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---
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
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