|
--- |
|
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:1936 |
|
- loss:OnlineContrastiveLoss |
|
widget: |
|
- source_sentence: What are the symptoms of COVID-19? |
|
sentences: |
|
- How to identify COVID-19? |
|
- What is the process for booking a dinner table? |
|
- It is not necessary to include specific fields in a financial report; nevertheless, |
|
it is beneficial to add pertinent financial metrics to help investors gauge the |
|
company's condition. |
|
- source_sentence: How to apply for a scholarship? |
|
sentences: |
|
- Steps to apply for a scholarship |
|
- Advantages of practicing meditation |
|
- When `ignore_metadata` is set to `True`, all metadata and attributes are stripped |
|
from the file prior to processing. |
|
- source_sentence: How to write a novel? |
|
sentences: |
|
- How to write a short story? |
|
- Who wrote 'Macbeth'? |
|
- How to reset a phone |
|
- source_sentence: You can wrap the project in `job.utils.data.JobLoader` and create |
|
a collate function to collate the tasks into batches. |
|
sentences: |
|
- Steps to prepare a steak |
|
- How many people live in Germany? |
|
- You can use `job.utils.data.JobLoader` to encapsulate the project and define a |
|
collate function to group the tasks into batches. |
|
- source_sentence: What is the time now? |
|
sentences: |
|
- How to cook a chicken? |
|
- Current time |
|
- Guide to starting a small business |
|
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: 0.9212962962962963 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8385236263275146 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.9403508771929825 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8385236263275146 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.9370629370629371 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.9436619718309859 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9872231100578164 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9212962962962963 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8385236263275146 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.9403508771929825 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8385236263275146 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.9370629370629371 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.9436619718309859 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9872231100578164 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9166666666666666 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.658426284790039 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.9391891891891893 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 9.594137191772461 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.9025974025974026 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.9788732394366197 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.987218816132896 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9212962962962963 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.568278431892395 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.9403508771929825 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.568278431892395 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.9370629370629371 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.9436619718309859 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9872231100578164 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9212962962962963 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.658426284790039 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.9403508771929825 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 9.594137191772461 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.9370629370629371 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.9788732394366197 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9872231100578164 |
|
name: Max Ap |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: pair class test |
|
type: pair-class-test |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.9305555555555556 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8569861650466919 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.9484536082474226 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.8531842827796936 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.9261744966442953 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.971830985915493 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.9898045699188958 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.9305555555555556 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 0.8569861650466919 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.9484536082474226 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 0.8531842231750488 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.9261744966442953 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.971830985915493 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.9898045699188958 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.9351851851851852 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 8.299823760986328 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.9517241379310345 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 8.299823760986328 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.9324324324324325 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.971830985915493 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.9895380844501982 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.9305555555555556 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 0.534814715385437 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.9484536082474226 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 0.5418605804443359 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.9261744966442953 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.971830985915493 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.9898045699188958 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.9351851851851852 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 8.299823760986328 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.9517241379310345 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 8.299823760986328 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.9324324324324325 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.971830985915493 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.9898045699188958 |
