File size: 2,801 Bytes
193d748 |
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 74 75 76 |
---
language:
- vi
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large_baseline_words
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. -->
# xlm-roberta-large_baseline_words
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the covid19_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0850
- Patient Id: 0.9840
- Name: 0.7711
- Gender: 0.9767
- Age: 0.9821
- Job: 0.8062
- Location: 0.9570
- Organization: 0.8784
- Date: 0.9869
- Symptom And Disease: 0.8688
- Transportation: 1.0
- F1 Macro: 0.9211
- F1 Micro: 0.9459
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Patient Id | Name | Gender | Age | Job | Location | Organization | Date | Symptom And Disease | Transportation | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:------:|:------:|:------:|:--------:|:------------:|:------:|:-------------------:|:--------------:|:--------:|:--------:|
| 0.2002 | 1.0 | 629 | 0.1212 | 0.9160 | 0.8349 | 0.8346 | 0.8122 | 0.5385 | 0.8769 | 0.7201 | 0.9553 | 0.7769 | 0.8587 | 0.8124 | 0.8586 |
| 0.0557 | 2.0 | 1258 | 0.1025 | 0.9594 | 0.8836 | 0.9533 | 0.9731 | 0.2841 | 0.9228 | 0.8301 | 0.9842 | 0.8545 | 0.9444 | 0.8590 | 0.9191 |
| 0.038 | 3.0 | 1887 | 0.0804 | 0.9741 | 0.7154 | 0.9732 | 0.9821 | 0.7615 | 0.9372 | 0.8576 | 0.9869 | 0.8461 | 0.9943 | 0.9028 | 0.9309 |
| 0.0222 | 4.0 | 2516 | 0.0862 | 0.9871 | 0.5567 | 0.9691 | 0.9862 | 0.8207 | 0.9553 | 0.8707 | 0.9847 | 0.8740 | 1.0 | 0.9005 | 0.9398 |
| 0.0148 | 5.0 | 3145 | 0.0850 | 0.9840 | 0.7711 | 0.9767 | 0.9821 | 0.8062 | 0.9570 | 0.8784 | 0.9869 | 0.8688 | 1.0 | 0.9211 | 0.9459 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|