---
language: en
license: mit
library_name: transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-embeddings
  - retrieval
  - contrastive-learning
  - bert
base_model: bert-base-uncased
datasets:
  - nyu-mll/multi_nli
metrics:
  - cosine
---

# Vectra

BERT-base sentence embeddings trained with in-batch contrastive learning (Multiple Negatives Ranking Loss) on MultiNLI entailment pairs.

## Model

- Base: `bert-base-uncased`
- Pooling: mean pooling over token embeddings (masked)
- Normalization: L2
- Objective: MNRL / InfoNCE-style softmax with temperature 0.05
- Training data: MultiNLI entailment pairs (subset)

## Usage (embeddings)

```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(last_hidden_state, attention_mask):
    mask = attention_mask.unsqueeze(-1).to(dtype=last_hidden_state.dtype)
    summed = (last_hidden_state * mask).sum(dim=1)
    counts = mask.sum(dim=1).clamp(min=1e-6)
    return summed / counts

@torch.no_grad()
def embed_texts(texts, model_id="rafidka/vectra", max_length=128, device="cuda"):
    tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
    model = AutoModel.from_pretrained(model_id, add_pooling_layer=False).to(device).eval()
    batch = tok(texts, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt").to(device)
    out = model(**batch)
    emb = mean_pooling(out.last_hidden_state, batch["attention_mask"])
    emb = F.normalize(emb, p=2, dim=-1)
    return emb
```