Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ViktorDo/EcoBERT-POWO_Life_Form_Pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/EcoBERT-POWO_Life_Form_Pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/EcoBERT-POWO_Life_Form_Pretrained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/EcoBERT-POWO_Life_Form_Pretrained") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/EcoBERT-POWO_Life_Form_Pretrained", 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 [ViktorDo/EcoBERT-Pretrained](https://huggingface.co/ViktorDo/EcoBERT-Pretrained) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [ViktorDo/EcoBERT-Pretrained](https://huggingface.co/ViktorDo/EcoBERT-Pretrained) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4176
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.4758 | 1.0 | 1004 | 0.4511 |
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| 0.3869 | 2.0 | 2008 | 0.4154 |
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| 0.3417 | 3.0 | 3012 | 0.4176 |
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### Framework versions
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