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--- |
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tags: |
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- transformers |
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- text-classification |
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- text-embedding |
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- tinybert |
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license: apache-2.0 |
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library_name: transformers |
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widget: |
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- text: "Encode this text using TinyBERT" |
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--- |
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# 🚀 TinyBERT Encoder Model |
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This is a fine-tuned **TinyBERT Encoder** model, optimized for lightweight NLP tasks. |
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## 🔹 Use This Model |
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To use this model with **transformers**, simply run: |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model_name = "hjsgfd/my_tinybert_encoder" # Replace with your actual repo name |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained(model_name) |
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# Encode text |
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text = "TinyBERT is small but powerful." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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print(outputs.last_hidden_state) # Encoded text representation |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("hjsgfd/my_tinybert_encoder") |
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embeddings = model.encode("This is an example sentence.") |
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print(embeddings) |
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--- |
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# TinyBERT Encoder Model |
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This is a fine-tuned **TinyBERT Encoder** model optimized for lightweight NLP tasks. |
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## 🔹 How to Use |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model_name = " hjsgfd/my_tinybert_encoder" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained(model_name) |
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# Encode text |
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text = "TinyBERT is small but powerful." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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print(outputs.last_hidden_state) # Encoded text representation |
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