---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2244
- loss:MultipleNegativesRankingLoss
- Education
- Retrieval
- Syllabus
widget:
- source_sentence: Participation in both lecture and discussion sections is required.
sentences:
- >-
Service learning: 1.5 to 2 units depending on portfolio evaluation. Must
meet 15 service hours for max credit.
- >-
Students are expected to attend both lectures and discussion sessions for
full participation credit.
- 'General info mailbox: replies from Dr. Anil Goyal and Prof. Lucy Salgado.'
- source_sentence: What is the name of the TA?
sentences:
- The instructors on record are Prof. Jae Sun Kim and Dr. Bea Valdez.
- TAs include Priya Reddy and Lena Hoffmann.
- 'The official hours: 4 credits.'
- source_sentence: Who are the teaching staff?
sentences:
- 'Teaching staff: Dr. Doris Ren, Prof. David Sarpong, Prof. Olivia Boland.'
- 'Teaching faculty: Dr. Malak Mahfouz and Prof. William Ruiz.'
- 'Lab teaching assistants: Julio Mendez, Siri Eriksson.'
- source_sentence: Who operates the Canvas FAQ as TA?
sentences:
- 'FAQ moderators: Pia Fenwick, Pilar Nasser. Email canvasfaq@school.edu.'
- 'Designation: 3 credit hours.'
- 'Lab: 1 hour (semester).'
- source_sentence: What is the eligibility requirement to register for extra unit load?
sentences:
- Completion awards three credit hours.
- 'Eligibility for extra: GPA >= 3.5 and at least 30 completed units.'
- 'The academic credit hours: 2'
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("rsajja/Fine-tuned-Educational-Model-MNRL")
# Run inference
sentences = [
'What is the eligibility requirement to register for extra unit load?',
'Eligibility for extra: GPA >= 3.5 and at least 30 completed units.',
'Completion awards three credit hours.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,244 training samples
* Columns: sentence_0
and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details |
Students with disabilities may request accommodations.
| Accessibility services are available to students when needed.
|
| Who teaches the course?
| This course is taught collaboratively by Dr. Louise McCann and Dr. Omar Franco.
|
| State the credit hour load for this section.
| Credit load: 3.5 hours
|
* Loss: [MultipleNegativesRankingLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 25
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters