|
--- |
|
base_model: intfloat/multilingual-e5-small |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy |
|
- cosine_accuracy_threshold |
|
- cosine_f1 |
|
- cosine_f1_threshold |
|
- cosine_precision |
|
- cosine_recall |
|
- cosine_ap |
|
- dot_accuracy |
|
- dot_accuracy_threshold |
|
- dot_f1 |
|
- dot_f1_threshold |
|
- dot_precision |
|
- dot_recall |
|
- dot_ap |
|
- manhattan_accuracy |
|
- manhattan_accuracy_threshold |
|
- manhattan_f1 |
|
- manhattan_f1_threshold |
|
- manhattan_precision |
|
- manhattan_recall |
|
- manhattan_ap |
|
- euclidean_accuracy |
|
- euclidean_accuracy_threshold |
|
- euclidean_f1 |
|
- euclidean_f1_threshold |
|
- euclidean_precision |
|
- euclidean_recall |
|
- euclidean_ap |
|
- max_accuracy |
|
- max_accuracy_threshold |
|
- max_f1 |
|
- max_f1_threshold |
|
- max_precision |
|
- max_recall |
|
- max_ap |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:2476 |
|
- loss:OnlineContrastiveLoss |
|
widget: |
|
- source_sentence: Why do you want to be to president? |
|
sentences: |
|
- Can you teach me how to cook? |
|
- Recipe for baking cookies |
|
- Would you want to be President? |
|
- source_sentence: What is the speed of sound in air? |
|
sentences: |
|
- Velocity of sound waves in the atmosphere |
|
- What is the most delicious dish you've ever eaten and why? |
|
- The `safe` parameter in the `to_spreadsheet` method determines if a secure conversion |
|
is necessary for certain plant attributes to be stored in a SpreadsheetTable or |
|
Row. |
|
- source_sentence: How many countries are in the European Union? |
|
sentences: |
|
- Number of countries in the European Union |
|
- Artist who painted the Sistine Chapel |
|
- The RecipeManager class is employed to oversee the downloading and unpacking of |
|
recipes. |
|
- source_sentence: What is the currency of the United States? |
|
sentences: |
|
- What's the purpose of life? What is life actually about? |
|
- Iter_zip() is employed to sequentially access and yield files inside ZIP archives. |
|
- Official currency of the USA |
|
- source_sentence: Who wrote the book "To Kill a Mockingbird"? |
|
sentences: |
|
- At what speed does light travel? |
|
- How to set up a yoga studio? |
|
- Who wrote the book "1984"? |
|
model-index: |
|
- name: SentenceTransformer based on intfloat/multilingual-e5-small |
|
results: |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: pair class dev |
|
type: pair-class-dev |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.8768115942028986 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8267427086830139 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.8969696969696969 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8267427086830139 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.8809523809523809 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9135802469135802 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9300650297384708 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.8768115942028986 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8267427682876587 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.8969696969696969 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8267427682876587 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.8809523809523809 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9135802469135802 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9300650297384708 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.8731884057971014 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.953017234802246 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.8929663608562691 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 9.028047561645508 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.8848484848484849 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9012345679012346 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9284992066218356 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.8768115942028986 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.5886479616165161 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.8969696969696969 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.5886479616165161 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.8809523809523809 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9135802469135802 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9300650297384708 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.8768115942028986 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.953017234802246 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.8969696969696969 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 9.028047561645508 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.8848484848484849 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9135802469135802 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9300650297384708 |
|
