|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: [] |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:26 |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: The Supplier shall deliver the Batteries to the Manufacturer within |
|
5 days of receipt of each |
|
sentences: |
|
- according to the MOU? |
|
- What is the Delivery Schedule for the Batteries? |
|
- single order? |
|
- source_sentence: The Employee agrees to abide by the Employer’s rules, regulations, |
|
guidelines, policies, and |
|
sentences: |
|
- When does this Agreement terminate? |
|
- What rules and policies must the Employee abide by? |
|
- Which law governs this Agreement, and where would disputes be resolved? |
|
- source_sentence: 'Answer: Deepak Babbar agrees to pay Rs 5,10,000 as a full and |
|
final settlement to Ayushi' |
|
sentences: |
|
- What are the Payment Terms for the Batteries? |
|
- What financial settlement does Deepak Babbar agree to in the MOU? |
|
- order? |
|
- source_sentence: The Supplier agrees to supply 60,000 Batteries over the course |
|
of one year, as specified in |
|
sentences: |
|
- When does the Employee commence employment with the Employer? |
|
- When does the Company employ the Employee? |
|
- How many Batteries are Supplier obligated to supply under this Agreement? |
|
- source_sentence: The term of this Agreement shall continue until terminated by either |
|
party in accordance with |
|
sentences: |
|
- What is the pricing per Battery under this Agreement? |
|
- What events constitute Force Majeure under this Agreement? |
|
- What is the term of the Agreement? |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4336766652213271 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3703703703703704 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.3862433862433863 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4336766652213271 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3703703703703704 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.38703703703703707 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4336766652213271 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3703703703703704 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.38791423001949316 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5524123942573345 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.425925925925926 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.425925925925926 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2222222222222222 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13333333333333333 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4444444444444444 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.47008547008547 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("vineet10/fm") |
|
# Run inference |
|
sentences = [ |
|
'The term of this Agreement shall continue until terminated by either party in accordance with', |
|
'What is the term of the Agreement?', |
|
'What events constitute Force Majeure under this Agreement?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### 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 |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.3333 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.3333 | |
|
| cosine_accuracy@10 | 0.6667 | |
|
| cosine_precision@1 | 0.3333 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0667 | |
|
| cosine_precision@10 | 0.0667 | |
|
| cosine_recall@1 | 0.3333 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.3333 | |
|
| cosine_recall@10 | 0.6667 | |
|
| cosine_ndcg@10 | 0.4337 | |
|
| cosine_mrr@10 | 0.3704 | |
|
| **cosine_map@100** | **0.3862** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.3333 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.3333 | |
|
| cosine_accuracy@10 | 0.6667 | |
|
| cosine_precision@1 | 0.3333 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0667 | |
|
| cosine_precision@10 | 0.0667 | |
|
| cosine_recall@1 | 0.3333 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.3333 | |
|
| cosine_recall@10 | 0.6667 | |
|
| cosine_ndcg@10 | 0.4337 | |
|
| cosine_mrr@10 | 0.3704 | |
|
| **cosine_map@100** | **0.387** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.3333 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.3333 | |
|
| cosine_accuracy@10 | 0.6667 | |
|
| cosine_precision@1 | 0.3333 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0667 | |
|
| cosine_precision@10 | 0.0667 | |
|
| cosine_recall@1 | 0.3333 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.3333 | |
|
| cosine_recall@10 | 0.6667 | |
|
| cosine_ndcg@10 | 0.4337 | |
|
| cosine_mrr@10 | 0.3704 | |
|
| **cosine_map@100** | **0.3879** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.3333 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.3333 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.3333 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0667 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.3333 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.3333 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.5524 | |
|
| cosine_mrr@10 | 0.4259 | |
|
| **cosine_map@100** | **0.4259** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.3333 | |
|
| cosine_accuracy@3 | 0.6667 | |
|
| cosine_accuracy@5 | 0.6667 | |
|
| cosine_accuracy@10 | 0.6667 | |
|
| cosine_precision@1 | 0.3333 | |
|
| cosine_precision@3 | 0.2222 | |
|
| cosine_precision@5 | 0.1333 | |
|
| cosine_precision@10 | 0.0667 | |
|
| cosine_recall@1 | 0.3333 | |
|
| cosine_recall@3 | 0.6667 | |
|
| cosine_recall@5 | 0.6667 | |
|
| cosine_recall@10 | 0.6667 | |
|
| cosine_ndcg@10 | 0.5 | |
|
| cosine_mrr@10 | 0.4444 | |
|
| **cosine_map@100** | **0.4701** | |
|
|
|
<!-- |
|
## 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: 26 training samples |
|
* Columns: <code>context</code> and <code>question</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | context | question | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 19.19 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.27 tokens</li><li>max: 18 tokens</li></ul> | |
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* Samples: |
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| context | question | |
|
|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| <code>Answer: Deepak Babbar makes the final payment of Rs 2,60,000 at the time of quashing FIR</code> | <code>MOU?</code> | |
|
| <code>This Agreement is governed by the laws of Indiana, and any disputes arising out of or in</code> | <code>Which law governs this Agreement, and where would disputes be resolved?</code> | |
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| <code>Answer: After the first motion, both parties must file petitions for quashing FIRs and</code> | <code>according to the MOU?</code> | |
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
|
### Training Hyperparameters |
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#### Non-Default Hyperparameters |
|
|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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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> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: 1 |
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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`: 5 |
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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`: True |
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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`: False |
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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 |
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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 |
|
- `resume_from_checkpoint`: None |
|
- `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 |
|
- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
|
- `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 |
|
- `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 |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-----:|:----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0 | 0 | 0.4259 | 0.3879 | 0.3870 | 0.4701 | 0.3862 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
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} |
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``` |
|
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