Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use mhassanch/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mhassanch/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mhassanch/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mhassanch/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("mhassanch/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mhassanch/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/mhassanch/my_awesome_model/resolve/main/training_args.bin
- Command line
-
hf download hf://mhassanch/my_awesome_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mhassanch/my_awesome_model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- f408a3d0241078ba5dcca6c0353221c718bc210105bfc16e403611f3f6ef5ad0
- Size of remote file:
- 5.37 kB
- SHA256:
- 82ca141b4521daf4f75cc464e9f8581966f6a1ce0fa9cc1603a1cae6989fa616
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.