|
--- |
|
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:160 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Priya Softweb emphasizes the importance of maintaining a clean |
|
and organized workspace. The company's HR policies clearly state that employees |
|
are responsible for keeping their assigned workspaces clean, orderly, and free |
|
from unnecessary items. Spitting tobacco, gum, or other substances in the washrooms |
|
is strictly prohibited. The company believes that a clean and organized work environment |
|
contributes to a more efficient and professional work experience for everyone. |
|
This emphasis on cleanliness reflects the company's commitment to creating a pleasant |
|
and hygienic workspace for its employees. |
|
sentences: |
|
- What is Priya Softweb's policy on the use of mobile phones during work hours? |
|
- What steps does Priya Softweb take to ensure that the workspace is clean and organized? |
|
- What are the repercussions for employees who violate the Non-Disclosure Agreement |
|
at Priya Softweb? |
|
- source_sentence: Priya Softweb provides allocated basement parking facilities for |
|
employees to park their two-wheelers and four-wheelers. However, parking on the |
|
ground floor, around the lawn or main premises, is strictly prohibited as this |
|
space is reserved for Directors. Employees should use the parking under wings |
|
5 and 6, while other parking spaces are allocated to different wings. Parking |
|
two-wheelers in the car parking zone is not permitted, even if space is available. |
|
Two-wheelers should be parked in the designated basement space on the main stand, |
|
not on the side stand. Employees are encouraged to park in common spaces on a |
|
first-come, first-served basis. The company clarifies that it is not responsible |
|
for providing parking and that employees park their vehicles at their own risk. |
|
This comprehensive parking policy ensures organized parking arrangements and clarifies |
|
the company's liability regarding vehicle safety. |
|
sentences: |
|
- What is the application process for planned leaves at Priya Softweb? |
|
- What are the parking arrangements at Priya Softweb? |
|
- What is the process for reporting a security breach at Priya Softweb? |
|
- source_sentence: The Diwali bonus at Priya Softweb is a discretionary benefit linked |
|
to the company's business performance. Distributed during the festive season of |
|
Diwali, it serves as a gesture of appreciation for employees' contributions throughout |
|
the year. However, it's important to note that employees currently under the notice |
|
period are not eligible for this bonus. This distinction highlights that the bonus |
|
is intended to reward ongoing commitment and contribution to the company's success. |
|
sentences: |
|
- What steps does Priya Softweb take to promote responsible use of company resources? |
|
- How does Priya Softweb demonstrate its commitment to Diversity, Equity, and Inclusion |
|
(DEI)? |
|
- What is the significance of the company's Diwali bonus at Priya Softweb? |
|
- source_sentence: Priya Softweb's HR Manual paints a picture of a company that values |
|
its employees while upholding a strong sense of professionalism and ethical conduct. |
|
The company emphasizes a structured and transparent approach to its HR processes, |
|
ensuring clarity and fairness in areas like recruitment, performance appraisals, |
|
compensation, leave management, work-from-home arrangements, and incident reporting. |
|
The manual highlights the importance of compliance with company policies, promotes |
|
diversity and inclusion, and encourages a culture of continuous learning and development. |
|
Overall, the message conveyed is one of creating a supportive, respectful, and |
|
growth-oriented work environment for all employees. |
|
sentences: |
|
- What is the overall message conveyed by Priya Softweb's HR Manual? |
|
- What is the process for reporting employee misconduct at Priya Softweb? |
|
- What is Priya Softweb's policy on salary disbursement and payslips? |
|
- source_sentence: No, work-from-home arrangements do not affect an employee's employment |
|
terms, compensation, and benefits at Priya Softweb. This clarifies that work-from-home |
|
is a flexible work arrangement and does not impact the employee's overall employment |
|
status or benefits. |
|
sentences: |
|
- Do work-from-home arrangements affect compensation and benefits at Priya Softweb? |
|
- What is the objective of the Work From Home Policy at Priya Softweb? |
|
- What is the procedure for a new employee joining Priya Softweb? |
|
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: 0.8333333333333334 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33333333333333326 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.923940541865081 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.898148148148148 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.898148148148148 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33333333333333326 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.923940541865081 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.898148148148148 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.898148148148148 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33333333333333326 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8333333333333334 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9312144170634953 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9074074074074076 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9074074074074073 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33333333333333326 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9107105144841319 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8796296296296297 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8796296296296295 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6111111111111112 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9444444444444444 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6111111111111112 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.31481481481481477 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6111111111111112 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9444444444444444 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.826662566744103 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7685185185185186 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7685185185185185 |
|
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("kr-manish/fine-tune-embedding-bge-base-HrPolicy") |
|
# Run inference |
|
sentences = [ |
|
"No, work-from-home arrangements do not affect an employee's employment terms, compensation, and benefits at Priya Softweb. This clarifies that work-from-home is a flexible work arrangement and does not impact the employee's overall employment status or benefits.", |
|
'Do work-from-home arrangements affect compensation and benefits at Priya Softweb?', |
|
'What is the objective of the Work From Home Policy at Priya Softweb?', |
|
] |
|
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 | 0.8333 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8333 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8333 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9239 | |
|
| cosine_mrr@10 | 0.8981 | |
|
| **cosine_map@100** | **0.8981** | |
|
|
|
#### 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 | 0.8333 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8333 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8333 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9239 | |
|
| cosine_mrr@10 | 0.8981 | |
|
| **cosine_map@100** | **0.8981** | |
|
|
|
#### 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 | 0.8333 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8333 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8333 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9312 | |
|
| cosine_mrr@10 | 0.9074 | |
|
| **cosine_map@100** | **0.9074** | |
|
|
|
#### 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 | 0.7778 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.7778 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.7778 | |
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| cosine_recall@3 | 1.0 | |
