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
File size: 709 Bytes
ac44c20 f9533e2 ac44c20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"argmax_disagreements": 0,
"device": "mps",
"max_abs_logit_difference": 1.430511474609375e-06,
"pairs": 1200,
"seconds": 6.302990674972534,
"sets": {
"mnli.dev": {
"argmax_disagreements": 0,
"max_abs_logit_difference": 1.430511474609375e-06,
"pairs": 300
},
"snli.dev": {
"argmax_disagreements": 0,
"max_abs_logit_difference": 9.5367431640625e-07,
"pairs": 300
},
"unrelated.dev": {
"argmax_disagreements": 0,
"max_abs_logit_difference": 1.430511474609375e-06,
"pairs": 300
},
"vitaminc.dev": {
"argmax_disagreements": 0,
"max_abs_logit_difference": 9.5367431640625e-07,
"pairs": 300
}
}
} |