Text Classification
Transformers
Safetensors
English
modernbert
insurance
document-classification
uk-insurance
bytical
Eval Results (legacy)
text-embeddings-inference
Instructions to use piyushptiwari/InsureDocClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use piyushptiwari/InsureDocClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="piyushptiwari/InsureDocClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("piyushptiwari/InsureDocClassifier") model = AutoModelForSequenceClassification.from_pretrained("piyushptiwari/InsureDocClassifier") - Notebooks
- Google Colab
- Kaggle
File size: 963 Bytes
06a0c29 | 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 30 31 32 33 34 35 36 37 38 39 40 | {
"labels": [
"Policy Schedule",
"Certificate of Insurance",
"Claim Form",
"Loss Adjuster Report",
"Bordereaux \u2014 Premium",
"Bordereaux \u2014 Claims",
"Endorsement",
"Renewal Notice",
"Statement of Fact",
"FNOL Report",
"Subrogation Notice",
"Policy Wording"
],
"id2label": {
"0": "Policy Schedule",
"1": "Certificate of Insurance",
"2": "Claim Form",
"3": "Loss Adjuster Report",
"4": "Bordereaux \u2014 Premium",
"5": "Bordereaux \u2014 Claims",
"6": "Endorsement",
"7": "Renewal Notice",
"8": "Statement of Fact",
"9": "FNOL Report",
"10": "Subrogation Notice",
"11": "Policy Wording"
},
"results": {
"eval_loss": 4.1706562114995904e-06,
"eval_accuracy": 1.0,
"eval_f1_macro": 1.0,
"eval_f1_weighted": 1.0,
"eval_runtime": 30.3435,
"eval_samples_per_second": 32.956,
"eval_steps_per_second": 2.076,
"epoch": 5.0
}
} |