|
--- |
|
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:2305 |
|
- loss:OnlineContrastiveLoss |
|
widget: |
|
- source_sentence: Steps to start a vegetable garden |
|
sentences: |
|
- How to plant a vegetable garden? |
|
- If there will be a war between India and Pakistan who will win? |
|
- What is the most visited tourist attraction in the world? |
|
- source_sentence: What's the best way to jump rope? |
|
sentences: |
|
- If I jump rope for five minutes, how many calories will I use? |
|
- You can collaborate on models and datasets using Machine Learning platforms by |
|
joining the community and accessing enhanced resources. |
|
- How can I improve my public speaking skills? |
|
- source_sentence: How can remote team management be improved? |
|
sentences: |
|
- What are the key challenges of managing remote teams? |
|
- The library supports various audio formats such as WAV, MP3, and FLAC. |
|
- The `validate_data` method is used to perform checks on the data set for correctness. |
|
- source_sentence: Latest advancements in quantum computing |
|
sentences: |
|
- How to cook a turkey? |
|
- Latest advancements in AI |
|
- How to create a resume? |
|
- source_sentence: Practical guides are available to assist you in achieving specific |
|
goals and addressing real-world challenges with the framework. |
|
sentences: |
|
- How to bake cookies? |
|
- Yes, there are practical guides to help you achieve specific objectives and solve |
|
real-world problems with the framework. |
|
- How to create an email signature? |
|
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: 0.9182879377431906 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8421422243118286 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.920754716981132 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8421422243118286 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.9037037037037037 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9384615384615385 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9452952670187734 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9182879377431906 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8421421647071838 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.920754716981132 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8421421647071838 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.9037037037037037 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9384615384615385 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9452952670187734 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9182879377431906 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.50709342956543 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.9195402298850576 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 8.64261245727539 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.916030534351145 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9230769230769231 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9453829621939649 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9182879377431906 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.5618841648101807 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.920754716981132 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.5618841648101807 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.9037037037037037 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9384615384615385 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9452952670187734 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9182879377431906 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.50709342956543 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.920754716981132 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 8.64261245727539 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.916030534351145 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9384615384615385 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9453829621939649 |
|
name: Max Ap |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: pair class test |
|
type: pair-class-test |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.9182879377431906 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8421422243118286 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.920754716981132 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8421422243118286 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.9037037037037037 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9384615384615385 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9452952670187734 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9182879377431906 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8421421647071838 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.920754716981132 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8421421647071838 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.9037037037037037 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9384615384615385 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9452952670187734 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9182879377431906 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.50709342956543 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.9195402298850576 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 8.64261245727539 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.916030534351145 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9230769230769231 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9453829621939649 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9182879377431906 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.5618841648101807 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.920754716981132 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.5618841648101807 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.9037037037037037 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9384615384615385 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9452952670187734 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9182879377431906 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.50709342956543 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.920754716981132 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 8.64261245727539 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.916030534351145 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9384615384615385 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9453829621939649 |
|
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_8") |
|
# Run inference |
|
sentences = [ |
|
