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bert_for_job_descr_parsing
jfriduss/bert_for_job_descr_parsing
bert_for_job_descr_parsing is a token classification model from jfriduss. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Sep 15, 2023
- Updated
- Sep 15, 2023
- SHA
- 30de96cb
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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. -->
tok_train_info
This model is a fine-tuned version of jjzha/jobbert_knowledge_extraction on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2616
- Precision: 0.5755
- Recall: 0.5980
- F1: 0.5865
- Accuracy: 0.9072
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: 8
- eval_batch_size: 8
- 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 | 20 | 0.4390 | 0.3790 | 0.4608 | 0.4159 | 0.8845 |
| No log | 2.0 | 40 | 0.2831 | 0.5321 | 0.5686 | 0.5498 | 0.9034 |
| No log | 3.0 | 60 | 0.2616 | 0.5755 | 0.5980 | 0.5865 | 0.9072 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 15, 2023
- Updated
- Sep 15, 2023