Instructions to use cp500/mlm-heb-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cp500/mlm-heb-medical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cp500/mlm-heb-medical")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cp500/mlm-heb-medical") model = AutoModelForMaskedLM.from_pretrained("cp500/mlm-heb-medical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +12 -5
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README.md
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This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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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 |
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### Framework versions
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This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7673
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 1.4809 | 1.0 | 5304 | 1.3390 |
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| 1.2434 | 2.0 | 10608 | 1.1131 |
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| 1.105 | 3.0 | 15912 | 0.9920 |
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| 1.0178 | 4.0 | 21216 | 0.9150 |
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| 0.961 | 5.0 | 26520 | 0.8706 |
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| 0.9326 | 6.0 | 31824 | 0.8399 |
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| 0.9119 | 7.0 | 37128 | 0.8179 |
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| 0.8716 | 8.0 | 42432 | 0.7860 |
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| 0.8644 | 9.0 | 47736 | 0.7903 |
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| 0.8666 | 10.0 | 53040 | 0.7676 |
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### Framework versions
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pytorch_model.bin
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