transformers tensorboard safetensors lilt token-classification generated_from_trainer base_model:doc2txt/lilt_roberta_like_en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: doc2txt/lilt_roberta_like_en
tags:
- generated_from_trainer
model-index:
- name: tst_fine-tuning-lilt2
  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. -->

# tst_fine-tuning-lilt2

This model is a fine-tuned version of [doc2txt/lilt_roberta_like_en](https://huggingface.co/doc2txt/lilt_roberta_like_en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4439
- Answer: {'precision': 0.14533622559652928, 'recall': 0.2460220318237454, 'f1': 0.18272727272727274, 'number': 817}
- Header: {'precision': 0.033482142857142856, 'recall': 0.12605042016806722, 'f1': 0.05291005291005291, 'number': 119}
- Question: {'precision': 0.27887323943661974, 'recall': 0.4596100278551532, 'f1': 0.34712482468443195, 'number': 1077}
- Overall Precision: 0.1972
- Overall Recall: 0.3532
- Overall F1: 0.2531
- Overall Accuracy: 0.4392

## 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 |
|:-------------:|:------:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3672        | 10.53  | 200  | 1.6951          | {'precision': 0.11161290322580646, 'recall': 0.211750305997552, 'f1': 0.14617659484579637, 'number': 817}   | {'precision': 0.0498220640569395, 'recall': 0.11764705882352941, 'f1': 0.06999999999999999, 'number': 119}    | {'precision': 0.26964933494558646, 'recall': 0.4141132776230269, 'f1': 0.32662028560966677, 'number': 1077} | 0.1816            | 0.3145         | 0.2303     | 0.4331           |
| 0.4121        | 21.05  | 400  | 2.9692          | {'precision': 0.12375621890547264, 'recall': 0.24357405140758873, 'f1': 0.16412371134020617, 'number': 817} | {'precision': 0.023622047244094488, 'recall': 0.07563025210084033, 'f1': 0.036, 'number': 119}                | {'precision': 0.26241534988713316, 'recall': 0.43175487465181056, 'f1': 0.3264303264303264, 'number': 1077} | 0.1789            | 0.3343         | 0.2331     | 0.4366           |
| 0.0862        | 31.58  | 600  | 4.0881          | {'precision': 0.1258692628650904, 'recall': 0.2215422276621787, 'f1': 0.1605321507760532, 'number': 817}    | {'precision': 0.020833333333333332, 'recall': 0.07563025210084033, 'f1': 0.032667876588021776, 'number': 119} | {'precision': 0.26891220320265047, 'recall': 0.4521819870009285, 'f1': 0.3372576177285318, 'number': 1077}  | 0.1839            | 0.3363         | 0.2378     | 0.4278           |
| 0.0334        | 42.11  | 800  | 4.5480          | {'precision': 0.11213626685592619, 'recall': 0.193390452876377, 'f1': 0.14195867026055706, 'number': 817}   | {'precision': 0.021691973969631236, 'recall': 0.08403361344537816, 'f1': 0.034482758620689655, 'number': 119} | {'precision': 0.2576306259699948, 'recall': 0.4623955431754875, 'f1': 0.33089700996677734, 'number': 1077}  | 0.1751            | 0.3308         | 0.2290     | 0.4257           |
| 0.0171        | 52.63  | 1000 | 4.8940          | {'precision': 0.13886671987230648, 'recall': 0.21297429620563035, 'f1': 0.1681159420289855, 'number': 817}  | {'precision': 0.026217228464419477, 'recall': 0.11764705882352941, 'f1': 0.042879019908116385, 'number': 119} | {'precision': 0.2647058823529412, 'recall': 0.4596100278551532, 'f1': 0.335934848998982, 'number': 1077}    | 0.1868            | 0.3393         | 0.2409     | 0.4297           |
| 0.0101        | 63.16  | 1200 | 5.0531          | {'precision': 0.13835701050030882, 'recall': 0.2741738066095471, 'f1': 0.18390804597701146, 'number': 817}  | {'precision': 0.024886877828054297, 'recall': 0.09243697478991597, 'f1': 0.0392156862745098, 'number': 119}   | {'precision': 0.2734418865805727, 'recall': 0.4521819870009285, 'f1': 0.34079776067179846, 'number': 1077}  | 0.1879            | 0.3587         | 0.2466     | 0.4270           |
| 0.0058        | 73.68  | 1400 | 5.2725          | {'precision': 0.13805185704274703, 'recall': 0.24112607099143207, 'f1': 0.17557932263814616, 'number': 817} | {'precision': 0.033707865168539325, 'recall': 0.12605042016806722, 'f1': 0.05319148936170213, 'number': 119}  | {'precision': 0.2645301466594242, 'recall': 0.4521819870009285, 'f1': 0.3337902673063742, 'number': 1077}   | 0.1883            | 0.3472         | 0.2441     | 0.4293           |
| 0.0029        | 84.21  | 1600 | 5.2656          | {'precision': 0.13925822253324002, 'recall': 0.24357405140758873, 'f1': 0.17720391807658056, 'number': 817} | {'precision': 0.043726235741444866, 'recall': 0.19327731092436976, 'f1': 0.07131782945736433, 'number': 119}  | {'precision': 0.2663316582914573, 'recall': 0.4428969359331476, 'f1': 0.33263598326359833, 'number': 1077}  | 0.1866            | 0.3472         | 0.2428     | 0.4289           |
| 0.0013        | 94.74  | 1800 | 5.4292          | {'precision': 0.1434659090909091, 'recall': 0.24724602203182375, 'f1': 0.18157303370786518, 'number': 817}  | {'precision': 0.03333333333333333, 'recall': 0.13445378151260504, 'f1': 0.05342237061769616, 'number': 119}   | {'precision': 0.26543878656554715, 'recall': 0.45496750232126276, 'f1': 0.3352719808416011, 'number': 1077} | 0.1896            | 0.3517         | 0.2464     | 0.4291           |
| 0.0006        | 105.26 | 2000 | 5.4360          | {'precision': 0.14817572598659717, 'recall': 0.24357405140758873, 'f1': 0.18425925925925926, 'number': 817} | {'precision': 0.03456221198156682, 'recall': 0.12605042016806722, 'f1': 0.054249547920433995, 'number': 119}  | {'precision': 0.2717271727172717, 'recall': 0.4586815227483751, 'f1': 0.3412780656303972, 'number': 1077}   | 0.1969            | 0.3517         | 0.2525     | 0.4365           |
| 0.0005        | 115.79 | 2200 | 5.4540          | {'precision': 0.14905933429811866, 'recall': 0.2521419828641371, 'f1': 0.18735788994997726, 'number': 817}  | {'precision': 0.038817005545286505, 'recall': 0.17647058823529413, 'f1': 0.06363636363636363, 'number': 119}  | {'precision': 0.28058823529411764, 'recall': 0.4428969359331476, 'f1': 0.34353619013323733, 'number': 1077} | 0.1943            | 0.3497         | 0.2498     | 0.4337           |
| 0.0003        | 126.32 | 2400 | 5.4439          | {'precision': 0.14533622559652928, 'recall': 0.2460220318237454, 'f1': 0.18272727272727274, 'number': 817}  | {'precision': 0.033482142857142856, 'recall': 0.12605042016806722, 'f1': 0.05291005291005291, 'number': 119}  | {'precision': 0.27887323943661974, 'recall': 0.4596100278551532, 'f1': 0.34712482468443195, 'number': 1077} | 0.1972            | 0.3532         | 0.2531     | 0.4392           |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
