File size: 7,993 Bytes
b248799 9e0248e b248799 00969a9 cf0fd57 00969a9 b248799 00969a9 b248799 |
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 77 |
---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7584
- Answer: {'precision': 0.8773364485981309, 'recall': 0.9192166462668299, 'f1': 0.8977884040645547, 'number': 817}
- Header: {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}
- Question: {'precision': 0.9097605893186004, 'recall': 0.9173630454967502, 'f1': 0.9135460009246417, 'number': 1077}
- Overall Precision: 0.8833
- Overall Recall: 0.8952
- Overall F1: 0.8892
- Overall Accuracy: 0.8076
## 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-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
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3859 | 10.5263 | 200 | 1.1901 | {'precision': 0.8349514563106796, 'recall': 0.8421052631578947, 'f1': 0.8385131017672149, 'number': 817} | {'precision': 0.42953020134228187, 'recall': 0.5378151260504201, 'f1': 0.47761194029850745, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8270 | 0.8594 | 0.8429 | 0.7738 |
| 0.0461 | 21.0526 | 400 | 1.3985 | {'precision': 0.8586448598130841, 'recall': 0.8996328029375765, 'f1': 0.8786610878661089, 'number': 817} | {'precision': 0.5, 'recall': 0.6050420168067226, 'f1': 0.5475285171102661, 'number': 119} | {'precision': 0.8864468864468864, 'recall': 0.8987929433611885, 'f1': 0.8925772245274319, 'number': 1077} | 0.8485 | 0.8818 | 0.8648 | 0.7846 |
| 0.0139 | 31.5789 | 600 | 1.4340 | {'precision': 0.8617021276595744, 'recall': 0.8922888616891065, 'f1': 0.8767288033674082, 'number': 817} | {'precision': 0.5263157894736842, 'recall': 0.5882352941176471, 'f1': 0.5555555555555555, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8470 | 0.8828 | 0.8645 | 0.8029 |
| 0.0074 | 42.1053 | 800 | 1.5489 | {'precision': 0.8450057405281286, 'recall': 0.9008567931456548, 'f1': 0.8720379146919431, 'number': 817} | {'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119} | {'precision': 0.8733738074588031, 'recall': 0.9350046425255338, 'f1': 0.9031390134529148, 'number': 1077} | 0.8512 | 0.8952 | 0.8726 | 0.7957 |
| 0.0026 | 52.6316 | 1000 | 1.6408 | {'precision': 0.8661800486618005, 'recall': 0.8714810281517748, 'f1': 0.8688224527150702, 'number': 817} | {'precision': 0.5932203389830508, 'recall': 0.5882352941176471, 'f1': 0.5907172995780592, 'number': 119} | {'precision': 0.8859964093357271, 'recall': 0.9164345403899722, 'f1': 0.9009584664536742, 'number': 1077} | 0.8612 | 0.8788 | 0.8699 | 0.7988 |
| 0.005 | 63.1579 | 1200 | 1.5299 | {'precision': 0.8518518518518519, 'recall': 0.9008567931456548, 'f1': 0.875669244497323, 'number': 817} | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119} | {'precision': 0.883128295254833, 'recall': 0.9331476323119777, 'f1': 0.90744920993228, 'number': 1077} | 0.8584 | 0.8977 | 0.8776 | 0.8010 |
| 0.004 | 73.6842 | 1400 | 1.5962 | {'precision': 0.8402699662542182, 'recall': 0.9143206854345165, 'f1': 0.8757327080890973, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119} | {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} | 0.8528 | 0.8922 | 0.8721 | 0.8084 |
| 0.0007 | 84.2105 | 1600 | 1.6587 | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119} | {'precision': 0.8946894689468947, 'recall': 0.9229340761374187, 'f1': 0.9085923217550275, 'number': 1077} | 0.8610 | 0.8957 | 0.8780 | 0.8051 |
| 0.0007 | 94.7368 | 1800 | 1.5919 | {'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817} | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119} | {'precision': 0.9030797101449275, 'recall': 0.9257195914577531, 'f1': 0.9142595139844107, 'number': 1077} | 0.8650 | 0.9011 | 0.8827 | 0.8102 |
| 0.0004 | 105.2632 | 2000 | 1.7501 | {'precision': 0.8614318706697459, 'recall': 0.9130966952264382, 'f1': 0.8865121806298276, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119} | {'precision': 0.9106976744186046, 'recall': 0.9090064995357474, 'f1': 0.9098513011152416, 'number': 1077} | 0.8730 | 0.8912 | 0.8820 | 0.8070 |
| 0.0003 | 115.7895 | 2200 | 1.7584 | {'precision': 0.8773364485981309, 'recall': 0.9192166462668299, 'f1': 0.8977884040645547, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.9097605893186004, 'recall': 0.9173630454967502, 'f1': 0.9135460009246417, 'number': 1077} | 0.8833 | 0.8952 | 0.8892 | 0.8076 |
| 0.0001 | 126.3158 | 2400 | 1.7527 | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119} | {'precision': 0.8963963963963963, 'recall': 0.9238625812441968, 'f1': 0.9099222679469592, 'number': 1077} | 0.8648 | 0.8962 | 0.8802 | 0.8057 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|