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roberta-large-ner-new
douglasadams11/roberta-large-ner-new
roberta-large-ner-new is a token classification model from douglasadams11. 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
- roberta
- Access
- Public
- Created
- Dec 27, 2023
- Updated
- Dec 27, 2023
- SHA
- 0e1375d1
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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. -->
roberta-large-ner-new
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1106
- Precision: 0.9670
- Recall: 0.9604
- F1: 0.9637
- Accuracy: 0.9600
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1241 | 0.71 | 5000 | 0.1161 | 0.9618 | 0.9505 | 0.9561 | 0.9521 |
| 0.0993 | 1.42 | 10000 | 0.1132 | 0.9633 | 0.9568 | 0.9600 | 0.9562 |
| 0.0812 | 2.13 | 15000 | 0.1223 | 0.9662 | 0.9574 | 0.9618 | 0.9580 |
| 0.074 | 2.84 | 20000 | 0.1118 | 0.9661 | 0.9607 | 0.9634 | 0.9598 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Dec 27, 2023
- Updated
- Dec 27, 2023