'Practical guides are available to assist you in achieving specific goals and addressing real-world challenges with the framework.', |
|
'Yes, there are practical guides to help you achieve specific objectives and solve real-world problems with the framework.', |
|
'How to bake cookies?', |
|
] |
|
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 | 0.9183 | |
|
| cosine_accuracy_threshold | 0.8421 | |
|
| cosine_f1 | 0.9208 | |
|
| cosine_f1_threshold | 0.8421 | |
|
| cosine_precision | 0.9037 | |
|
| cosine_recall | 0.9385 | |
|
| cosine_ap | 0.9453 | |
|
| dot_accuracy | 0.9183 | |
|
| dot_accuracy_threshold | 0.8421 | |
|
| dot_f1 | 0.9208 | |
|
| dot_f1_threshold | 0.8421 | |
|
| dot_precision | 0.9037 | |
|
| dot_recall | 0.9385 | |
|
| dot_ap | 0.9453 | |
|
| manhattan_accuracy | 0.9183 | |
|
| manhattan_accuracy_threshold | 8.5071 | |
|
| manhattan_f1 | 0.9195 | |
|
| manhattan_f1_threshold | 8.6426 | |
|
| manhattan_precision | 0.916 | |
|
| manhattan_recall | 0.9231 | |
|
| manhattan_ap | 0.9454 | |
|
| euclidean_accuracy | 0.9183 | |
|
| euclidean_accuracy_threshold | 0.5619 | |
|
| euclidean_f1 | 0.9208 | |
|
| euclidean_f1_threshold | 0.5619 | |
|
| euclidean_precision | 0.9037 | |
|
| euclidean_recall | 0.9385 | |
|
| euclidean_ap | 0.9453 | |
|
| max_accuracy | 0.9183 | |
|
| max_accuracy_threshold | 8.5071 | |
|
| max_f1 | 0.9208 | |
|
| max_f1_threshold | 8.6426 | |
|
| max_precision | 0.916 | |
|
| max_recall | 0.9385 | |
|
| **max_ap** | **0.9454** | |
|
|
|
#### 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 | 0.9183 | |
|
| cosine_accuracy_threshold | 0.8421 | |
|
| cosine_f1 | 0.9208 | |
|
| cosine_f1_threshold | 0.8421 | |
|
| cosine_precision | 0.9037 | |
|
| cosine_recall | 0.9385 | |
|
| cosine_ap | 0.9453 | |
|
| dot_accuracy | 0.9183 | |
|
| dot_accuracy_threshold | 0.8421 | |
|
| dot_f1 | 0.9208 | |
|
| dot_f1_threshold | 0.8421 | |
|
| dot_precision | 0.9037 | |
|
| dot_recall | 0.9385 | |
|
| dot_ap | 0.9453 | |
|
| manhattan_accuracy | 0.9183 | |
|
| manhattan_accuracy_threshold | 8.5071 | |
|
| manhattan_f1 | 0.9195 | |
|
| manhattan_f1_threshold | 8.6426 | |
|
| manhattan_precision | 0.916 | |
|
| manhattan_recall | 0.9231 | |
|
| manhattan_ap | 0.9454 | |
|
| euclidean_accuracy | 0.9183 | |
|
| euclidean_accuracy_threshold | 0.5619 | |
|
| euclidean_f1 | 0.9208 | |
|
| euclidean_f1_threshold | 0.5619 | |
|
| euclidean_precision | 0.9037 | |
|
| euclidean_recall | 0.9385 | |
|
| euclidean_ap | 0.9453 | |
|
| max_accuracy | 0.9183 | |
|
| max_accuracy_threshold | 8.5071 | |
|
| max_f1 | 0.9208 | |
|
| max_f1_threshold | 8.6426 | |
|
| max_precision | 0.916 | |
|
| max_recall | 0.9385 | |
|
| **max_ap** | **0.9454** | |
|
|
|
<!-- |
|
## 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: 2,305 training samples |
|
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence2 | sentence1 | label | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 13.74 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.13 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>0: ~43.00%</li><li>1: ~57.00%</li></ul> | |
|
* Samples: |
|
| sentence2 | sentence1 | label | |
|
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------| |
|
| <code>What are the components of a computer?</code> | <code>How does a computer work?</code> | <code>0</code> | |
|
| <code>You have the option to create your own personal blog with the help of Blogging Platforms.</code> | <code>Yes, you can start your own personal blog using Blogging Platforms.</code> | <code>1</code> | |
|
| <code>It provides the layout of the data and its components.</code> | <code>It returns the structure of the data and its fields.</code> | <code>1</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 257 evaluation samples |
|
* Columns: <code>sentence2</code>, <code>sentence1</code>, and <code>label</code> |
|
* Approximate statistics based on the first 257 samples: |
|
| | sentence2 | sentence1 | label | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 14.92 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.84 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>0: ~49.42%</li><li>1: ~50.58%</li></ul> | |
|
* Samples: |
|
| sentence2 | sentence1 | label | |
|
|:------------------------------------------------|:-----------------------------------------------------|:---------------| |
|
| <code>What is the speed of sound in air?</code> | <code>What is the speed of light in a vacuum?</code> | <code>0</code> | |
|
| <code>Steps to fix a leaking faucet</code> | <code>How to repair a leaking faucet?</code> | <code>1</code> | |
|
| <code>Total bones in an adult human</code> | <code>How many bones are in the human body?</code> | <code>1</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `gradient_accumulation_steps`: 2 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 2 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `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 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `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 |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
|
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap | |
|
|:----------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:| |
|
| 0 | 0 | - | - | 0.7947 | - | |
|
| 0.2740 | 10 | 1.6052 | - | - | - | |
|
| 0.5479 | 20 | 0.8914 | - | - | - | |
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| 0.8219 | 30 | 0.8434 | - | - | - | |
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| 0.9863 | 36 | - | 0.6144 | 0.9366 | - | |
|
| 1.0959 | 40 | 0.7351 | - | - | - | |
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| 1.3699 | 50 | 0.5016 | - | - | - | |
|
| 1.6438 | 60 | 0.3754 | - | - | - | |
|
| 1.9178 | 70 | 0.3364 | - | - | - | |
|
| 2.0 | 73 | - | 0.5985 | 0.9396 | - | |
|
| 2.1918 | 80 | 0.3456 | - | - | - | |
|
| 2.4658 | 90 | 0.1953 | - | - | - | |
|
| 2.7397 | 100 | 0.1186 | - | - | - | |
|
| 2.9863 | 109 | - | 0.5853 | 0.9455 | - | |
|
| 3.0137 | 110 | 0.1622 | - | - | - | |
|
| 3.2877 | 120 | 0.1863 | - | - | - | |
|
| 3.5616 | 130 | 0.0906 | - | - | - | |
|
| 3.8356 | 140 | 0.1035 | - | - | - | |
|
| **3.9452** | **144** | **-** | **0.5461** | **0.9454** | **0.9454** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.34.2 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
|
|
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## Citation |
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|
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### BibTeX |
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|
|
#### Sentence Transformers |
|
```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
|
publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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