File size: 2,080 Bytes
4809f3c be210b1 4809f3c be210b1 4809f3c be210b1 4809f3c |
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 |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-large-cased-ner-food-combined-v2
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. -->
# xlnet-large-cased-ner-food-combined-v2
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0681
- Precision: 0.8554
- Recall: 0.8743
- F1: 0.8647
- Accuracy: 0.9769
## 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-06
- train_batch_size: 16
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2606 | 1.12 | 500 | 0.0822 | 0.7976 | 0.8664 | 0.8306 | 0.9712 |
| 0.0837 | 2.25 | 1000 | 0.0955 | 0.7657 | 0.8764 | 0.8173 | 0.9683 |
| 0.0706 | 3.37 | 1500 | 0.0732 | 0.8322 | 0.8714 | 0.8513 | 0.9750 |
| 0.0631 | 4.49 | 2000 | 0.0681 | 0.8554 | 0.8743 | 0.8647 | 0.9769 |
| 0.0549 | 5.62 | 2500 | 0.0713 | 0.8356 | 0.8868 | 0.8604 | 0.9754 |
| 0.0521 | 6.74 | 3000 | 0.0700 | 0.8425 | 0.8863 | 0.8639 | 0.9759 |
| 0.0493 | 7.87 | 3500 | 0.0721 | 0.8444 | 0.8859 | 0.8647 | 0.9763 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|