name: Max Ap |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: pair class test |
|
type: pair-class-test |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.8768115942028986 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8267427086830139 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.8969696969696969 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8267427086830139 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.8809523809523809 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9135802469135802 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9300650297384708 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.8768115942028986 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8267427682876587 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.8969696969696969 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8267427682876587 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.8809523809523809 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9135802469135802 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9300650297384708 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.8731884057971014 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.953017234802246 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.8929663608562691 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 9.028047561645508 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.8848484848484849 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9012345679012346 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9284992066218356 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.8768115942028986 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.5886479616165161 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.8969696969696969 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.5886479616165161 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.8809523809523809 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9135802469135802 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9300650297384708 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.8768115942028986 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.953017234802246 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.8969696969696969 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 9.028047561645508 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.8848484848484849 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9135802469135802 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9300650297384708 |
|
name: Max Ap |
|
--- |
|
|
|
# SentenceTransformer based on intfloat/multilingual-e5-small |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 384 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': 384, '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): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("srikarvar/fine_tuned_model_15") |
|
# Run inference |
|
sentences = [ |
|
'Who wrote the book "To Kill a Mockingbird"?', |
|
'Who wrote the book "1984"?', |
|
'At what speed does light travel?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 384] |
|
|
|
# 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 |
|
|
|
#### Binary Classification |
|
* Dataset: `pair-class-dev` |
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
| Metric | Value | |
|
|:-----------------------------|:-----------| |
|
| cosine_accuracy | 0.8768 | |
|
| cosine_accuracy_threshold | 0.8267 | |
|
| cosine_f1 | 0.897 | |
|
| cosine_f1_threshold | 0.8267 | |
|
| cosine_precision | 0.881 | |
|
| cosine_recall | 0.9136 | |
|
| cosine_ap | 0.9301 | |
|
| dot_accuracy | 0.8768 | |
|
| dot_accuracy_threshold | 0.8267 | |
|
| dot_f1 | 0.897 | |
|
| dot_f1_threshold | 0.8267 | |
|
| dot_precision | 0.881 | |
|
| dot_recall | 0.9136 | |
|
| dot_ap | 0.9301 | |
|
| manhattan_accuracy | 0.8732 | |
|
| manhattan_accuracy_threshold | 8.953 | |
|
| manhattan_f1 | 0.893 | |
|
| manhattan_f1_threshold | 9.028 | |
|
| manhattan_precision | 0.8848 | |
|
| manhattan_recall | 0.9012 | |
|
| manhattan_ap | 0.9285 | |
|
| euclidean_accuracy | 0.8768 | |
|
| euclidean_accuracy_threshold | 0.5886 | |
|
| euclidean_f1 | 0.897 | |
|
| euclidean_f1_threshold | 0.5886 | |
|
| euclidean_precision | 0.881 | |
|
| euclidean_recall | 0.9136 | |
|
| euclidean_ap | 0.9301 | |
|
| max_accuracy | 0.8768 | |
|
| max_accuracy_threshold | 8.953 | |
|
| max_f1 | 0.897 | |
|
| max_f1_threshold | 9.028 | |
|
| max_precision | 0.8848 | |
|
| max_recall | 0.9136 | |
|
| **max_ap** | **0.9301** | |
|
|
|
#### Binary Classification |
|
* Dataset: `pair-class-test` |
|
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
|
| Metric | Value | |
|
|:-----------------------------|:-----------| |
|
| cosine_accuracy | 0.8768 | |
|
| cosine_accuracy_threshold | 0.8267 | |
|
| cosine_f1 | 0.897 | |
|
| cosine_f1_threshold | 0.8267 | |
|
| cosine_precision | 0.881 | |
|
| cosine_recall | 0.9136 | |
|
| cosine_ap | 0.9301 | |
