Text Classification
Transformers
PyTorch
Portuguese
xlm-roberta
msmarco
miniLM
tensorflow
pt-br
text-embeddings-inference
Instructions to use unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-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-en-pt-msmarco-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-en-pt-msmarco-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2") model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2: direct link, hf CLI and curl.
- Browser
- Download file 428 MB
-
https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2/resolve/main/pytorch_model.bin
428 MB
- Xet hash:
- f28d2d8ace6f487638f705b3827dfa2e1823e730dc7f5c766f8fa39fc8821057
- Size of remote file:
- 428 MB
- SHA256:
- e71bfa862091da6f3d70f6782bc72e47657afdb24abf051cbfb3ae4b3b5b8022
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