Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use absonS/finetuning_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use absonS/finetuning_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="absonS/finetuning_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("absonS/finetuning_test") model = AutoModelForSequenceClassification.from_pretrained("absonS/finetuning_test") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- all_results.json +8 -0
- test_results.json +10 -0
all_results.json
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{
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"epoch": 1.0,
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"total_flos": 1340245973568.0,
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"train_loss": 0.6966156482696533,
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"train_runtime": 839.2284,
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{
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"epoch": 1.0,
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"test_accuracy": 0.75,
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"test_f1-score": 0.8571428571428571,
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"test_loss": 0.6540221571922302,
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"test_precision": 0.75,
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"test_recall": 1.0,
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"test_runtime": 2.4978,
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"test_samples_per_second": 3.203,
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"test_steps_per_second": 0.4,
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"total_flos": 1340245973568.0,
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"train_loss": 0.6966156482696533,
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"train_runtime": 839.2284,
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test_results.json
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{
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"test_accuracy": 0.75,
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"test_f1-score": 0.8571428571428571,
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"test_loss": 0.6540221571922302,
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"test_precision": 0.75,
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"test_recall": 1.0,
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"test_runtime": 2.4978,
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"test_samples_per_second": 3.203,
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"test_steps_per_second": 0.4
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}
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