Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use atrisaxena/covid-tweet-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atrisaxena/covid-tweet-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atrisaxena/covid-tweet-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atrisaxena/covid-tweet-classification") model = AutoModelForSequenceClassification.from_pretrained("atrisaxena/covid-tweet-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 1679ca8
Training in progress epoch 4
Browse files- README.md +3 -2
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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: 0.2786
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- Train Accuracy: 0.9282
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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-base-uncased](https://huggingface.co/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: 0.0168
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- Validation Loss: 0.2786
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- Train Accuracy: 0.9282
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- Epoch: 4
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## Model description
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| 0.0194 | 0.2786 | 0.9282 | 1 |
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| 0.0162 | 0.2786 | 0.9282 | 2 |
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| 0.0167 | 0.2786 | 0.9282 | 3 |
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### Framework versions
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tf_model.h5
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