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mlma_nchan19_biogpt_on_adr_test_set
nimeeshachan/mlma_nchan19_biogpt_on_adr_test_set
mlma_nchan19_biogpt_on_adr_test_set is a token classification model from nimeeshachan. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- May 15, 2023
- Updated
- May 15, 2023
- SHA
- 3d39fb3f
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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. -->
mlma_nchan19_biogpt_on_adr_test_set
This model is a fine-tuned version of microsoft/biogpt on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1452
- Precision: 0.4772
- Recall: 0.5467
- F1: 0.5096
- Accuracy: 0.9523
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 448 | 0.1998 | 0.4210 | 0.3185 | 0.3626 | 0.9382 |
| 0.289 | 2.0 | 896 | 0.1630 | 0.4394 | 0.5043 | 0.4696 | 0.9474 |
| 0.1587 | 3.0 | 1344 | 0.1452 | 0.4772 | 0.5467 | 0.5096 | 0.9523 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- gpt2
- License
- mit
- Created
- May 15, 2023
- Updated
- May 15, 2023