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M_gpt_v1.5
terzimert/M_gpt_v1.5
M_gpt_v1.5 is a token classification model from terzimert. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- gpt2
- Access
- Public
- Created
- May 22, 2023
- Updated
- May 22, 2023
- SHA
- 693c0561
Try a prompt
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. -->
M_gpt_v1.5
This model is a fine-tuned version of ai-forever/mGPT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5589
- Precision: 0.4836
- Recall: 0.2252
- F1: 0.3073
- Accuracy: 0.8959
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.643 | 1.0 | 1532 | 0.4457 | 0.4 | 0.1450 | 0.2129 | 0.8911 |
| 0.4563 | 2.0 | 3065 | 0.5391 | 0.4667 | 0.1870 | 0.2670 | 0.8963 |
| 0.3724 | 3.0 | 4596 | 0.5589 | 0.4836 | 0.2252 | 0.3073 | 0.8959 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- gpt2
- License
- apache-2.0
- Created
- May 22, 2023
- Updated
- May 22, 2023