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distilbert-carpentries-restaurant-ner
karlholten/distilbert-carpentries-restaurant-ner
distilbert-carpentries-restaurant-ner is a token classification model from karlholten. 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
- distilbert
- Access
- Public
- Created
- Apr 25, 2023
- Updated
- Apr 25, 2023
- SHA
- e54c0859
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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. -->
distilbert-carpentries-restaurant-ner
This model is a fine-tuned version of distilbert-base-uncased on the mit_restaurants dataset. It achieves the following results on the evaluation set:
- Loss: 0.3068
- Precision: 0.7777
- Recall: 0.8091
- F1: 0.7931
- Accuracy: 0.9089
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 | 479 | 0.3555 | 0.7344 | 0.7764 | 0.7548 | 0.8934 |
| 0.6242 | 2.0 | 958 | 0.3149 | 0.7730 | 0.8106 | 0.7914 | 0.9063 |
| 0.297 | 3.0 | 1437 | 0.3068 | 0.7777 | 0.8091 | 0.7931 | 0.9089 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Apr 25, 2023
- Updated
- Apr 25, 2023