File size: 6,867 Bytes
3c8802f a5d8943 3c8802f a5d8943 3c8802f a5d8943 3c8802f 93970f5 3c8802f 93970f5 3c8802f a5d8943 3c8802f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-6
tags:
- generated_from_trainer
model-index:
- name: my_awesome_ner-token_classification_v1.0.7-6
results: []
---
<!-- 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. -->
# my_awesome_ner-token_classification_v1.0.7-6
This model is a fine-tuned version of [lilyyellow/my_awesome_ner-token_classification_v1.0.7-6](https://huggingface.co/lilyyellow/my_awesome_ner-token_classification_v1.0.7-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3464
- Age: {'precision': 0.9153846153846154, 'recall': 0.8880597014925373, 'f1': 0.9015151515151514, 'number': 134}
- Datetime: {'precision': 0.6675824175824175, 'recall': 0.7386018237082067, 'f1': 0.7012987012987013, 'number': 987}
- Disease: {'precision': 0.6712328767123288, 'recall': 0.7480916030534351, 'f1': 0.7075812274368231, 'number': 262}
- Event: {'precision': 0.28023598820059, 'recall': 0.3392857142857143, 'f1': 0.30694668820678517, 'number': 280}
- Gender: {'precision': 0.6947368421052632, 'recall': 0.7586206896551724, 'f1': 0.7252747252747253, 'number': 87}
- Law: {'precision': 0.5664556962025317, 'recall': 0.7019607843137254, 'f1': 0.626970227670753, 'number': 255}
- Location: {'precision': 0.6860587002096437, 'recall': 0.7292479108635097, 'f1': 0.7069943289224953, 'number': 1795}
- Organization: {'precision': 0.6145833333333334, 'recall': 0.7019167217448777, 'f1': 0.6553532860228325, 'number': 1513}
- Person: {'precision': 0.6846725185685347, 'recall': 0.7294964028776978, 'f1': 0.7063740856844305, 'number': 1390}
- Quantity: {'precision': 0.5110782865583456, 'recall': 0.6113074204946997, 'f1': 0.5567176186645212, 'number': 566}
- Role: {'precision': 0.4672897196261682, 'recall': 0.5484460694698354, 'f1': 0.5046257359125315, 'number': 547}
- Transportation: {'precision': 0.46, 'recall': 0.6, 'f1': 0.5207547169811321, 'number': 115}
- Overall Precision: 0.6197
- Overall Recall: 0.6915
- Overall F1: 0.6536
- Overall Accuracy: 0.8962
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Age | Datetime | Disease | Event | Gender | Law | Location | Organization | Person | Quantity | Role | Transportation | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2089 | 1.9965 | 1156 | 0.3464 | {'precision': 0.9153846153846154, 'recall': 0.8880597014925373, 'f1': 0.9015151515151514, 'number': 134} | {'precision': 0.6675824175824175, 'recall': 0.7386018237082067, 'f1': 0.7012987012987013, 'number': 987} | {'precision': 0.6712328767123288, 'recall': 0.7480916030534351, 'f1': 0.7075812274368231, 'number': 262} | {'precision': 0.28023598820059, 'recall': 0.3392857142857143, 'f1': 0.30694668820678517, 'number': 280} | {'precision': 0.6947368421052632, 'recall': 0.7586206896551724, 'f1': 0.7252747252747253, 'number': 87} | {'precision': 0.5664556962025317, 'recall': 0.7019607843137254, 'f1': 0.626970227670753, 'number': 255} | {'precision': 0.6860587002096437, 'recall': 0.7292479108635097, 'f1': 0.7069943289224953, 'number': 1795} | {'precision': 0.6145833333333334, 'recall': 0.7019167217448777, 'f1': 0.6553532860228325, 'number': 1513} | {'precision': 0.6846725185685347, 'recall': 0.7294964028776978, 'f1': 0.7063740856844305, 'number': 1390} | {'precision': 0.5110782865583456, 'recall': 0.6113074204946997, 'f1': 0.5567176186645212, 'number': 566} | {'precision': 0.4672897196261682, 'recall': 0.5484460694698354, 'f1': 0.5046257359125315, 'number': 547} | {'precision': 0.46, 'recall': 0.6, 'f1': 0.5207547169811321, 'number': 115} | 0.6197 | 0.6915 | 0.6536 | 0.8962 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|