|
--- |
|
base_model: intfloat/multilingual-e5-small |
|
datasets: [] |
|
language: [] |
|
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 |
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pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:971 |
|
- loss:OnlineContrastiveLoss |
|
widget: |
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- source_sentence: Steps to bake a pie |
|
sentences: |
|
- How to bake a pie? |
|
- What are the ingredients of a pizza? |
|
- How to create a business plan? |
|
- source_sentence: What are the benefits of yoga? |
|
sentences: |
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- If I combine the yellow and blue colors, what color will I get? |
|
- Can you help me understand this contract? |
|
- What are the benefits of meditation? |
|
- source_sentence: Capital city of Canada |
|
sentences: |
|
- What time does the movie start? |
|
- Who is the President of the United States? |
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- What is the capital of Canada? |
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- source_sentence: Tell me about Shopify |
|
sentences: |
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- Who discovered penicillin? |
|
- Share info about Shopify |
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- Who invented the telephone? |
|
- source_sentence: What is the melting point of ice at sea level? |
|
sentences: |
|
- What is the boiling point of water at sea level? |
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- Can you recommend a good restaurant nearby? |
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- Tell me a joke |
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model-index: |
|
- name: SentenceTransformer based on intfloat/multilingual-e5-small |
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results: |
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- task: |
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type: binary-classification |
|
name: Binary Classification |
|
dataset: |
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name: pair class dev |
|
type: pair-class-dev |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.9300411522633745 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.788658857345581 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.9237668161434978 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.7819762825965881 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.8956521739130435 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9537037037037037 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9603135110633257 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9300411522633745 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.788658857345581 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.9237668161434978 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.7819762229919434 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.8956521739130435 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9537037037037037 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9603135110633257 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9218106995884774 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 9.936657905578613 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.914798206278027 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 10.316186904907227 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.8869565217391304 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9444444444444444 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9578931449470002 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9300411522633745 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.6501401662826538 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.9237668161434978 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.6603381633758545 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.8956521739130435 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9537037037037037 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9603135110633257 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9300411522633745 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 9.936657905578613 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.9237668161434978 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 10.316186904907227 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.8956521739130435 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9537037037037037 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9603135110633257 |
|
name: Max Ap |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: pair class test |
|
