Instructions to use dhanush23/emo_bert_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhanush23/emo_bert_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dhanush23/emo_bert_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dhanush23/emo_bert_1") model = AutoModel.from_pretrained("dhanush23/emo_bert_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dhanush23/emo_bert_1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/dhanush23/emo_bert_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dhanush23/emo_bert_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dhanush23/emo_bert_1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 5f0a06a43f23144651b498348cf7e3b6a9d526db28b11731ca68312102bd3091
- Size of remote file:
- 438 MB
- SHA256:
- dbea7a2d271558b2c269671aceda8a7090b394939f7034de31b23b8df7c2a64f
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