|
--- |
|
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 has specific guidelines for managing the arrival |
|
of international shipments. To ensure smooth customs clearance, the company requires |
|
an authorization letter from the client, written on their company letterhead. |
|
This letter must clearly state that the shipment is "Not for commercial purposes" |
|
to prevent the application of duty charges by the customs office. All international |
|
shipments should be addressed to Keyur Patel at Priya Softweb Solutions Pvt. Ltd., |
|
with the company's full address and contact information clearly indicated. Employees |
|
are advised to contact the HR department for the correct format of the authorization |
|
letter and to inform Keyur Patel about the expected arrival of such shipments. |
|
These procedures streamline the handling of international shipments and help avoid |
|
potential customs-related delays or complications. |
|
sentences: |
|
- Female employees at Priya Softweb are allowed to wear:- Formal trousers/jeans |
|
and shirts- Sarees- Formal skirts- T-shirts with collars- Chudidars & Kurtis- |
|
Salwar SuitsHowever, they are not allowed to wear:- Round neck, deep neck, cold |
|
shoulder, and fancy T-shirts- Low waist jeans, short T-shirts, and short shirts- |
|
Transparent wear- Wear with deep-cut sleeves- Capris- Slippers- Visible tattoos |
|
& piercingsPriya Softweb emphasizes a professional appearance for its employees |
|
while providing flexibility in choosing appropriate attire within the defined |
|
guidelines. |
|
- Priya Softweb has specific guidelines for managing the arrival of international |
|
shipments. To ensure smooth customs clearance, the company requires an authorization |
|
letter from the client, written on their company letterhead. This letter must |
|
clearly state that the shipment is "Not for commercial purposes" to prevent the |
|
application of duty charges by the customs office. All international shipments |
|
should be addressed to Keyur Patel at Priya Softweb Solutions Pvt. Ltd., with |
|
the company's full address and contact information clearly indicated. Employees |
|
are advised to contact the HR department for the correct format of the authorization |
|
letter and to inform Keyur Patel about the expected arrival of such shipments. |
|
These procedures streamline the handling of international shipments and help avoid |
|
potential customs-related delays or complications. |
|
- Priya Softweb has a structured onboarding process for new employees. Upon joining, |
|
new hires undergo an induction program conducted by the HR department. This program |
|
introduces them to the company's culture, values, processes, and policies, ensuring |
|
they are well-acquainted with the work environment and expectations. HR also facilitates |
|
introductions to the relevant department and sends out a company-wide email announcing |
|
the new employee's arrival. Additionally, new employees are required to complete |
|
quarterly Ethics & Compliance training to familiarize themselves with the company's |
|
ethical standards and compliance requirements. This comprehensive onboarding approach |
|
helps new employees integrate seamlessly into the company and quickly become productive |
|
members of the team. |
|
- source_sentence: The sanctioning and approving authority for Casual Leave, Sick |
|
Leave, and Privilege Leave at Priya Softweb is the Leader/Manager. |
|
sentences: |
|
- Even if an employee utilizes the 'Hybrid' Work From Home model for only half a |
|
day, a full count is deducted from their monthly allowance of 4 WFH days. This |
|
clarifies that any utilization of the 'Hybrid' model, regardless of the duration, |
|
is considered a full WFH day and counts towards the monthly limit. |
|
- The sanctioning and approving authority for Casual Leave, Sick Leave, and Privilege |
|
Leave at Priya Softweb is the Leader/Manager. |
|
- To be eligible for gratuity at Priya Softweb, an employee must have completed |
|
a minimum of 5 continuous years of service. This ensures that only long-term employees |
|
are entitled to this benefit. |
|
- source_sentence: 'Priya Softweb utilizes Employee Agreements/Bonds as a mechanism |
|
to retain talent within the company. These agreements are implemented in various |
|
situations, including: * **Retention:** When the company seeks to retain valuable |
|
employees who have resigned, a 15-month bond may be applied based on the company''s |
|
requirements. * **Freshers:** New employees with 0 to 1 year of experience are |
|
generally subject to an 18-month bond. * **Rejoining:** When former employees |
|
are rehired, a 15-month bond is typically implemented. These bond periods vary |
|
based on the specific circumstances and aim to ensure a certain level of commitment |
|
from employees, especially in roles that require significant investment in training |
|
and development.' |
|
sentences: |
|
- To claim gratuity, employees must submit an application form to the Accounts department. |
|
This formal process ensures proper documentation and timely processing of the |
|
gratuity payment. |
|
- Priya Softweb acknowledges the efforts of employees who work late hours. Employees |
|
working more than 11 hours on weekdays are eligible for reimbursement of up to |
|
Rs. 250/- for their dinner expenses. However, this reimbursement is subject to |
|
approval from their Department Head. This policy recognizes the extra effort put |
|
in by employees working extended hours and provides some financial compensation |
|
for their meals. |
|
- 'Priya Softweb utilizes Employee Agreements/Bonds as a mechanism to retain talent |
|
within the company. These agreements are implemented in various situations, including: |
