": 248849,
+  "🔩": 248781,
+  "🔪": 248906,
+  "🔭": 249261,
+  "🔯": 248674,
+  "🔲": 248988,
+  "🔳": 248985,
+  "🔼": 248754,
+  "🔽": 248581,
+  "🕋": 249027,
+  "🕌": 249241,
+  "🕍": 248719,
+  "🕎": 248626,
+  "🕐": 248855,
+  "🕑": 248818,
+  "🕒": 248485,
+  "🕓": 248465,
+  "🕕": 248494,
+  "🕖": 248847,
+  "🕗": 249221,
+  "🕘": 248552,
+  "🕙": 248539,
+  "🕚": 248607,
+  "🕛": 248889,
+  "🕜": 248513,
+  "🕝": 248763,
+  "🕞": 248601,
+  "🕟": 248884,
+  "🕠": 249109,
+  "🕡": 248982,
+  "🕢": 248896,
+  "🕣": 248854,
+  "🕤": 248682,
+  "🕥": 248627,
+  "🕦": 248938,
+  "🕧": 248482,
+  "🕰": 248810,
+  "🕳": 248700,
+  "🕴": 248483,
+  "🕶": 248688,
+  "🕷": 248437,
+  "🕸": 248749,
+  "🕹": 248871,
+  "🖇": 248563,
+  "🖊": 248760,
+  "🖌": 249209,
+  "🖍": 248560,
+  "🖕": 249105,
+  "🖖": 248864,
+  "🖨": 248947,
+  "🖱": 248784,
+  "🖲": 248766,
+  "🖼": 248506,
+  "🗂": 248631,
+  "🗃": 248497,
+  "🗄": 248980,
+  "🗑": 249088,
+  "🗒": 249048,
+  "🗜": 248764,
+  "🗝": 248629,
+  "🗞": 248867,
+  "🗡": 249134,
+  "🗨": 249150,
+  "🗯": 248585,
+  "🗳": 248461,
+  "🗺": 248493,
+  "🗻": 249133,
+  "🗾": 248464,
+  "🗿": 249135,
+  "😧": 249227,
+  "😸": 248908,
+  "😺": 248851,
+  "😼": 248614,
+  "😽": 249121,
+  "😾": 248675,
+  "😿": 248796,
+  "🙉": 248633,
+  "🙍": 249066,
+  "🙎": 248808,
+  "🚁": 248677,
+  "🚂": 248504,
+  "🚃": 249184,
+  "🚄": 248648,
+  "🚅": 249102,
+  "🚆": 248974,
+  "🚈": 248901,
+  "🚉": 248856,
+  "🚊": 249178,
+  "🚋": 248878,
+  "🚍": 249016,
+  "🚎": 249163,
+  "🚏": 249119,
+  "🚐": 248731,
+  "🚑": 249188,
+  "🚒": 249225,
+  "🚓": 248817,
+  "🚔": 249259,
+  "🚕": 248608,
+  "🚖": 248667,
+  "🚜": 249076,
+  "🚝": 248882,
+  "🚞": 248596,
+  "🚟": 249154,
+  "🚠": 248640,
+  "🚡": 249020,
+  "🚢": 248547,
+  "🚣": 249186,
+  "🚤": 249204,
+  "🚥": 249164,
+  "🚦": 248717,
+  "🚧": 249143,
+  "🚪": 248861,
+  "🚬": 248918,
+  "🚭": 248836,
+  "🚮": 248583,
+  "🚯": 248659,
+  "🚰": 249202,
+  "🚱": 248786,
+  "🚳": 248758,
+  "🚷": 249149,
+  "🚸": 248550,
+  "🚹": 249024,
+  "🚺": 249044,
+  "🚻": 248636,
+  "🚼": 249214,
+  "🚽": 248726,
+  "🚾": 249205,
+  "🚿": 248941,
+  "🛀": 249097,
+  "🛁": 248430,
+  "🛂": 248770,
+  "🛃": 248732,
+  "🛄": 248879,
+  "🛅": 249041,
+  "🛋": 249203,
+  "🛎": 249113,
+  "🛏": 249078,
+  "🛐": 248830,
+  "🛕": 248859,
