---
license: mit
---
---
language: en
license: apache-2.0
---

# My BERT Model

This is a BERT model fine-tuned for extracting embeddings from CVs and startup descriptions for matching purposes.

## Model Details

- **Architecture:** BERT-base-uncased
- **Use case:** CV and Startup matching
- **Training data:** Not applicable (pre-trained model used)

## How to use

```python
from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained("your_username/your_model_name")
model = BertModel.from_pretrained("your_username/your_model_name")

text = "Sample text"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
embedding = outputs.last_hidden_state.mean(dim=1).detach().numpy()