Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use snigdaa/trial-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snigdaa/trial-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="snigdaa/trial-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("snigdaa/trial-model") model = AutoModelForSequenceClassification.from_pretrained("snigdaa/trial-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ac47888a3e7347857be20decc58b8f73c70ea8a007b418a456559647b8df652
- Size of remote file:
- 4.6 kB
- SHA256:
- db8cb8e8d96880ea9d168a40352a80cb2871f12127f51e55bdd57a314f221e3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.