+  "🛖": 248435,
+  "🛗": 248551,
+  "🛜": 248519,
+  "🛝": 248510,
+  "🛞": 248897,
+  "🛟": 248981,
+  "🛠": 248966,
+  "🛡": 248811,
+  "🛢": 249240,
+  "🛣": 249040,
+  "🛤": 248973,
+  "🛥": 248436,
+  "🛩": 249156,
+  "🛬": 248951,
+  "🛰": 248656,
+  "🛳": 249123,
+  "🛴": 249111,
+  "🛵": 248857,
+  "🛶": 248940,
+  "🛷": 248824,
+  "🛸": 248562,
+  "🛹": 248669,
+  "🛺": 248569,
+  "🛻": 249017,
+  "🛼": 248991,
+  "🟠": 249195,
+  "🟡": 248460,
+  "🟢": 248489,
+  "🟣": 248737,
+  "🟤": 248936,
+  "🟥": 249239,
+  "🟦": 248491,
+  "🟧": 249166,
+  "🟨": 248765,
+  "🟩": 248439,
+  "🟪": 248787,
+  "🟫": 249255,
+  "🟰": 248874,
+  "🤌": 248620,
+  "🤍": 248967,
+  "🤎": 249116,
+  "🤏": 248699,
+  "🤐": 248806,
+  "🤒": 248610,
+  "🤕": 248475,
+  "🤚": 249011,
+  "🤛": 249107,
+  "🤜": 249152,
+  "🤠": 248862,
+  "🤢": 249023,
+  "🤥": 248628,
+  "🤧": 249064,
+  "🤫": 248706,
+  "🤬": 249219,
+  "🤮": 248819,
+  "🤯": 249096,
+  "🤰": 249207,
+  "🤱": 248925,
+  "🤳": 248541,
+  "🤴": 248573,
+  "🤶": 249094,
+  "🤸": 248809,
+  "🤹": 248570,
+  "🤺": 248443,
+  "🤼": 249115,
+  "🤽": 248794,
+  "🤾": 248721,
+  "🤿": 249191,
+  "🥁": 249118,
+  "🥃": 248788,
+  "🥄": 248944,
+  "🥅": 249101,
+  "🥈": 248899,
+  "🥉": 248762,
+  "🥊": 248768,
+  "🥋": 248772,
+  "🥌": 249177,
+  "🥍": 249051,
+  "🥎": 248645,
+  "🥏": 248557,
+  "🥐": 249131,
+  "🥑": 248511,
+  "🥒": 249229,
+  "🥓": 248751,
+  "🥔": 248905,
+  "🥕": 248838,
+  "🥖": 248470,
+  "🥗": 248745,
+  "🥘": 248445,
+  "🥙": 248463,
+  "🥚": 248520,
+  "🥛": 248582,
+  "🥜": 249083,
+  "🥝": 248848,
+  "🥞": 248453,
+  "🥟": 249033,
+  "🥠": 248942,
+  "🥡": 248720,
+  "🥢": 249168,
+  "🥤": 248949,
+  "🥥": 248956,
+  "🥦": 248432,
+  "🥧": 248595,
+  "🥨": 248799,
+  "🥩": 248928,
+  "🥪": 248963,
+  "🥫": 248509,
+  "🥬": 249237,
+  "🥭": 248634,
+  "🥮": 248446,
+  "🥯": 248757,
+  "🥱": 249244,
+  "🥲": 248429,
+  "🥳": 249128,
+  "🥴": 248860,
+  "🥵": 249029,
+  "🥶": 249147,
+  "🥷": 248702,
+  "🥸": 248486,
+  "🥹": 248431,
+  "🥻": 249039,
+  "🥼": 248832,
+  "🥽": 248875,
+  "🥾": 249056,
+  "🥿": 249138,
+  "🦀": 249201,
+  "🦂": 249034,
+  "🦃": 248834,
+  "🦆": 249042,
+  "🦇": 248543,
+  "🦈": 249139,
+  "🦉": 249008,
+  "🦌": 248625,
+  "🦍": 248713,
+  "🦎": 248911,
+  "🦏": 248555,
+  "🦐": 248950,
+  "🦑": 248746,
+  "🦒": 248843,
