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---
base_model: huudan123/model_stage2_latest
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:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: trắng và nâu đang chạy nhanh qua đám cỏ.
sentences:
- Một chiếc máy bay trên bầu trời.
- trắng lớn đang chạy trên cỏ.
- Hai con đại bàng đang đậu trên cành cây.
- source_sentence: Chúng tôi đang di chuyển \"... liên quan đến khung nghỉ vũ trụ
comoving ... với tốc độ khoảng 371 km/s về phía chòm sao Sư Tử\".
sentences:
- Một bức ảnh đen trắng của một người đàn ông đứng cạnh xe buýt.
- Một vận động viên quần vợt ở giữa trận đấu.
- Không có 'tĩnh' không liên quan đến một số đối tượng khác.
- source_sentence: Một người đàn ông đang trượt băng xuống cầu thang.
sentences:
- Tôi đồng ý với những người khác rằng theo dõi thời gian của bạn là cơ bản cho
giải pháp.
- Người đàn ông đang trượt tuyết xuống một ngọn đồi tuyết.
- Một đứa bé đang cười.
- source_sentence: Theo trang web này, cường độ khả kiến cực đại sẽ vào khoảng 10,5
vào khoảng ngày 2/2.
sentences:
- Trẻ em nhìn một con cừu.
- Dữ liệu AAVSO dường như chỉ ra rằng nó có thể đã đạt đỉnh, vào khoảng 10,5 (trực
quan).
- Chim đen đứng trên bê tông.
- source_sentence: Tôi có thể nghĩ ra ba yếu tố chính là những phỏng đoán khá logic.
sentences:
- Những ở một mình trong rừng.
- Cô gái đang đứng trước cánh cửa mở của xe buýt.
- Đã có khá nhiều nghiên cứu trong bóng đá / bóng đá thảo luận về lợi thế sân nhà.
model-index:
- name: SentenceTransformer based on huudan123/model_stage2_latest
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts evaluator
type: sts-evaluator
metrics:
- type: pearson_cosine
value: 0.8454565422917285
name: Pearson Cosine
- type: spearman_cosine
value: 0.845527756857174
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8361734084244434
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8435783241552874
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8359678844722435
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8434666682443507
name: Spearman Euclidean
- type: pearson_dot
value: 0.8301976528382738
name: Pearson Dot
- type: spearman_dot
value: 0.8288697839085633
name: Spearman Dot
- type: pearson_max
value: 0.8454565422917285
name: Pearson Max
- type: spearman_max
value: 0.845527756857174
name: Spearman Max
---
# SentenceTransformer based on huudan123/model_stage2_latest
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/model_stage2_latest](https://huggingface.co/huudan123/model_stage2_latest). 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/model_stage2_latest](https://huggingface.co/huudan123/model_stage2_latest) <!-- at revision 8b6f753a27cb476cb187731b7939aff4a5baad7c -->
- **Maximum Sequence Length:** 256 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': 256, '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/model_stage3_latest")
# Run inference
sentences = [
'Tôi có thể nghĩ ra ba yếu tố chính là những phỏng đoán khá logic.',
'Đã có khá nhiều nghiên cứu trong bóng đá / bóng đá thảo luận về lợi thế sân nhà.',
'Cô gái đang đứng trước cánh cửa mở của xe buýt.',
]
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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-evaluator`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.8455 |
| spearman_cosine | 0.8455 |
| pearson_manhattan | 0.8362 |
| spearman_manhattan | 0.8436 |
| pearson_euclidean | 0.836 |
| spearman_euclidean | 0.8435 |
| pearson_dot | 0.8302 |
| spearman_dot | 0.8289 |
| pearson_max | 0.8455 |
| **spearman_max** | **0.8455** |
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `overwrite_output_dir`: True
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
- `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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 15
- `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`: 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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-evaluator_spearman_max |
|:-------:|:-------:|:-------------:|:----------:|:--------------------------:|
| 0 | 0 | - | - | 0.6849 |
| 0.5556 | 25 | 0.0801 | - | - |
| 1.0 | 45 | - | 0.0390 | 0.7990 |
| 1.1111 | 50 | 0.0388 | - | - |
| 1.6667 | 75 | 0.0309 | - | - |
| 2.0 | 90 | - | 0.0315 | 0.8401 |
| 2.2222 | 100 | 0.0264 | - | - |
| 2.7778 | 125 | 0.0222 | - | - |
| 3.0 | 135 | - | 0.0302 | 0.8412 |
| 3.3333 | 150 | 0.0188 | - | - |
| 3.8889 | 175 | 0.0164 | - | - |
| 4.0 | 180 | - | 0.0300 | 0.8411 |
| 4.4444 | 200 | 0.0138 | - | - |
| 5.0 | 225 | 0.0135 | 0.0291 | 0.8446 |
| 5.5556 | 250 | 0.011 | - | - |
| 6.0 | 270 | - | 0.0291 | 0.8458 |
| 6.1111 | 275 | 0.0104 | - | - |
| 6.6667 | 300 | 0.0093 | - | - |
| 7.0 | 315 | - | 0.0280 | 0.8479 |
| 7.2222 | 325 | 0.0088 | - | - |
| 7.7778 | 350 | 0.0081 | - | - |
| **8.0** | **360** | **-** | **0.0285** | **0.848** |
| 8.3333 | 375 | 0.0075 | - | - |
| 8.8889 | 400 | 0.0071 | - | - |
| 9.0 | 405 | - | 0.0285 | 0.8463 |
| 9.4444 | 425 | 0.0066 | - | - |
| 10.0 | 450 | 0.0066 | 0.0287 | 0.8455 |
| 10.5556 | 475 | 0.0062 | - | - |
| 11.0 | 495 | - | 0.0285 | 0.8458 |
| 11.1111 | 500 | 0.0058 | - | - |
| 11.6667 | 525 | 0.0056 | - | - |
| 12.0 | 540 | - | 0.0291 | 0.8452 |
| 12.2222 | 550 | 0.0055 | - | - |
| 12.7778 | 575 | 0.0053 | - | - |
| 13.0 | 585 | - | 0.0289 | 0.8455 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
```
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