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bert-multilingual-pt-archive
lfcc/bert-multilingual-pt-archive
bert-multilingual-pt-archive is a token classification model from lfcc. 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
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 11, 2021
- SHA
- a3fda178
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. -->
portuguese-archival-finding-aids
This model is a fine-tuned version of bert-base-multilingual-cased on an unkown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.8624
- Recall: 0.9557
- F1: 0.9067
- Accuracy: 0.9618
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 192 | 0.1565 | 0.8511 | 0.9327 | 0.8900 | 0.9563 |
| 0.1849 | 2.0 | 384 | 0.1594 | 0.8634 | 0.9543 | 0.9065 | 0.9619 |
| 0.0454 | 3.0 | 576 | 0.1812 | 0.8624 | 0.9557 | 0.9067 | 0.9618 |
Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Dec 11, 2021