Instructions to use DanielNRU/pollen_re2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanielNRU/pollen_re2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanielNRU/pollen_re2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanielNRU/pollen_re2") model = AutoModelForSequenceClassification.from_pretrained("DanielNRU/pollen_re2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) 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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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| No log | 1.0 | 422 | 0.
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### Framework versions
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This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5235
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- F1: 0.8505
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| No log | 1.0 | 422 | 0.6813 | 0.3148 |
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| 0.6396 | 2.0 | 844 | 0.6553 | 0.4260 |
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| 0.6787 | 3.0 | 1266 | 0.5011 | 0.5496 |
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| 0.4929 | 4.0 | 1688 | 0.5218 | 0.6561 |
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| 0.3969 | 5.0 | 2110 | 0.5235 | 0.8505 |
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### Framework versions
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