|
* **Retention:** When the company seeks to retain valuable employees who have |
|
resigned, a 15-month bond may be applied based on the company''s requirements. |
|
* **Freshers:** New employees with 0 to 1 year of experience are generally subject |
|
to an 18-month bond. * **Rejoining:** When former employees are rehired, a 15-month |
|
bond is typically implemented. These bond periods vary based on the specific circumstances |
|
and aim to ensure a certain level of commitment from employees, especially in |
|
roles that require significant investment in training and development.' |
|
- source_sentence: Chewing tobacco, gutka, gum, or smoking within the office premises |
|
is strictly prohibited at Priya Softweb. Bringing such substances inside the office |
|
will lead to penalties and potentially harsh decisions from management. This strict |
|
policy reflects Priya Softweb's commitment to a healthy and clean work environment. |
|
sentences: |
|
- Chewing tobacco, gutka, gum, or smoking within the office premises is strictly |
|
prohibited at Priya Softweb. Bringing such substances inside the office will lead |
|
to penalties and potentially harsh decisions from management. This strict policy |
|
reflects Priya Softweb's commitment to a healthy and clean work environment. |
|
- In situations of 'Bad Weather', the HR department at Priya Softweb will enable |
|
the 'Work From Home' option within the OMS system based on the severity of the |
|
weather and potential safety risks for employees commuting to the office. This |
|
proactive approach prioritizes employee safety and allows for flexible work arrangements |
|
during adverse weather events. |
|
- Priya Softweb employees are entitled to 5 Casual Leaves (CL) per year. |
|
- source_sentence: Priya Softweb prioritizes the health and wellness of its employees. |
|
The company strongly prohibits chewing tobacco, gutka, gum, or smoking within |
|
the office premises. Penalties and harsh decisions from management await anyone |
|
found bringing such substances into the office. Furthermore, carrying food to |
|
the desk is not permitted. Employees are encouraged to use the terrace dining |
|
facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness |
|
and orderliness in the workspace. Employees are responsible for maintaining their |
|
designated work areas, keeping them clean, organized, and free from unnecessary |
|
items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. |
|
These policies contribute to a healthier and more pleasant work environment for |
|
everyone. |
|
sentences: |
|
- Priya Softweb prioritizes the health and wellness of its employees. The company |
|
strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. |
|
Penalties and harsh decisions from management await anyone found bringing such |
|
substances into the office. Furthermore, carrying food to the desk is not permitted. |
|
Employees are encouraged to use the terrace dining facility for lunch, snacks, |
|
and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. |
|
Employees are responsible for maintaining their designated work areas, keeping |
|
them clean, organized, and free from unnecessary items. Spitting gutka, gum, or |
|
tobacco in the washrooms is strictly prohibited. These policies contribute to |
|
a healthier and more pleasant work environment for everyone. |
|
- The Performance Appraisal at Priya Softweb is solely based on the employee's performance |
|
evaluation. The evaluation score is compiled by the Team Leader/Project Manager, |
|
who also gives the final rating to the team member. Detailed recommendations are |
|
provided by the TL/PM, and increment or promotion is granted accordingly. This |
|
process ensures that performance is the primary factor driving salary revisions |
|
and promotions. |
|
- Priya Softweb actively promotes diversity in its hiring practices. The company |
|
focuses on recruiting individuals from a wide range of backgrounds, including |
|
different races, ethnicities, religions, political beliefs, education levels, |
|
socio-economic backgrounds, geographical locations, languages, and cultures. This |
|
commitment to diversity enriches the company culture and brings in a variety of |
|
perspectives and experiences. |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 1.0 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 1.0 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 1.0 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 1.0 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 1.0 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 1.0 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 1.0 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 1.0 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
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: 1.0 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 1.0 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 1.0 |
|
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_vfinal") |
|
# Run inference |
|
sentences = [ |
|
'Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone.', |
|
'Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone.', |
|
"The Performance Appraisal at Priya Softweb is solely based on the employee's performance evaluation. The evaluation score is compiled by the Team Leader/Project Manager, who also gives the final rating to the team member. Detailed recommendations are provided by the TL/PM, and increment or promotion is granted accordingly. This process ensures that performance is the primary factor driving salary revisions and promotions.", |
|
] |
|
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.* |
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--> |
