End of training

README.md

@@ -0,0 +1,76 @@
+---
+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: 1.6214
+- Answer: {'precision': 0.8792899408284024, 'recall': 0.9094247246022031, 'f1': 0.8941034897713599, 'number': 817}
+- Header: {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}
+- Question: {'precision': 0.8960573476702509, 'recall': 0.9285051067780873, 'f1': 0.9119927040583674, 'number': 1077}
+- Overall Precision: 0.8749
+- Overall Recall: 0.8967
+- Overall F1: 0.8857
+- Overall Accuracy: 0.8129
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4022        | 10.5263  | 200  | 1.0717          | {'precision': 0.8156424581005587, 'recall': 0.8935128518971848, 'f1': 0.852803738317757, 'number': 817}  | {'precision': 0.452991452991453, 'recall': 0.44537815126050423, 'f1': 0.4491525423728814, 'number': 119}  | {'precision': 0.8516579406631762, 'recall': 0.9062209842154132, 'f1': 0.8780926675663517, 'number': 1077} | 0.8151            | 0.8738         | 0.8434     | 0.7966           |
+| 0.0484        | 21.0526  | 400  | 1.2460          | {'precision': 0.8537794299876085, 'recall': 0.8433292533659731, 'f1': 0.8485221674876847, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.5546218487394958, 'f1': 0.5499999999999999, 'number': 119}  | {'precision': 0.8713398402839396, 'recall': 0.9117920148560817, 'f1': 0.8911070780399274, 'number': 1077} | 0.8453            | 0.8629         | 0.8540     | 0.8008           |
+| 0.0143        | 31.5789  | 600  | 1.5585          | {'precision': 0.8566433566433567, 'recall': 0.8996328029375765, 'f1': 0.8776119402985075, 'number': 817} | {'precision': 0.5294117647058824, 'recall': 0.5294117647058824, 'f1': 0.5294117647058824, 'number': 119}  | {'precision': 0.8879310344827587, 'recall': 0.8607242339832869, 'f1': 0.8741159830268741, 'number': 1077} | 0.8535            | 0.8569         | 0.8552     | 0.7935           |
+| 0.0079        | 42.1053  | 800  | 1.5146          | {'precision': 0.8556338028169014, 'recall': 0.8922888616891065, 'f1': 0.8735769922109047, 'number': 817} | {'precision': 0.47761194029850745, 'recall': 0.5378151260504201, 'f1': 0.5059288537549407, 'number': 119} | {'precision': 0.8851540616246498, 'recall': 0.8802228412256268, 'f1': 0.88268156424581, 'number': 1077}   | 0.8464            | 0.8649         | 0.8555     | 0.7989           |
+| 0.0041        | 52.6316  | 1000 | 1.5279          | {'precision': 0.8536299765807962, 'recall': 0.8922888616891065, 'f1': 0.8725314183123878, 'number': 817} | {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119}                  | {'precision': 0.8764342453662842, 'recall': 0.9220055710306406, 'f1': 0.8986425339366516, 'number': 1077} | 0.8539            | 0.8852         | 0.8693     | 0.8063           |
+| 0.0031        | 63.1579  | 1200 | 1.5225          | {'precision': 0.8413793103448276, 'recall': 0.8959608323133414, 'f1': 0.8678126852400712, 'number': 817} | {'precision': 0.5794392523364486, 'recall': 0.5210084033613446, 'f1': 0.5486725663716815, 'number': 119}  | {'precision': 0.8873499538319483, 'recall': 0.8922934076137419, 'f1': 0.8898148148148148, 'number': 1077} | 0.8519            | 0.8718         | 0.8618     | 0.8014           |
+| 0.0016        | 73.6842  | 1400 | 1.6214          | {'precision': 0.8792899408284024, 'recall': 0.9094247246022031, 'f1': 0.8941034897713599, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}  | {'precision': 0.8960573476702509, 'recall': 0.9285051067780873, 'f1': 0.9119927040583674, 'number': 1077} | 0.8749            | 0.8967         | 0.8857     | 0.8129           |
+| 0.0011        | 84.2105  | 1600 | 1.7158          | {'precision': 0.8770186335403727, 'recall': 0.8641370869033048, 'f1': 0.8705302096177557, 'number': 817} | {'precision': 0.5419847328244275, 'recall': 0.5966386554621849, 'f1': 0.568, 'number': 119}               | {'precision': 0.9052044609665427, 'recall': 0.904363974001857, 'f1': 0.9047840222944729, 'number': 1077}  | 0.8703            | 0.8698         | 0.8701     | 0.8070           |
+| 0.001         | 94.7368  | 1800 | 1.6261          | {'precision': 0.8481735159817352, 'recall': 0.9094247246022031, 'f1': 0.8777318369757826, 'number': 817} | {'precision': 0.6017699115044248, 'recall': 0.5714285714285714, 'f1': 0.5862068965517241, 'number': 119}  | {'precision': 0.9050751879699248, 'recall': 0.8941504178272981, 'f1': 0.8995796356842597, 'number': 1077} | 0.8641            | 0.8813         | 0.8726     | 0.8128           |
+| 0.0004        | 105.2632 | 2000 | 1.6253          | {'precision': 0.8611435239206534, 'recall': 0.9033047735618115, 'f1': 0.8817204301075269, 'number': 817} | {'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119}               | {'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077}  | 0.8671            | 0.8912         | 0.8790     | 0.8194           |
+| 0.0001        | 115.7895 | 2200 | 1.6711          | {'precision': 0.8823529411764706, 'recall': 0.8812729498164015, 'f1': 0.8818126148193509, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119}  | {'precision': 0.8967391304347826, 'recall': 0.9192200557103064, 'f1': 0.9078404401650619, 'number': 1077} | 0.8760            | 0.8847         | 0.8804     | 0.8190           |
+| 0.0002        | 126.3158 | 2400 | 1.6682          | {'precision': 0.8756038647342995, 'recall': 0.8873929008567931, 'f1': 0.8814589665653496, 'number': 817} | {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}  | {'precision': 0.8997289972899729, 'recall': 0.924791086350975, 'f1': 0.9120879120879122, 'number': 1077}  | 0.8757            | 0.8892         | 0.8824     | 0.8179           |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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