type: pair-class-test |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.9300411522633745 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.788658857345581 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.9237668161434978 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.7819762825965881 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.8956521739130435 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9537037037037037 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9603135110633257 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9300411522633745 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.788658857345581 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.9237668161434978 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.7819762229919434 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.8956521739130435 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9537037037037037 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9603135110633257 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9218106995884774 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 9.936657905578613 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.914798206278027 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 10.316186904907227 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.8869565217391304 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9444444444444444 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9578931449470002 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9300411522633745 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.6501401662826538 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.9237668161434978 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.6603381633758545 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.8956521739130435 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9537037037037037 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9603135110633257 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9300411522633745 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 9.936657905578613 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.9237668161434978 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 10.316186904907227 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.8956521739130435 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9537037037037037 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9603135110633257 |
|
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/multilingual-e5-small-pairclass-4") |
|
# Run inference |
|
sentences = [ |
|
'What is the melting point of ice at sea level?', |
|
'What is the boiling point of water at sea level?', |
|
'Can you recommend a good restaurant nearby?', |
|
] |
|
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 | 0.93 | |
|
| cosine_accuracy_threshold | 0.7887 | |
|
| cosine_f1 | 0.9238 | |
|
| cosine_f1_threshold | 0.782 | |
|
| cosine_precision | 0.8957 | |
|
| cosine_recall | 0.9537 | |
|
| cosine_ap | 0.9603 | |
|
| dot_accuracy | 0.93 | |
|
| dot_accuracy_threshold | 0.7887 | |
|
| dot_f1 | 0.9238 | |
|
| dot_f1_threshold | 0.782 | |
|
| dot_precision | 0.8957 | |
|
| dot_recall | 0.9537 | |
|
| dot_ap | 0.9603 | |
|
| manhattan_accuracy | 0.9218 | |
|
| manhattan_accuracy_threshold | 9.9367 | |
|
| manhattan_f1 | 0.9148 | |
|
| manhattan_f1_threshold | 10.3162 | |
|
| manhattan_precision | 0.887 | |
|
| manhattan_recall | 0.9444 | |
|
| manhattan_ap | 0.9579 | |
|
| euclidean_accuracy | 0.93 | |
|
| euclidean_accuracy_threshold | 0.6501 | |
|
| euclidean_f1 | 0.9238 | |
|
| euclidean_f1_threshold | 0.6603 | |
|
| euclidean_precision | 0.8957 | |
|
| euclidean_recall | 0.9537 | |
|
| euclidean_ap | 0.9603 | |
|
| max_accuracy | 0.93 | |
|
| max_accuracy_threshold | 9.9367 | |
|
| max_f1 | 0.9238 | |
|
| max_f1_threshold | 10.3162 | |
|
| max_precision | 0.8957 | |
|
| max_recall | 0.9537 | |
|
| **max_ap** | **0.9603** | |
|
|
|
#### 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 | 0.93 | |
|
| cosine_accuracy_threshold | 0.7887 | |
|
| cosine_f1 | 0.9238 | |
|
| cosine_f1_threshold | 0.782 | |
|
| cosine_precision | 0.8957 | |
|
| cosine_recall | 0.9537 | |
|
| cosine_ap | 0.9603 | |
|
| dot_accuracy | 0.93 | |
|
| dot_accuracy_threshold | 0.7887 | |
|
| dot_f1 | 0.9238 | |
|
| dot_f1_threshold | 0.782 | |
|
| dot_precision | 0.8957 | |
|
| dot_recall | 0.9537 | |
|
| dot_ap | 0.9603 | |
|
| manhattan_accuracy | 0.9218 | |
|
| manhattan_accuracy_threshold | 9.9367 | |
|
| manhattan_f1 | 0.9148 | |
|
| manhattan_f1_threshold | 10.3162 | |
|
| manhattan_precision | 0.887 | |
|
| manhattan_recall | 0.9444 | |
|
| manhattan_ap | 0.9579 | |
|
| euclidean_accuracy | 0.93 | |
|
| euclidean_accuracy_threshold | 0.6501 | |
|
| euclidean_f1 | 0.9238 | |
|
| euclidean_f1_threshold | 0.6603 | |
|
| euclidean_precision | 0.8957 | |
|
| euclidean_recall | 0.9537 | |
|
| euclidean_ap | 0.9603 | |
|
| max_accuracy | 0.93 | |
|
| max_accuracy_threshold | 9.9367 | |
|
| max_f1 | 0.9238 | |
|
| max_f1_threshold | 10.3162 | |
|
| max_precision | 0.8957 | |
|