+  "🦓": 248789,
+  "🦔": 248641,
+  "🦕": 248654,
+  "🦖": 248686,
+  "🦗": 249235,
+  "🦘": 248825,
+  "🦙": 248523,
+  "🦚": 248797,
+  "🦛": 249080,
+  "🦜": 248816,
+  "🦝": 248661,
+  "🦞": 249045,
+  "🦟": 248753,
+  "🦠": 249085,
+  "🦡": 249246,
+  "🦢": 248965,
+  "🦣": 248578,
+  "🦤": 248662,
+  "🦥": 248939,
+  "🦦": 249161,
+  "🦧": 249030,
+  "🦨": 248776,
+  "🦩": 248624,
+  "🦪": 248738,
+  "🦫": 248566,
+  "🦬": 249245,
+  "🦭": 249159,
+  "🦮": 248517,
+  "🦯": 249089,
+  "🦰": 249132,
+  "🦱": 248613,
+  "🦲": 249047,
+  "🦳": 248927,
+  "🦴": 248691,
+  "🦵": 248885,
+  "🦶": 248876,
+  "🦷": 248771,
+  "🦸": 248538,
+  "🦹": 248958,
+  "🦺": 248948,
+  "🦻": 248729,
+  "🦼": 248639,
+  "🦽": 248959,
+  "🦾": 248507,
+  "🦿": 249222,
+  "🧀": 248712,
+  "🧁": 248840,
+  "🧂": 249018,
+  "🧃": 249125,
+  "🧄": 248723,
+  "🧅": 248895,
+  "🧆": 248571,
+  "🧇": 248767,
+  "🧈": 249211,
+  "🧉": 248684,
+  "🧊": 249092,
+  "🧋": 248696,
+  "🧌": 249061,
+  "🧍": 249218,
+  "🧎": 248537,
+  "🧏": 248544,
+  "🧐": 249095,
+  "🧑": 248672,
+  "🧒": 248536,
+  "🧓": 249145,
+  "🧔": 249110,
+  "🧕": 249176,
+  "🧖": 249025,
+  "🧗": 248853,
+  "🧘": 248663,
+  "🧙": 248605,
+  "🧛": 248887,
+  "🧝": 248524,
+  "🧞": 248558,
+  "🧟": 249197,
+  "🧠": 248893,
+  "🧢": 249035,
+  "🧣": 249155,
+  "🧤": 248740,
+  "🧥": 248657,
+  "🧦": 248900,
+  "🧧": 248428,
+  "🧨": 248793,
+  "🧩": 249234,
+  "🧪": 249192,
+  "🧫": 248975,
+  "🧬": 248681,
+  "🧭": 249180,
+  "🧮": 249049,
+  "🧯": 249167,
+  "🧰": 248593,
+  "🧱": 249112,
+  "🧲": 249082,
+  "🧳": 249179,
+  "🧴": 249104,
+  "🧵": 249169,
+  "🧶": 248651,
+  "🧷": 248977,
+  "🧸": 248488,
+  "🧹": 249233,
+  "🧺": 248921,
+  "🧻": 249236,
+  "🧼": 248993,
+  "🧽": 248545,
+  "🧾": 248827,
+  "🧿": 249114,
+  "🩰": 248852,
+  "🩱": 248529,
+  "🩲": 249171,
+  "🩳": 248970,
+  "🩴": 248705,
+  "🩵": 248989,
+  "🩶": 249243,
+  "🩷": 248945,
+  "🩸": 248839,
+  "🩹": 248477,
+  "🩺": 248983,
+  "🩻": 248474,
+  "🩼": 249127,
+  "🪀": 249216,
+  "🪁": 249183,
+  "🪂": 248448,
+  "🪃": 248689,
+  "🪄": 248653,
+  "🪅": 248440,
+  "🪆": 248616,
+  "🪇": 249238,
+  "🪈": 249038,
+  "🪐": 249032,
+  "🪑": 249185,
+  "🪒": 248467,
+  "🪓": 249028,
+  "🪔": 248919,
+  "🪕": 248759,
+  "🪖": 248447,
+  "🪗": 249006,
+  "🪘": 248643,
+  "🪙": 248476,
+  "🪚": 249165,
+  "🪛": 249077,
