Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use tKah/DistilBERT-TxtClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tKah/DistilBERT-TxtClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tKah/DistilBERT-TxtClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tKah/DistilBERT-TxtClassification") model = AutoModelForSequenceClassification.from_pretrained("tKah/DistilBERT-TxtClassification") - Notebooks
- Google Colab
- Kaggle
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# DistilBERT-TxtClassification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1911
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- Validation Loss: 0.5444
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# DistilBERT-TxtClassification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an CoLA dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1911
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- Validation Loss: 0.5444
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