Fill-Mask
Transformers
Safetensors
Portuguese
bert
feature-extraction
legal
licitação
editais
custom_code
Instructions to use tcepi/helbert-lsg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcepi/helbert-lsg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tcepi/helbert-lsg", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tcepi/helbert-lsg", trust_remote_code=True) model = AutoModel.from_pretrained("tcepi/helbert-lsg", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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```python
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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tokenizer = AutoTokenizer.from_pretrained("tcepi/helbert-
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model = AutoModelForMaskedLM.from_pretrained("tcepi/helbert-
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input_text = "A proposta será avaliada com base no critério do [MASK]."
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inputs = tokenizer(input_text, return_tensors="pt")
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```python
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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tokenizer = AutoTokenizer.from_pretrained("tcepi/helbert-lsg")
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model = AutoModelForMaskedLM.from_pretrained("tcepi/helbert-lsg")
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input_text = "A proposta será avaliada com base no critério do [MASK]."
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inputs = tokenizer(input_text, return_tensors="pt")
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