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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased_fine_tuned_food_ner
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. -->
# distilbert-base-cased_fine_tuned_food_ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6129
- Precision: 0.9080
- Recall: 0.9328
- F1: 0.9203
- Accuracy: 0.9095
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 40 | 1.2541 | 0.7806 | 0.7299 | 0.7544 | 0.6782 |
| No log | 2.0 | 80 | 0.7404 | 0.8301 | 0.8657 | 0.8475 | 0.8047 |
| No log | 3.0 | 120 | 0.5886 | 0.8416 | 0.8900 | 0.8651 | 0.8507 |
| No log | 4.0 | 160 | 0.5094 | 0.8772 | 0.9122 | 0.8944 | 0.8727 |
| No log | 5.0 | 200 | 0.4724 | 0.8727 | 0.9159 | 0.8938 | 0.8863 |
| No log | 6.0 | 240 | 0.4471 | 0.8975 | 0.9240 | 0.9105 | 0.8960 |
| No log | 7.0 | 280 | 0.4446 | 0.9028 | 0.9255 | 0.9140 | 0.9006 |
| No log | 8.0 | 320 | 0.4437 | 0.9042 | 0.9336 | 0.9187 | 0.9032 |
| No log | 9.0 | 360 | 0.4582 | 0.9144 | 0.9299 | 0.9221 | 0.9074 |
| No log | 10.0 | 400 | 0.4525 | 0.9080 | 0.9328 | 0.9203 | 0.9066 |
| No log | 11.0 | 440 | 0.4650 | 0.9076 | 0.9351 | 0.9211 | 0.9032 |
| No log | 12.0 | 480 | 0.4725 | 0.9119 | 0.9395 | 0.9255 | 0.9095 |
| 0.406 | 13.0 | 520 | 0.4862 | 0.9161 | 0.9343 | 0.9251 | 0.9095 |
| 0.406 | 14.0 | 560 | 0.4735 | 0.9214 | 0.9424 | 0.9318 | 0.9154 |
| 0.406 | 15.0 | 600 | 0.4973 | 0.9085 | 0.9380 | 0.9230 | 0.9095 |
| 0.406 | 16.0 | 640 | 0.5075 | 0.9026 | 0.9373 | 0.9196 | 0.9099 |
| 0.406 | 17.0 | 680 | 0.5057 | 0.9124 | 0.9380 | 0.9250 | 0.9121 |
| 0.406 | 18.0 | 720 | 0.5179 | 0.9098 | 0.9380 | 0.9237 | 0.9129 |
| 0.406 | 19.0 | 760 | 0.5156 | 0.9111 | 0.9380 | 0.9244 | 0.9121 |
| 0.406 | 20.0 | 800 | 0.5325 | 0.9077 | 0.9358 | 0.9215 | 0.9099 |
| 0.406 | 21.0 | 840 | 0.5350 | 0.9203 | 0.9373 | 0.9287 | 0.9137 |
| 0.406 | 22.0 | 880 | 0.5405 | 0.9077 | 0.9365 | 0.9219 | 0.9108 |
| 0.406 | 23.0 | 920 | 0.5682 | 0.9107 | 0.9336 | 0.9220 | 0.9066 |
| 0.406 | 24.0 | 960 | 0.5545 | 0.9109 | 0.9351 | 0.9228 | 0.9095 |
| 0.0303 | 25.0 | 1000 | 0.5717 | 0.9044 | 0.9351 | 0.9194 | 0.9049 |
| 0.0303 | 26.0 | 1040 | 0.5637 | 0.9101 | 0.9343 | 0.9221 | 0.9108 |
| 0.0303 | 27.0 | 1080 | 0.5736 | 0.9102 | 0.9351 | 0.9225 | 0.9104 |
| 0.0303 | 28.0 | 1120 | 0.5793 | 0.9027 | 0.9380 | 0.9200 | 0.9074 |
| 0.0303 | 29.0 | 1160 | 0.5753 | 0.9137 | 0.9380 | 0.9257 | 0.9112 |
| 0.0303 | 30.0 | 1200 | 0.5804 | 0.9111 | 0.9380 | 0.9244 | 0.9108 |
| 0.0303 | 31.0 | 1240 | 0.5877 | 0.9123 | 0.9365 | 0.9243 | 0.9099 |
| 0.0303 | 32.0 | 1280 | 0.5837 | 0.9116 | 0.9358 | 0.9235 | 0.9087 |
| 0.0303 | 33.0 | 1320 | 0.5886 | 0.9113 | 0.9402 | 0.9255 | 0.9108 |
| 0.0303 | 34.0 | 1360 | 0.5847 | 0.9145 | 0.9387 | 0.9264 | 0.9121 |
| 0.0303 | 35.0 | 1400 | 0.5981 | 0.9083 | 0.9358 | 0.9218 | 0.9082 |
| 0.0303 | 36.0 | 1440 | 0.5963 | 0.9056 | 0.9343 | 0.9197 | 0.9095 |
| 0.0303 | 37.0 | 1480 | 0.6027 | 0.9101 | 0.9343 | 0.9221 | 0.9104 |
| 0.0086 | 38.0 | 1520 | 0.6003 | 0.9102 | 0.9351 | 0.9225 | 0.9099 |
| 0.0086 | 39.0 | 1560 | 0.5958 | 0.9082 | 0.9343 | 0.9211 | 0.9095 |
| 0.0086 | 40.0 | 1600 | 0.6054 | 0.9059 | 0.9306 | 0.9181 | 0.9091 |
| 0.0086 | 41.0 | 1640 | 0.6056 | 0.9075 | 0.9343 | 0.9207 | 0.9112 |
| 0.0086 | 42.0 | 1680 | 0.6029 | 0.9080 | 0.9321 | 0.9199 | 0.9091 |
| 0.0086 | 43.0 | 1720 | 0.6027 | 0.9109 | 0.9351 | 0.9228 | 0.9104 |
| 0.0086 | 44.0 | 1760 | 0.6071 | 0.9075 | 0.9336 | 0.9203 | 0.9099 |
| 0.0086 | 45.0 | 1800 | 0.6100 | 0.9102 | 0.9351 | 0.9225 | 0.9095 |
| 0.0086 | 46.0 | 1840 | 0.6106 | 0.9102 | 0.9351 | 0.9225 | 0.9104 |
| 0.0086 | 47.0 | 1880 | 0.6132 | 0.9101 | 0.9343 | 0.9221 | 0.9091 |
| 0.0086 | 48.0 | 1920 | 0.6134 | 0.9095 | 0.9343 | 0.9217 | 0.9095 |
| 0.0086 | 49.0 | 1960 | 0.6129 | 0.9080 | 0.9328 | 0.9203 | 0.9095 |
| 0.005 | 50.0 | 2000 | 0.6129 | 0.9080 | 0.9328 | 0.9203 | 0.9095 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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