Downloads · 30 days
5
2% of all-time downloads
albert-large-v2-finetuned-abbDet-finetuned-ner
jaggernaut007/albert-large-v2-finetuned-abbDet-finetuned-ner
albert-large-v2-finetuned-abbDet-finetuned-ner is a token classification model from jaggernaut007. 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
253
Public
Parameters
16.6M
133 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors66.6 MB · 97%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- albert
- Access
- Public
- Created
- Apr 22, 2024
- Updated
- Apr 22, 2024
- SHA
- be193b9b
Try a prompt
Base models
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. -->
albert-large-v2-finetuned-abbDet-finetuned-ner
This model is a fine-tuned version of surrey-nlp/albert-large-v2-finetuned-abbDet on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0950
- Precision: 0.9784
- Recall: 0.9763
- F1: 0.9773
- Accuracy: 0.9757
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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.37 | 100 | 0.1655 | 0.9638 | 0.9621 | 0.9629 | 0.9622 |
| No log | 0.75 | 200 | 0.1073 | 0.9752 | 0.9705 | 0.9729 | 0.9709 |
| No log | 1.12 | 300 | 0.0951 | 0.9776 | 0.9742 | 0.9759 | 0.9740 |
| No log | 1.49 | 400 | 0.0952 | 0.9778 | 0.9752 | 0.9765 | 0.9748 |
| 0.1901 | 1.87 | 500 | 0.0948 | 0.9780 | 0.9745 | 0.9763 | 0.9746 |
| 0.1901 | 2.24 | 600 | 0.0947 | 0.9788 | 0.9758 | 0.9773 | 0.9755 |
| 0.1901 | 2.61 | 700 | 0.0962 | 0.9789 | 0.9766 | 0.9778 | 0.9758 |
| 0.1901 | 2.99 | 800 | 0.0950 | 0.9784 | 0.9763 | 0.9773 | 0.9757 |
| 0.1901 | 3.36 | 900 | 0.0984 | 0.9784 | 0.9763 | 0.9773 | 0.9755 |
| 0.0493 | 3.73 | 1000 | 0.1012 | 0.9781 | 0.9759 | 0.9770 | 0.9752 |
| 0.0493 | 4.1 | 1100 | 0.1029 | 0.9781 | 0.9763 | 0.9772 | 0.9754 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- albert
- License
- apache-2.0
- Created
- Apr 22, 2024
- Updated
- Apr 22, 2024