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--- |
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base_model: sentence-transformers/all-MiniLM-L12-v2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:2144 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: How do I find out when I should write my examinations? |
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sentences: |
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- Information relating to examination timetables is available from the Examination |
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Office and will be published on the official Institute Notice Board and the website. |
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- If you find an error on your academic record, you should contact the Registration |
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and Student Records Management Office immediately. |
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- To request accommodations for a disability, you must submit documentation of the |
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disability to the disability services office and meet with a disability services |
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coordinator. |
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- source_sentence: What is the language of instruction at the Harare Institute of |
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Technology? |
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sentences: |
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- English is the language of instruction. |
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- Tracking international events and conference and strategically link them to HIT, |
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internationalizing HIT programmes and activities, developing bouquet of events |
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and activities for international visitors, helping affiliate, accredit HIT, staff |
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and students to international bodies and associations, liaising with national |
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bodies and promote Zimbabwean culture and symbols, serving as a point of contact |
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for exchange students, staff and visitors, ensuring international programmes align |
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to national programmes and symbols, helping affiliate HIT ethos to national art |
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and culture, monitoring implementation of MoUs and MoAs, facilitation of international |
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travel and visits, providing Institute departments with consular advice, ensuring |
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HIT members get oriented to particular countries’ culture and services before |
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departure, driving recruitment of foreign students and exchange programmes. |
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- BFA 7206 is the course code for Financial Institutions Fraud, which is an elective |
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course in the second semester of the program. |
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- source_sentence: What is the process for collecting a certificate? |
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sentences: |
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- The programme is designed such that on completion, graduates should be able to |
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innovatively execute their professional role within prescribed and legislative |
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parameters, demonstrate a critical understanding and application of quality assurance |
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and radiation protection in Radiography, apply scientific knowledge and technical |
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skills to perform Radiography procedures, plan, develop and apply total quality |
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management appropriate to the Radiography context, apply management, entrepreneurial, |
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education and research skills independently and function in a supervisory clinical |
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governance and quality assurance capacity within the professional sector, demonstrate |
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the ability to reflect in clinical practice, critically evaluate and adjust to |
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current and new trends in Radiography, demonstrate capability to implement new |
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knowledge and solve problems in varying contexts, and engage life-long learning |
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and development in their profession. |
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- The process involves clearing any dues to the Institute and providing valid identification |
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documents. |
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- A student can apply for change of programme within two weeks after commencement |
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of lectures. |
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- source_sentence: How do I change my address or contact information? |
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sentences: |
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- Information Security & Assurance is a field that deals with the protection of |
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information and information systems from unauthorized access, use, disclosure, |
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disruption, modification, or destruction. |
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- The Information and Communications Technology Services (ICTS) Department at HIT |
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is responsible for providing and maintaining the Institute's IT infrastructure |
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and services. |
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- You can update your address or contact information through the online student |
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portal or by contacting the Academic Registry. |
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- source_sentence: What is the difference between Cloud Computing and Information |
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Security & Assurance? |
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sentences: |
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- The fourth semester focuses on courses such as Research Project, Clinical Practice |
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IV, and Seminar. |
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- Cloud Computing is focused on the design, implementation, and management of cloud |
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services, while Information Security & Assurance is focused on the protection |
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of information by mitigating information risks and ensuring availability, privacy, |
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and integrity of data. |
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- The Applied Research Methods course is designed to equip students with the skills |
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and knowledge necessary to conduct research in chemical engineering process and |
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plant design. |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision a05860a77cef7b37e0048a7864658139bc18a854 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Dex-X/finehit") |
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# Run inference |
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sentences = [ |
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'What is the difference between Cloud Computing and Information Security & Assurance?', |
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'Cloud Computing is focused on the design, implementation, and management of cloud services, while Information Security & Assurance is focused on the protection of information by mitigating information risks and ensuring availability, privacy, and integrity of data.', |
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'The Applied Research Methods course is designed to equip students with the skills and knowledge necessary to conduct research in chemical engineering process and plant design.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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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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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 2,144 training samples |
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* Columns: <code>question</code> and <code>answer</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | question | answer | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 13.94 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 30.7 tokens</li><li>max: 128 tokens</li></ul> | |
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* Samples: |
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| question | answer | |
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|:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>What is the role of the Dean of Students?</code> | <code>The Dean of Students oversees various aspects of student life, including student affairs, campus life and development, accommodation, wellness, and more.</code> | |
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| <code>What does the Student Affairs department do?</code> | <code>The Student Affairs department handles matters related to student life, conduct, and welfare.</code> | |
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| <code>What is the role of Campus Life and Student Development?</code> | <code>Campus Life and Student Development is responsible for fostering a positive campus environment and promoting student growth and development.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```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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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 214 evaluation samples |
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* Columns: <code>question</code> and <code>answer</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | question | answer | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 15.12 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 31.14 tokens</li><li>max: 128 tokens</li></ul> | |
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* Samples: |
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| question | answer | |
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|:--------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------| |
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| <code>What is Student Accommodation and Catering?</code> | <code>Student Accommodation and Catering is a department that manages student housing and dining services.</code> | |
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| <code>What certification does Mr. Njonga have from the National Social Security Authority?</code> | <code>Safety and Health Advisor Certification</code> | |
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| <code>What is the duration of the B Tech (Hons) Computer Science programme?</code> | <code>The B Tech (Hons) Computer Science programme is a four-year full-time regular programme.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```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`: 1 |
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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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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `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`: 1 |
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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 |
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- `resume_from_checkpoint`: None |
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- `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 |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `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 |
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- `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 |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | loss | |
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|:------:|:----:|:-------------:|:------:| |
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| 0.7463 | 100 | 0.5551 | 0.0665 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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