| max_recall | 0.9537 | |
|
| **max_ap** | **0.9603** | |
|
|
|
<!-- |
|
## 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: 971 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: 4 tokens</li><li>mean: 10.12 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.82 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~48.61%</li><li>1: ~51.39%</li></ul> | |
|
* Samples: |
|
| sentence2 | sentence1 | label | |
|
|:----------------------------------------------------------|:--------------------------------------------------------|:---------------| |
|
| <code>Total number of bones in an adult human body</code> | <code>How many bones are in the human body?</code> | <code>1</code> | |
|
| <code>What is the largest river in North America?</code> | <code>What is the largest lake in North America?</code> | <code>0</code> | |
|
| <code>What is the capital of Australia?</code> | <code>What is the capital of New Zealand?</code> | <code>0</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 243 evaluation 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: 4 tokens</li><li>mean: 10.09 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.55 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~55.56%</li><li>1: ~44.44%</li></ul> | |
|
* Samples: |
|
| sentence2 | sentence1 | label | |
|
|:-------------------------------------------------------------|:---------------------------------------------------------------|:---------------| |
|
| <code>What are the various forms of renewable energy?</code> | <code>What are the different types of renewable energy?</code> | <code>1</code> | |
|
| <code>Gravity discoverer</code> | <code>Who discovered gravity?</code> | <code>1</code> | |
|
| <code>Can you help me write this report?</code> | <code>Can you help me understand this report?</code> | <code>0</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 |
|
- `learning_rate`: 3e-06 |
|
- `weight_decay`: 0.01 |
|
- `num_train_epochs`: 15 |
|
- `lr_scheduler_type`: reduce_lr_on_plateau |
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- `warmup_ratio`: 0.1 |
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- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
|
|
#### 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 |
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- `learning_rate`: 3e-06 |
|
- `weight_decay`: 0.01 |
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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`: 15 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: reduce_lr_on_plateau |
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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 |
|
- `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 |
|
- `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 |
|
- `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 |
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- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
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- `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`: batch_sampler |
|
- `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 | - | - | 0.6426 | - | |
|
| 0.6452 | 10 | 4.7075 | - | - | - | |
|
| 0.9677 | 15 | - | 3.1481 | 0.7843 | - | |
|
| 1.2903 | 20 | 3.431 | - | - | - | |
|
| 1.9355 | 30 | 3.4054 | - | - | - | |
|
| 2.0 | 31 | - | 2.1820 | 0.8692 | - | |
|
| 2.5806 | 40 | 2.2735 | - | - | - | |
|
| 2.9677 | 46 | - | 1.8185 | 0.9078 | - | |
|
| 3.2258 | 50 | 2.3159 | - | - | - | |
|
| 3.8710 | 60 | 2.1466 | - | - | - | |
|
| 4.0 | 62 | - | 1.5769 | 0.9252 | - | |
|
| 4.5161 | 70 | 1.6873 | - | - | - | |
|
| 4.9677 | 77 | - | 1.4342 | 0.9310 | - | |
|
| 5.1613 | 80 | 1.5927 | - | - | - | |
|
| 5.8065 | 90 | 1.4184 | - | - | - | |
|
| 6.0 | 93 | - | 1.3544 | 0.9357 | - | |
|
| 6.4516 | 100 | 1.333 | - | - | - | |
|
| 6.9677 | 108 | - | 1.2630 | 0.9402 | - | |
|
| 7.0968 | 110 | 1.089 | - | - | - | |
|
| 7.7419 | 120 | 1.0947 | - | - | - | |
|
| 8.0 | 124 | - | 1.2120 | 0.9444 | - | |
|
| 8.3871 | 130 | 0.8118 | - | - | - | |
|
| 8.9677 | 139 | - | 1.1641 | 0.9454 | - | |
|
| 9.0323 | 140 | 1.0237 | - | - | - | |
|
| 9.6774 | 150 | 0.8406 | - | - | - | |
|
| 10.0 | 155 | - | 1.0481 | 0.9464 | - | |
|
| 10.3226 | 160 | 0.7081 | - | - | - | |
|
| 10.9677 | 170 | 0.7397 | 0.9324 | 0.9509 | - | |
|
| 11.6129 | 180 | 0.5604 | - | - | - | |
|
| 12.0 | 186 | - | 0.8386 | 0.9556 | - | |
|
| 12.2581 | 190 | 0.5841 | - | - | - | |
|
| 12.9032 | 200 | 0.5463 | - | - | - | |
|
| 12.9677 | 201 | - | 0.7930 | 0.9577 | - | |
|
| 13.5484 | 210 | 0.4599 | - | - | - | |
|
| 14.0 | 217 | - | 0.7564 | 0.9599 | - | |
|
| 14.1935 | 220 | 0.2437 | - | - | - | |
|
| **14.5161** | **225** | **-** | **0.7522** | **0.9603** | **0.9603** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.32.1 |
|
- 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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