Text Classification
Transformers
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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jithinpothireddy21/ease-delta-reader with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jithinpothireddy21/ease-delta-reader")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jithinpothireddy21/ease-delta-reader") model = AutoModelForSequenceClassification.from_pretrained("jithinpothireddy21/ease-delta-reader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 704 Bytes
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"argmax_disagreements": 0,
"device": "mps",
"max_abs_logit_difference": 9.5367431640625e-07,
"pairs": 1200,
"seconds": 15.925388097763062,
"sets": {
"mnli.dev": {
"argmax_disagreements": 0,
"max_abs_logit_difference": 9.5367431640625e-07,
"pairs": 300
},
"snli.dev": {
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},
"unrelated.dev": {
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"max_abs_logit_difference": 9.5367431640625e-07,
"pairs": 300
},
"vitaminc.dev": {
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}
}
} |