Text Classification
Transformers
PyTorch
Portuguese
xlm-roberta
msmarco
miniLM
tensorflow
pt-br
text-embeddings-inference
Instructions to use unicamp-dl/mMiniLM-L6-v2-mmarco-v1 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-v1 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-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-mmarco-v1") model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-mmarco-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71458b94de5f67e5021fd895a02cbc2b6557a1cc56763908287b469902b696b7
- Size of remote file:
- 428 MB
- SHA256:
- 7b6e7330c2e1c26d704fe7f3133dc6a36f32866309e91cf134460618b6d487af
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