Text Classification
Transformers
ONNX
Safetensors
English
modernbert
natural-language-inference
nli
fact-verification
fact-checking
cross-encoder
rag
evidence
Eval Results (legacy)
text-embeddings-inference
Instructions to use jithinpothireddy21/ease-delta-reader-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jithinpothireddy21/ease-delta-reader-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jithinpothireddy21/ease-delta-reader-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jithinpothireddy21/ease-delta-reader-base") model = AutoModelForSequenceClassification.from_pretrained("jithinpothireddy21/ease-delta-reader-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jithinpothireddy21/ease-delta-reader-base: direct link, hf CLI and curl.
- Browser
- Download file 2.13 MB
-
https://huggingface.co/jithinpothireddy21/ease-delta-reader-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://jithinpothireddy21/ease-delta-reader-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jithinpothireddy21/ease-delta-reader-base/resolve/main/tokenizer.json
2.13 MB
File too large to display, you can check the raw version instead.