|
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_11") |
|
# Run inference |
|
sentences = [ |
|
'What is the time now?', |
|
'Current time', |
|
'Guide to starting a small business', |
|
] |
|
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 | 0.9213 | |
|
| cosine_accuracy_threshold | 0.8385 | |
|
| cosine_f1 | 0.9404 | |
|
| cosine_f1_threshold | 0.8385 | |
|
| cosine_precision | 0.9371 | |
|
| cosine_recall | 0.9437 | |
|
| cosine_ap | 0.9872 | |
|
| dot_accuracy | 0.9213 | |
|
| dot_accuracy_threshold | 0.8385 | |
|
| dot_f1 | 0.9404 | |
|
| dot_f1_threshold | 0.8385 | |
|
| dot_precision | 0.9371 | |
|
| dot_recall | 0.9437 | |
|
| dot_ap | 0.9872 | |
|
| manhattan_accuracy | 0.9167 | |
|
| manhattan_accuracy_threshold | 8.6584 | |
|
| manhattan_f1 | 0.9392 | |
|
| manhattan_f1_threshold | 9.5941 | |
|
| manhattan_precision | 0.9026 | |
|
| manhattan_recall | 0.9789 | |
|
| manhattan_ap | 0.9872 | |
|
| euclidean_accuracy | 0.9213 | |
|
| euclidean_accuracy_threshold | 0.5683 | |
|
| euclidean_f1 | 0.9404 | |
|
| euclidean_f1_threshold | 0.5683 | |
|
| euclidean_precision | 0.9371 | |
|
| euclidean_recall | 0.9437 | |
|
| euclidean_ap | 0.9872 | |
|
| max_accuracy | 0.9213 | |
|
| max_accuracy_threshold | 8.6584 | |
|
| max_f1 | 0.9404 | |
|
| max_f1_threshold | 9.5941 | |
|
| max_precision | 0.9371 | |
|
| max_recall | 0.9789 | |
|
| **max_ap** | **0.9872** | |
|
|
|
#### 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 | 0.9306 | |
|
| cosine_accuracy_threshold | 0.857 | |
|
| cosine_f1 | 0.9485 | |
|
| cosine_f1_threshold | 0.8532 | |
|
| cosine_precision | 0.9262 | |
|
| cosine_recall | 0.9718 | |
|
| cosine_ap | 0.9898 | |
|
| dot_accuracy | 0.9306 | |
|
| dot_accuracy_threshold | 0.857 | |
|
| dot_f1 | 0.9485 | |
|
| dot_f1_threshold | 0.8532 | |
|
| dot_precision | 0.9262 | |
|
| dot_recall | 0.9718 | |
|
| dot_ap | 0.9898 | |
|
| manhattan_accuracy | 0.9352 | |
|
| manhattan_accuracy_threshold | 8.2998 | |
|
| manhattan_f1 | 0.9517 | |
|
| manhattan_f1_threshold | 8.2998 | |
|
| manhattan_precision | 0.9324 | |
|
| manhattan_recall | 0.9718 | |
|
| manhattan_ap | 0.9895 | |
|
| euclidean_accuracy | 0.9306 | |
|
| euclidean_accuracy_threshold | 0.5348 | |
|
| euclidean_f1 | 0.9485 | |
|
| euclidean_f1_threshold | 0.5419 | |
|
| euclidean_precision | 0.9262 | |
|
| euclidean_recall | 0.9718 | |
|
| euclidean_ap | 0.9898 | |
|
| max_accuracy | 0.9352 | |
|
| max_accuracy_threshold | 8.2998 | |
|
| max_f1 | 0.9517 | |
|
| max_f1_threshold | 8.2998 | |
|
| max_precision | 0.9324 | |
|
| max_recall | 0.9718 | |
|
| **max_ap** | **0.9898** | |
|
|
|
<!-- |
|
## 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: 1,936 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: ~35.30%</li><li>1: ~64.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.19 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.75 tokens</li><li>max: 98 tokens</li></ul> | |
|
* Samples: |
|
| label | sentence1 | sentence2 | |
|
|:---------------|:----------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| <code>1</code> | <code>How do I apply for a credit card?</code> | <code>How do I get a credit card?</code> | |
|
| <code>1</code> | <code>What is the function of a learning rate scheduler?</code> | <code>How does a learning rate scheduler optimize training?</code> | |
|
| <code>0</code> | <code>What is the speed of a rocket?</code> | <code>What is the speed of a jet plane?</code> | |
|
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 216 evaluation samples |
|
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code> |
|
* Approximate statistics based on the first 216 samples: |
|
| | label | sentence1 | sentence2 | |
|
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | int | string | string | |
|
| details | <ul><li>0: ~34.26%</li><li>1: ~65.74%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.87 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.61 tokens</li><li>max: 86 tokens</li></ul> | |
|
* Samples: |
|
| label | sentence1 | sentence2 | |
|
|:---------------|:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| <code>0</code> | <code>What is the freezing point of ethanol?</code> | <code>What is the boiling point of ethanol?</code> | |
|
| <code>0</code> | <code>Healthy habits</code> | <code>Unhealthy habits</code> | |
|
| <code>0</code> | <code>What is the difference between omnivores and herbivores?</code> | <code>What is the difference between omnivores, carnivores, and herbivores?</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 |
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- `warmup_ratio`: 0.1 |
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- `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 |
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- `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 |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 2 |
|
- `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 |
|
- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `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 |
|
- `fsdp`: [] |
|
- `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} |
|
- `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} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
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- `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 |
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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 |
|
- `resume_from_checkpoint`: None |
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- `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 | - | - | 0.8705 | - | |
|
| 0.3279 | 10 | 1.3831 | - | - | - | |
|
| 0.6557 | 20 | 0.749 | - | - | - | |
|
| 0.9836 | 30 | 0.5578 | 0.2991 | 0.9862 | - | |
|
| 1.3115 | 40 | 0.3577 | - | - | - | |
|
| 1.6393 | 50 | 0.2594 | - | - | - | |
|
| 1.9672 | 60 | 0.2119 | - | - | - | |
|
| **2.0** | **61** | **-** | **0.2753** | **0.9898** | **-** | |
|
| 2.2951 | 70 | 0.17 | - | - | - | |
|
| 2.6230 | 80 | 0.1126 | - | - | - | |
|
| 2.9508 | 90 | 0.0538 | - | - | - | |
|
| 2.9836 | 91 | - | 0.3222 | 0.9864 | - | |
|
| 3.2787 | 100 | 0.1423 | - | - | - | |
|
| 3.6066 | 110 | 0.066 | - | - | - | |
|
| 3.9344 | 120 | 0.0486 | 0.3237 | 0.9872 | 0.9898 | |
|
|
|
* 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", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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