Downloads · 30 days
11
2% of all-time downloads
bert-base-uncased-finetuned-ner
graphcore-rahult/bert-base-uncased-finetuned-ner
bert-base-uncased-finetuned-ner is a token classification model from graphcore-rahult. 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. --
All-time downloads
589
Public
Repo size
4.1 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin218 MB · 100%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Nov 29, 2022
- Updated
- Feb 24, 2023
- SHA
- 7a422042
Try a prompt
Datasets
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. -->
bert-base-uncased-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0626
- Precision: 0.9201
- Recall: 0.9350
- F1: 0.9275
- Accuracy: 0.9832
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
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0943 | 1.0 | 877 | 0.0687 | 0.9019 | 0.9149 | 0.9084 | 0.9801 |
| 0.2395 | 2.0 | 1754 | 0.0623 | 0.9221 | 0.9298 | 0.9259 | 0.9829 |
| 0.0241 | 3.0 | 2631 | 0.0626 | 0.9201 | 0.9350 | 0.9275 | 0.9832 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 29, 2022
- Updated
- Feb 24, 2023