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-misogyny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/bert-base-uncased-ear-misogyny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/bert-base-uncased-ear-misogyny")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/bert-base-uncased-ear-misogyny") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/bert-base-uncased-ear-misogyny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MilaNLProc/bert-base-uncased-ear-misogyny: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MilaNLProc/bert-base-uncased-ear-misogyny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MilaNLProc/bert-base-uncased-ear-misogyny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MilaNLProc/bert-base-uncased-ear-misogyny/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 873c6fb50a4e84a07fa1015bfbc0d68095eb74adaf7a273f0655e4de952d8460
- Size of remote file:
- 438 MB
- SHA256:
- 8ba556cdcf15dbe9d23b5839d87bfbddb943cccf83c5ce9d58306f3dcdc5d62f
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