Instructions to use YakovElm/Apache10Classic_Balance_DATA_ratio_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache10Classic_Balance_DATA_ratio_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache10Classic_Balance_DATA_ratio_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache10Classic_Balance_DATA_ratio_3") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache10Classic_Balance_DATA_ratio_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload TFBertForSequenceClassification
Browse files- README.md +7 -7
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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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- Train Accuracy: 0.
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- Validation Loss: 0.
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- Validation Accuracy: 0.
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- Epoch: 2
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## Model description
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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: 0.4992
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- Train Accuracy: 0.7637
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- Validation Loss: 0.5755
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- Validation Accuracy: 0.7336
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- Epoch: 2
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## Model description
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
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|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
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| 0.5466 | 0.7493 | 0.5843 | 0.7029 | 0 |
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| 0.5130 | 0.7596 | 0.5762 | 0.7377 | 1 |
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| 0.4992 | 0.7637 | 0.5755 | 0.7336 | 2 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 438223128
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