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| cosine_recall@5 | 1.0 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.9107 | |
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| cosine_mrr@10 | 0.8796 | |
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| **cosine_map@100** | **0.8796** | |
|
|
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#### 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 | 0.6111 | |
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| cosine_accuracy@3 | 0.9444 | |
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| cosine_accuracy@5 | 0.9444 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.6111 | |
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| cosine_precision@3 | 0.3148 | |
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| cosine_precision@5 | 0.1889 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.6111 | |
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| cosine_recall@3 | 0.9444 | |
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| cosine_recall@5 | 0.9444 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.8267 | |
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| cosine_mrr@10 | 0.7685 | |
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| **cosine_map@100** | **0.7685** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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## Training Details |
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|
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### Training Dataset |
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|
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#### Unnamed Dataset |
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|
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* Size: 160 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 18 tokens</li><li>mean: 93.95 tokens</li><li>max: 381 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 20.32 tokens</li><li>max: 34 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------| |
|
| <code>Priya Softweb's HR Manual provides valuable insights into the company's culture and values. Key takeaways include: * **Structure and Transparency:** The company emphasizes a structured and transparent approach to its HR processes. This is evident in its clear policies for recruitment, performance appraisals, compensation, leave management, work-from-home arrangements, and incident reporting. * **Professionalism and Ethics:** Priya Softweb places a high value on professionalism and ethical conduct. Its dress code, guidelines for mobile phone usage, and strict policies against tobacco use within the office all point toward a commitment to maintaining a professional and respectful work environment. * **Employee Well-being:** The company demonstrates a genuine concern for the well-being of its employees. This is reflected in its comprehensive leave policies, flexible work-from-home arrangements, and efforts to promote a healthy and clean workspace. * **Diversity and Inclusion:** Priya Softweb is committed to fostering a diverse and inclusive workplace, where employees from all backgrounds feel valued and respected. Its DEI policy outlines the company's commitment to equal opportunities, diverse hiring practices, and inclusive benefits and policies. * **Continuous Learning and Development:** The company encourages a culture of continuous learning and development, providing opportunities for employees to expand their skillsets and stay current with industry advancements. This is evident in its policies for Ethics & Compliance training and its encouragement of utilizing idle time for self-learning and exploring new technologies. Overall, Priya Softweb's HR Manual reveals a company culture that prioritizes structure, transparency, professionalism, employee well-being, diversity, and a commitment to continuous improvement. The company strives to create a supportive and growth-oriented work environment where employees feel valued and empowered to succeed.</code> | <code>What are the key takeaways from Priya Softweb's HR Manual regarding the company's culture and values?</code> | |
|
| <code>Priya Softweb provides allocated basement parking facilities for employees to park their two-wheelers and four-wheelers. However, parking on the ground floor, around the lawn or main premises, is strictly prohibited as this space is reserved for Directors. Employees should use the parking under wings 5 and 6, while other parking spaces are allocated to different wings. Parking two-wheelers in the car parking zone is not permitted, even if space is available. Two-wheelers should be parked in the designated basement space on the main stand, not on the side stand. Employees are encouraged to park in common spaces on a first-come, first-served basis. The company clarifies that it is not responsible for providing parking and that employees park their vehicles at their own risk. This comprehensive parking policy ensures organized parking arrangements and clarifies the company's liability regarding vehicle safety.</code> | <code>What are the parking arrangements at Priya Softweb?</code> | |
|
| <code>Investments and declarations must be submitted on or before the 25th of each month through OMS at Priya Softweb.</code> | <code>What is the deadline for submitting investments and declarations at Priya Softweb?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
|
512, |
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256, |
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128, |
|
64 |
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], |
|
"matryoshka_weights": [ |
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1, |
|
1, |
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1, |
|
1, |
|
1 |
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], |
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"n_dims_per_step": -1 |
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} |
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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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|
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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`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 10 |
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- `lr_scheduler_type`: cosine |
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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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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `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`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 10 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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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 |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
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- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
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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 |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | 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 | - | 0.5729 | 0.5863 | 0.6595 | 0.5079 | 0.6896 | |
|
| 1.0 | 1 | - | 0.6636 | 0.6914 | 0.8213 | 0.6036 | 0.8472 | |
|
| 2.0 | 2 | - | 0.7833 | 0.8148 | 0.9352 | 0.7171 | 0.8796 | |
|
| 3.0 | 3 | - | 0.8213 | 0.8519 | 0.8981 | 0.7333 | 0.8981 | |
|
| 4.0 | 5 | - | 0.8426 | 0.9074 | 0.8981 | 0.75 | 0.8981 | |
|
| 5.0 | 6 | - | 0.8426 | 0.9074 | 0.8981 | 0.7685 | 0.8981 | |
|
| **6.0** | **7** | **-** | **0.8796** | **0.9074** | **0.8981** | **0.7685** | **0.8981** | |
|
| 7.0 | 9 | - | 0.8796 | 0.9074 | 0.8981 | 0.7685 | 0.8981 | |
|
| 8.0 | 10 | 0.5275 | 0.8796 | 0.9074 | 0.8981 | 0.7685 | 0.8981 | |
|
|
|
* 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: 0.31.0 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### 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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*Clearly define terms in order to be accessible across audiences.* |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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