+  "🪜": 248597,
+  "🪝": 248714,
+  "🪞": 248687,
+  "🪟": 249226,
+  "🪠": 248499,
+  "🪡": 248841,
+  "🪢": 249124,
+  "🪣": 248955,
+  "🪤": 248668,
+  "🪥": 248599,
+  "🪦": 248902,
+  "🪧": 248535,
+  "🪨": 248673,
+  "🪩": 248962,
+  "🪪": 249223,
+  "🪫": 248990,
+  "🪬": 248564,
+  "🪭": 248926,
+  "🪮": 248630,
+  "🪯": 248822,
+  "🪰": 248829,
+  "🪱": 249162,
+  "🪲": 248522,
+  "🪳": 249037,
+  "🪴": 248457,
+  "🪵": 248877,
+  "🪶": 249151,
+  "🪷": 249036,
+  "🪸": 248492,
+  "🪹": 248592,
+  "🪺": 248646,
+  "🪻": 248898,
+  "🪼": 248952,
+  "🪽": 249136,
+  "🪿": 249071,
+  "🫀": 248442,
+  "🫁": 248449,
+  "🫂": 248606,
+  "🫃": 248637,
+  "🫄": 248658,
+  "🫅": 248846,
+  "🫎": 248894,
+  "🫏": 248826,
+  "🫐": 248500,
+  "🫑": 248456,
+  "🫒": 248703,
+  "🫓": 248743,
+  "🫔": 249126,
+  "🫕": 249174,
+  "🫖": 248724,
+  "🫗": 248652,
+  "🫘": 249093,
+  "🫙": 249054,
+  "🫚": 248800,
+  "🫛": 248698,
+  "🫠": 249247,
+  "🫡": 248968,
+  "🫢": 248783,
+  "🫣": 248481,
+  "🫤": 248986,
+  "🫥": 249043,
+  "🫦": 248526,
+  "🫧": 248548,
+  "🫨": 248747,
+  "🫰": 249012,
+  "🫱": 249090,
+  "🫲": 248924,
+  "🫳": 249228,
+  "🫴": 248987,
+  "🫵": 248531,
+  "🫶": 249157,
+  "🫷": 249091,
+  "🫸": 248870
+}

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "/root/.cache/huggingface/hub/models--kornwtp--ConGen-BGE_M3-model-phayathaibert/snapshots/ace614ebc26a7232b5512651a25ede1aeec2562e/0_Transformer",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "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-12,
+  "max_position_embeddings": 512,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 249262
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.45.1",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96b034a9858f8750ba15f762380ac15a6bb26ef442508713f4f0deeb924637d1
+size 1109917664

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e295c936bc0d8b6669ae769a2f8a0363e6d3abcfd8d0869134aa1e903a447d26
+size 5261686

special_tokens_map.json

@@ -0,0 +1,56 @@
+{
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED",
+    "<_>"
+  ],
+  "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": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e38181341a7e882ace07a5087bcd38cbe826b035866264ebbd6c81e3442e0fe1
+size 17349790