|
|
|
## 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 | 1.0 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 1.0 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 1.0 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 1.0 | |
|
| cosine_mrr@10 | 1.0 | |
|
| **cosine_map@100** | **1.0** | |
|
|
|
#### 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 | 1.0 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 1.0 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 1.0 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 1.0 | |
|
| cosine_mrr@10 | 1.0 | |
|
| **cosine_map@100** | **1.0** | |
|
|
|
#### 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 | 1.0 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 1.0 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 1.0 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 1.0 | |
|
| cosine_mrr@10 | 1.0 | |
|
| **cosine_map@100** | **1.0** | |
|
|
|
#### 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 | 1.0 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 1.0 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 1.0 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 1.0 | |
|
| cosine_mrr@10 | 1.0 | |
|
| **cosine_map@100** | **1.0** | |
|
|
|
#### 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 | 1.0 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 1.0 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 1.0 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 1.0 | |
|
| cosine_mrr@10 | 1.0 | |
|
| **cosine_map@100** | **1.0** | |
|
|
|
<!-- |
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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.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
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|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 160 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 16 tokens</li><li>mean: 90.76 tokens</li><li>max: 380 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 90.76 tokens</li><li>max: 380 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>The general timings for the Marketing team vary: BD works from 1:00 PM to 10:00 PM or 3:00 PM to 12:00 AM, while BA/SEO works from 11:00 AM to 8:00 PM.</code> | <code>The general timings for the Marketing team vary: BD works from 1:00 PM to 10:00 PM or 3:00 PM to 12:00 AM, while BA/SEO works from 11:00 AM to 8:00 PM.</code> | |
|
| <code>Priya Softweb acknowledges the efforts of employees who work late hours. Employees working more than 11 hours on weekdays are eligible for reimbursement of up to Rs. 250/- for their dinner expenses. However, this reimbursement is subject to approval from their Department Head. This policy recognizes the extra effort put in by employees working extended hours and provides some financial compensation for their meals.</code> | <code>Priya Softweb acknowledges the efforts of employees who work late hours. Employees working more than 11 hours on weekdays are eligible for reimbursement of up to Rs. 250/- for their dinner expenses. However, this reimbursement is subject to approval from their Department Head. This policy recognizes the extra effort put in by employees working extended hours and provides some financial compensation for their meals.</code> | |
|
| <code>While Priya Softweb allows employees to keep their cell phones during work hours for emergency purposes, excessive personal mobile phone usage and lengthy calls within the office premises are strictly prohibited. Excessive use may result in disciplinary actions. This policy aims to strike a balance between allowing accessibility for emergencies and maintaining a productive work environment free from distractions.</code> | <code>While Priya Softweb allows employees to keep their cell phones during work hours for emergency purposes, excessive personal mobile phone usage and lengthy calls within the office premises are strictly prohibited. Excessive use may result in disciplinary actions. This policy aims to strike a balance between allowing accessibility for emergencies and maintaining a productive work environment free from distractions.</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 3e-05 |
|
- `num_train_epochs`: 15 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `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`: 3e-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`: 15 |
|
- `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 |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `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 |
|
- `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 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| **1.0** | **1** | **-** | **1.0** | **1.0** | **1.0** | **1.0** | **1.0** | |
|
| 2.0 | 3 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 3.0 | 4 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 4.0 | 6 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 5.0 | 8 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 6.0 | 9 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 6.4 | 10 | 0.0767 | - | - | - | - | - | |
|
| 7.0 | 11 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 8.0 | 12 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 9.0 | 13 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| 10.0 | 15 | - | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### 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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## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
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<!-- |
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## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
