Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Manaranjan/my_sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manaranjan/my_sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Manaranjan/my_sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Manaranjan/my_sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("Manaranjan/my_sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
bfd3af8
1
Parent(s): 27e2ab1
Training in progress epoch 1
Browse files
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: 0.6747
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- Train Accuracy: 0.475
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- Epoch:
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## Model description
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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| 0.6817 | 0.6747 | 0.475 | 0 |
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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: 0.6713
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- Validation Loss: 0.6747
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- Train Accuracy: 0.475
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- Epoch: 1
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## Model description
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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|:----------:|:---------------:|:--------------:|:-----:|
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| 0.6817 | 0.6747 | 0.475 | 0 |
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| 0.6713 | 0.6747 | 0.475 | 1 |
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### Framework versions
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