Instructions to use addykan/model_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use addykan/model_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="addykan/model_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("addykan/model_data") model = AutoModelForSequenceClassification.from_pretrained("addykan/model_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +8 -31
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step
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| 1.103 | 3.0 | 18000 | 1.1026 | 0.3333 |
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| 1.101 | 4.0 | 24000 | 1.1163 | 0.3333 |
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| 1.1027 | 5.0 | 30000 | 1.0987 | 0.3333 |
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| 1.1026 | 6.0 | 36000 | 1.0988 | 0.3333 |
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| 1.1039 | 7.0 | 42000 | 1.0998 | 0.3333 |
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| 1.1045 | 8.0 | 48000 | 1.1015 | 0.3333 |
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| 1.1026 | 9.0 | 54000 | 1.1206 | 0.3333 |
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| 1.1035 | 10.0 | 60000 | 1.1042 | 0.3333 |
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| 1.1027 | 11.0 | 66000 | 1.0989 | 0.3333 |
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| 1.1011 | 12.0 | 72000 | 1.1176 | 0.3333 |
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| 1.1015 | 13.0 | 78000 | 1.0994 | 0.3333 |
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| 1.1025 | 14.0 | 84000 | 1.0993 | 0.3333 |
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| 1.1007 | 15.0 | 90000 | 1.1004 | 0.3333 |
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| 1.101 | 16.0 | 96000 | 1.1016 | 0.3333 |
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| 1.0999 | 17.0 | 102000 | 1.1028 | 0.3333 |
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| 1.1003 | 18.0 | 108000 | 1.0988 | 0.3333 |
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| 1.0986 | 19.0 | 114000 | 1.0989 | 0.3333 |
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| 1.0999 | 20.0 | 120000 | 1.0987 | 0.3333 |
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| 1.0997 | 21.0 | 126000 | 1.0989 | 0.3333 |
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| 1.0992 | 22.0 | 132000 | 1.0990 | 0.3333 |
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| 1.0991 | 23.0 | 138000 | 1.0986 | 0.3333 |
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| 1.0989 | 24.0 | 144000 | 1.0986 | 0.3333 |
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| 1.0984 | 25.0 | 150000 | 1.0986 | 0.3333 |
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### Framework versions
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This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0010
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- Accuracy: 0.9999
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.0134 | 1.0 | 6000 | 0.0009 | 0.9999 |
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| 0.0 | 2.0 | 12000 | 0.0010 | 0.9999 |
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### Framework versions
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model.safetensors
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size 528371004
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version https://git-lfs.github.com/spec/v1
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size 528371004
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