Instructions to use AlmogMor345/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlmogMor345/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AlmogMor345/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AlmogMor345/my_awesome_model") model = AutoModelForTokenClassification.from_pretrained("AlmogMor345/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 0128260
Training in progress epoch 17
Browse files- README.md +5 -3
- 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.
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- Train Precision: 0.0
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- Train Recall: 0.0
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- Train F1: 0.0
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- Train Accuracy: 0.9140
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- Epoch:
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## Model description
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| 0.4619 | 0.5075 | 0.0 | 0.0 | 0.0 | 0.9140 | 13 |
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| 0.4268 | 0.5022 | 0.0 | 0.0 | 0.0 | 0.9140 | 14 |
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| 0.4409 | 0.4969 | 0.0 | 0.0 | 0.0 | 0.9140 | 15 |
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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: 0.4687
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- Validation Loss: 0.4834
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- Train Precision: 0.0
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- Train Recall: 0.0
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- Train F1: 0.0
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- Train Accuracy: 0.9140
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- Epoch: 17
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## Model description
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| 0.4619 | 0.5075 | 0.0 | 0.0 | 0.0 | 0.9140 | 13 |
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| 0.4268 | 0.5022 | 0.0 | 0.0 | 0.0 | 0.9140 | 14 |
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| 0.4409 | 0.4969 | 0.0 | 0.0 | 0.0 | 0.9140 | 15 |
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| 0.4958 | 0.4902 | 0.0 | 0.0 | 0.0 | 0.9140 | 16 |
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| 0.4687 | 0.4834 | 0.0 | 0.0 | 0.0 | 0.9140 | 17 |
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### Framework versions
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tf_model.h5
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size 265637136
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version https://git-lfs.github.com/spec/v1
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size 265637136
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