|
| dot_accuracy | 0.8768 | |
|
| dot_accuracy_threshold | 0.8267 | |
|
| dot_f1 | 0.897 | |
|
| dot_f1_threshold | 0.8267 | |
|
| dot_precision | 0.881 | |
|
| dot_recall | 0.9136 | |
|
| dot_ap | 0.9301 | |
|
| manhattan_accuracy | 0.8732 | |
|
| manhattan_accuracy_threshold | 8.953 | |
|
| manhattan_f1 | 0.893 | |
|
| manhattan_f1_threshold | 9.028 | |
|
| manhattan_precision | 0.8848 | |
|
| manhattan_recall | 0.9012 | |
|
| manhattan_ap | 0.9285 | |
|
| euclidean_accuracy | 0.8768 | |
|
| euclidean_accuracy_threshold | 0.5886 | |
|
| euclidean_f1 | 0.897 | |
|
| euclidean_f1_threshold | 0.5886 | |
|
| euclidean_precision | 0.881 | |
|
| euclidean_recall | 0.9136 | |
|
| euclidean_ap | 0.9301 | |
|
| max_accuracy | 0.8768 | |
|
| max_accuracy_threshold | 8.953 | |
|
| max_f1 | 0.897 | |
|
| max_f1_threshold | 9.028 | |
|
| max_precision | 0.8848 | |
|
| max_recall | 0.9136 | |
|
| **max_ap** | **0.9301** | |
|
|
|
<!-- |
|
## 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: 2,476 training samples |
|
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | label | sentence1 | sentence2 | |
|
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | int | string | string | |
|
| details | <ul><li>0: ~40.20%</li><li>1: ~59.80%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.35 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 98 tokens</li></ul> | |
|
* Samples: |
|
| label | sentence1 | sentence2 | |
|
|:---------------|:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
|
| <code>1</code> | <code>The ImageNet dataset is used for training models to classify images into various categories.</code> | <code>A model is trained using the ImageNet dataset to classify images into distinct categories.</code> | |
|
| <code>1</code> | <code>No, it doesn't exist in version 5.3.1.</code> | <code>Version 5.3.1 does not contain it.</code> | |
|
| <code>0</code> | <code>Can you help me with my homework?</code> | <code>Can you do my homework for me?</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 276 evaluation samples |
|
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code> |
|
* Approximate statistics based on the first 276 samples: |
|
| | label | sentence1 | sentence2 | |
|
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | int | string | string | |
|
| details | <ul><li>0: ~41.30%</li><li>1: ~58.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.34 tokens</li><li>max: 86 tokens</li></ul> | |
|
* Samples: |
|
| label | sentence1 | sentence2 | |
|
|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------| |
|
| <code>0</code> | <code>What are the challenges of AI in cybersecurity?</code> | <code>How is AI used to enhance cybersecurity?</code> | |
|
| <code>1</code> | <code>You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.</code> | <code>The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.</code> | |
|
| <code>1</code> | <code>What is the capital of Italy?</code> | <code>Name the capital city of Italy</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `gradient_accumulation_steps`: 2 |
|
- `num_train_epochs`: 4 |
|
- `warmup_ratio`: 0.1 |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### 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`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 2 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 5e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: linear |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
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- `log_level`: passive |
|
- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
|
- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
|
- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `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_fused |
|
- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap | |
|
|:-------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:| |
|
| 0 | 0 | - | - | 0.7876 | - | |
|
| 0.2564 | 10 | 1.5794 | - | - | - | |
|
| 0.5128 | 20 | 0.8392 | - | - | - | |
|
| 0.7692 | 30 | 0.7812 | - | - | - | |
|
| 1.0 | 39 | - | 0.8081 | 0.9138 | - | |
|
| 1.0256 | 40 | 0.6505 | - | - | - | |
|
| 1.2821 | 50 | 0.57 | - | - | - | |
|
| 1.5385 | 60 | 0.3015 | - | - | - | |
|
| 1.7949 | 70 | 0.3091 | - | - | - | |
|
| 2.0 | 78 | - | 0.7483 | 0.9267 | - | |
|
| 2.0513 | 80 | 0.3988 | - | - | - | |
|
| 2.3077 | 90 | 0.1801 | - | - | - | |
|
| 2.5641 | 100 | 0.1166 | - | - | - | |
|
| 2.8205 | 110 | 0.1255 | - | - | - | |
|
| 3.0 | 117 | - | 0.7106 | 0.9284 | - | |
|
| 3.0769 | 120 | 0.2034 | - | - | - | |
|
| 3.3333 | 130 | 0.0329 | - | - | - | |
|
| 3.5897 | 140 | 0.0805 | - | - | - | |
|
| 3.8462 | 150 | 0.0816 | - | - | - | |
|
| **4.0** | **156** | **-** | **0.6969** | **0.9301** | **0.9301** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.34.2 |
|
- 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", |
|
} |
|
``` |
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