Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use iftekher/web_classifier_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iftekher/web_classifier_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iftekher/web_classifier_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iftekher/web_classifier_model") model = AutoModelForSequenceClassification.from_pretrained("iftekher/web_classifier_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 12
Browse files
README.md
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.1347
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- Train Accuracy: 1.0
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- Epoch:
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## Model description
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### Framework versions
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1285
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- Validation Loss: 0.1347
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- Train Accuracy: 1.0
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- Epoch: 12
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## Model description
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| 0.1261 | 0.1347 | 1.0 | 9 |
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| 0.1250 | 0.1347 | 1.0 | 10 |
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| 0.1208 | 0.1347 | 1.0 | 11 |
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| 0.1285 | 0.1347 | 1.0 | 12 |
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### Framework versions
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