---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:500
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
widget:
- source_sentence: Can I get academic adjustments for mental health reasons?
sentences:
- Yes, appropriate academic accommodations can be arranged through the disability
services office with documentation from mental health professionals.
- Yes, many companies conduct online aptitude tests, coding challenges, or domain-specific
assessments as part of their selection process.
- The hostel offers Wi-Fi, mess services, laundry, and recreational areas.
- source_sentence: What are the hostel meal timings?
sentences:
- Career services include career counseling, resume workshops, interview coaching,
networking events, alumni mentoring, and job search assistance for all students.
- Fee concession applications can be submitted with financial documentation to demonstrate
need. Merit-based and need-based concessions may be available.
- Mess timings are typically breakfast 7:30-9:30 AM, lunch 12:00-2:00 PM, and dinner
7:00-9:00 PM. Special arrangements may be made during exams.
- source_sentence: Is there a bond signing for certain jobs?
sentences:
- Yes, detailed placement statistics including company-wise data, salary ranges,
and sector-wise placement percentages are available on the placement portal.
- Some companies may require service agreements or bonds. All terms and conditions
are clearly communicated during the pre-placement talk.
- Emergency services are available 24/7 through campus security (extension 911),
medical emergencies (campus health center), and crisis intervention services.
- source_sentence: Where is the medical center located?
sentences:
- Most companies require no active backlogs for placement eligibility. Clear all
backlogs before the placement season to ensure maximum opportunities.
- The campus medical center is located near the main administrative building and
provides basic healthcare services during working hours.
- Yes, photocopy and printing services are available at the library, administrative
building, and near the main canteen with reasonable rates.
- source_sentence: Are there mock interviews before placements?
sentences:
- Yes, mock interviews are conducted regularly to help students practice and improve
their interview skills before actual placement interviews.
- Overnight event permissions require advance approval from student affairs, security
clearance, safety protocols, and may need faculty supervision for student organizations.
- Final year students can typically choose 2-4 elective courses depending on their
program. Check with your academic advisor for specific requirements.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). 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/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1)
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Are there mock interviews before placements?',
'Yes, mock interviews are conducted regularly to help students practice and improve their interview skills before actual placement interviews.',
'Overnight event permissions require advance approval from student affairs, security clearance, safety protocols, and may need faculty supervision for student organizations.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.7937, -0.0089],
# [ 0.7937, 1.0000, 0.0156],
# [-0.0089, 0.0156, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 500 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 500 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 11.04 tokens
- max: 17 tokens
| - min: 13 tokens
- mean: 26.67 tokens
- max: 46 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What is the policy on retroactive course drops? | Retroactive drops are rare and require exceptional circumstances with documentation. Medical emergencies or administrative errors may qualify for consideration. |
| Can I get help for eating disorders? | Specialized counseling for eating disorders is available through the health center. Confidential support includes individual therapy and referrals to specialized treatment programs. |
| Are pets allowed in campus housing? | No, pets are not allowed in campus housing including hostels and faculty quarters due to health and safety regulations. |
* 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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: False
- `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
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}