Text Classification
Transformers
PyTorch
Portuguese
xlm-roberta
msmarco
miniLM
tensorflow
pt-br
text-embeddings-inference
Instructions to use unicamp-dl/mMiniLM-L6-v2-mmarco-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/mMiniLM-L6-v2-mmarco-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="unicamp-dl/mMiniLM-L6-v2-mmarco-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-mmarco-v2") model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-mmarco-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 219bc6a1e4e93c757870eb94f7623839eae8190a1ae99322cf38d87089f39ce0
- Size of remote file:
- 428 MB
- SHA256:
- 4d8c8ebbe711409bcaa613f496e2029c7e31a487edb80ad5691020ef2b0205cb
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