Instructions to use gouravchat/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gouravchat/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gouravchat/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gouravchat/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("gouravchat/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51936ea8d5eaa12df317c4bfb416b4b74a1162264d127a07cdd6cc565f4ecf1d
- Size of remote file:
- 3.45 kB
- SHA256:
- d39a7c9728790db240c7023eab8f4adde488aab71822ee22d8ac9bd111f83f9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.