Instructions to use jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position") model = AutoModelForSequenceClassification.from_pretrained("jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position: direct link, hf CLI and curl.
- Browser
- Download file 1.9 GB
-
https://huggingface.co/jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position/resolve/main/optimizer.pt
- Command line
-
hf download hf://jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/jusgowiturs/Tamil_Bert_L3_Cube_Relative_Position/resolve/main/optimizer.pt
1.9 GB
- Xet hash:
- fc6301b07437e4bbccff51f284b7615015918e5cfce4abc78860efddd2cb5b9c
- Size of remote file:
- 1.9 GB
- SHA256:
- 2d40ae99367233b33b35a92ffdbc287781cf11df6a1adf12fe2ae879c4f8c9b5
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