Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/mdeberta-v3-base-vsfc-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/mdeberta-v3-base-vsfc-100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-vsfc-100")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-100") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-100") - Notebooks
- Google Colab
- Kaggle
Upload all_results.json with huggingface_hub
Browse files- all_results.json +14 -0
all_results.json
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9456727732154138,
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"eval_loss": 0.22897541522979736,
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"eval_runtime": 3.74,
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"eval_samples": 1583,
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"eval_samples_per_second": 423.264,
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"eval_steps_per_second": 26.471,
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"train_loss": 0.21180775667258037,
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"train_runtime": 281.469,
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"train_samples": 11426,
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"train_samples_per_second": 121.782,
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"train_steps_per_second": 3.816
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}
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