README.md

---
tags:
- transformers
- text-classification
- text-embedding
- tinybert
license: apache-2.0
library_name: transformers
widget:
  - text: "Encode this text using TinyBERT"
---

# 🚀 TinyBERT Encoder Model

This is a fine-tuned **TinyBERT Encoder** model, optimized for lightweight NLP tasks.

## 🔹 Use This Model

To use this model with **transformers**, simply run:

```python
from transformers import AutoModel, AutoTokenizer

model_name = "hjsgfd/my_tinybert_encoder"  # Replace with your actual repo name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Encode text
text = "TinyBERT is small but powerful."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

print(outputs.last_hidden_state)  # Encoded text representation


from sentence_transformers import SentenceTransformer

model = SentenceTransformer("hjsgfd/my_tinybert_encoder")
embeddings = model.encode("This is an example sentence.")
print(embeddings)
---


# TinyBERT Encoder Model

This is a fine-tuned **TinyBERT Encoder** model optimized for lightweight NLP tasks.

## 🔹 How to Use

```python
from transformers import AutoModel, AutoTokenizer

model_name = " hjsgfd/my_tinybert_encoder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Encode text
text = "TinyBERT is small but powerful."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

print(outputs.last_hidden_state)  # Encoded text representation