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mini-gte

Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
mteb
Eval Results
text-embeddings-inference
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mini-gte
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: distilbert/distilbert-base-uncased

model-index:
- name: prdev/mini-gte
  results:
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 74.8955
    - type: f1
      value: 68.84209999999999
    - type: f1_weighted
      value: 77.1819
    - type: ap
      value: 37.731500000000004
    - type: ap_weighted
      value: 37.731500000000004
    - type: main_score
      value: 74.8955
    task:
      type: Classification

pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# Mini-GTE

This is a distillbert-based model trained from GTE-base. It can be used as a faster query encoder for the GTE series or as a standalone unit (MTEB scores are for standalone). 

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity

## 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 = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
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]
```

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## Training Details

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.1.0a0+32f93b1
- Accelerate: 1.2.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

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