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chembert_cased-MLM-chemistry-textCLS-RHEOLOGY
jonas-luehrs/chembert_cased-MLM-chemistry-textCLS-RHEOLOGY
chembert_cased-MLM-chemistry-textCLS-RHEOLOGY is a text classification model from jonas-luehrs. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Sep 22, 2023
- Updated
- Sep 22, 2023
- SHA
- 7e40a6a9
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
chembert_cased-MLM-chemistry-textCLS-RHEOLOGY
This model is a fine-tuned version of jonas-luehrs/chembert_cased-MLM-chemistry on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5925
- F1: 0.7527
- Precision: 0.7836
- Recall: 0.7716
- Accuracy: 0.7716
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.1553 | 1.0 | 46 | 0.8494 | 0.6731 | 0.6580 | 0.7099 | 0.7099 |
| 0.7613 | 2.0 | 92 | 0.6545 | 0.7297 | 0.7155 | 0.7593 | 0.7593 |
| 0.5792 | 3.0 | 138 | 0.5925 | 0.7527 | 0.7836 | 0.7716 | 0.7716 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 22, 2023
- Updated
- Sep 22, 2023