Text Classification
Transformers
PyTorch
English
bert
misogyny detection
abusive language
hate speech
offensive language
text-embeddings-inference
Instructions to use MilaNLProc/bert-base-uncased-ear-mlma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/bert-base-uncased-ear-mlma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/bert-base-uncased-ear-mlma")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/bert-base-uncased-ear-mlma") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/bert-base-uncased-ear-mlma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MilaNLProc/bert-base-uncased-ear-mlma: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MilaNLProc/bert-base-uncased-ear-mlma/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MilaNLProc/bert-base-uncased-ear-mlma/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MilaNLProc/bert-base-uncased-ear-mlma/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6971cf3dad691a443082eb1c1303b1552eff92a0e59bb41c444bef092e440802
- Size of remote file:
- 438 MB
- SHA256:
- 745c6e84c75e638e5194d1c328e7d6e3c66b30016d7e7bd278a08b927fde8862
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