Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use mirfan899/da-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mirfan899/da-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mirfan899/da-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mirfan899/da-sentiment") model = AutoModelForSequenceClassification.from_pretrained("mirfan899/da-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) 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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- 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 | Validation Loss | Accuracy |
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| No log | 1.0 |
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| No log | 2.0 |
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### Framework versions
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This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4976
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- Accuracy: 0.8377
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## Model description
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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: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 1.0 | 58 | 0.6168 | 0.7446 |
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| No log | 2.0 | 116 | 0.5689 | 0.7554 |
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| 0.4255 | 3.0 | 174 | 0.5542 | 0.7814 |
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| 0.4255 | 4.0 | 232 | 0.5224 | 0.7944 |
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| 0.2655 | 5.0 | 290 | 0.5172 | 0.8030 |
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| 0.2655 | 6.0 | 348 | 0.4992 | 0.8182 |
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| 0.1944 | 7.0 | 406 | 0.4852 | 0.8290 |
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| 0.1944 | 8.0 | 464 | 0.4972 | 0.8312 |
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| 0.1772 | 9.0 | 522 | 0.4905 | 0.8333 |
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| 0.1772 | 10.0 | 580 | 0.4915 | 0.8290 |
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| 0.1772 | 11.0 | 638 | 0.4936 | 0.8333 |
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| 0.1654 | 12.0 | 696 | 0.4939 | 0.8333 |
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| 0.1654 | 13.0 | 754 | 0.4944 | 0.8377 |
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| 0.1605 | 14.0 | 812 | 0.4969 | 0.8355 |
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| 0.1605 | 15.0 | 870 | 0.4976 | 0.8377 |
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### Framework versions
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