Instructions to use rajendrabaskota/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajendrabaskota/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajendrabaskota/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajendrabaskota/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("rajendrabaskota/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.758cc55
-
rajendrabaskota/hc3-wiki-domain-classification-roberta
0e3ac9c -
rajendrabaskota/hc3-wiki-domain-classification-roberta
8d50f76 -
rajendrabaskota/tmp_trainer
ca8b397 -
Upload RobertaForSequenceClassification
9d51d94 -
hc3-wiki-domain-classification-roberta-copy
086d60a -
initial commit
4a7002e