Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use EricPeter/comments-text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/comments-text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EricPeter/comments-text-classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EricPeter/comments-text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("EricPeter/comments-text-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EricPeter/comments-text-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/EricPeter/comments-text-classification-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EricPeter/comments-text-classification-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EricPeter/comments-text-classification-model/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- 3e5260df99a4eb883846d43ba92afc1ba95ed39e1b36d07f7f88956e6e96792f
- Size of remote file:
- 1.33 GB
- SHA256:
- e60c912a25e10340d93c8cdbc940c79f826d8410c769fe081f0350f6da1a4d74
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