Text Classification
Transformers
PyTorch
Portuguese
xlm-roberta
msmarco
miniLM
tensorflow
pt-br
text-embeddings-inference
Instructions to use unicamp-dl/mMiniLM-L6-v2-pt-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-pt-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-pt-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2") model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from unicamp-dl/mMiniLM-L6-v2-pt-v2: direct link, hf CLI and curl.
- Browser
- Download file 428 MB
-
https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-pt-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://unicamp-dl/mMiniLM-L6-v2-pt-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-pt-v2/resolve/main/pytorch_model.bin
428 MB
- Xet hash:
- 998efcc84ff2a3cbe2c57cf0a6f75756b85ceae4b867bff8c27eb440f036d231
- Size of remote file:
- 428 MB
- SHA256:
- b0fc25356518b3c6bc8aa48a2af3b5ad5639938140795c5af618341ccbbc2120
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