End of training

README.md

@@ -0,0 +1,77 @@
+---
+library_name: transformers
+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: 1.7187
+- Answer: {'precision': 0.8569767441860465, 'recall': 0.9020807833537332, 'f1': 0.8789505068574837, 'number': 817}
+- Header: {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119}
+- Question: {'precision': 0.8962693357597816, 'recall': 0.914577530176416, 'f1': 0.9053308823529412, 'number': 1077}
+- Overall Precision: 0.8671
+- Overall Recall: 0.8882
+- Overall F1: 0.8775
+- Overall Accuracy: 0.7998
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4061        | 10.5263  | 200  | 1.0439          | {'precision': 0.8062770562770563, 'recall': 0.9118727050183598, 'f1': 0.855829982768524, 'number': 817}  | {'precision': 0.6043956043956044, 'recall': 0.46218487394957986, 'f1': 0.5238095238095237, 'number': 119} | {'precision': 0.8777173913043478, 'recall': 0.8997214484679665, 'f1': 0.8885832187070151, 'number': 1077} | 0.8348            | 0.8788         | 0.8562     | 0.7914           |
+| 0.0454        | 21.0526  | 400  | 1.4836          | {'precision': 0.8243688254665203, 'recall': 0.9192166462668299, 'f1': 0.869212962962963, 'number': 817}  | {'precision': 0.48872180451127817, 'recall': 0.5462184873949579, 'f1': 0.5158730158730158, 'number': 119} | {'precision': 0.9097963142580019, 'recall': 0.8709377901578459, 'f1': 0.889943074003795, 'number': 1077}  | 0.8453            | 0.8713         | 0.8581     | 0.7934           |
+| 0.0145        | 31.5789  | 600  | 1.3593          | {'precision': 0.8672248803827751, 'recall': 0.8873929008567931, 'f1': 0.8771929824561404, 'number': 817} | {'precision': 0.6018518518518519, 'recall': 0.5462184873949579, 'f1': 0.5726872246696034, 'number': 119}  | {'precision': 0.8753339269813001, 'recall': 0.9127205199628597, 'f1': 0.8936363636363636, 'number': 1077} | 0.8578            | 0.8808         | 0.8691     | 0.8041           |
+| 0.0074        | 42.1053  | 800  | 1.5465          | {'precision': 0.8311111111111111, 'recall': 0.9155446756425949, 'f1': 0.8712871287128713, 'number': 817} | {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119}  | {'precision': 0.9031657355679702, 'recall': 0.9006499535747446, 'f1': 0.901906090190609, 'number': 1077}  | 0.8576            | 0.8857         | 0.8715     | 0.7981           |
+| 0.0045        | 52.6316  | 1000 | 1.4018          | {'precision': 0.8342922899884925, 'recall': 0.8873929008567931, 'f1': 0.860023724792408, 'number': 817}  | {'precision': 0.5803571428571429, 'recall': 0.5462184873949579, 'f1': 0.5627705627705628, 'number': 119}  | {'precision': 0.8855855855855855, 'recall': 0.9127205199628597, 'f1': 0.8989483310470964, 'number': 1077} | 0.8479            | 0.8808         | 0.8640     | 0.8064           |
+| 0.0031        | 63.1579  | 1200 | 1.6815          | {'precision': 0.8714810281517748, 'recall': 0.8714810281517748, 'f1': 0.8714810281517748, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119}  | {'precision': 0.8730569948186528, 'recall': 0.9387186629526463, 'f1': 0.9046979865771813, 'number': 1077} | 0.8593            | 0.8862         | 0.8726     | 0.7952           |
+| 0.0018        | 73.6842  | 1400 | 1.5823          | {'precision': 0.8553530751708428, 'recall': 0.9192166462668299, 'f1': 0.8861356932153391, 'number': 817} | {'precision': 0.5865384615384616, 'recall': 0.5126050420168067, 'f1': 0.5470852017937219, 'number': 119}  | {'precision': 0.8930530164533821, 'recall': 0.9071494893221913, 'f1': 0.9000460617227084, 'number': 1077} | 0.8618            | 0.8887         | 0.8750     | 0.8061           |
+| 0.0015        | 84.2105  | 1600 | 1.6540          | {'precision': 0.8509895227008148, 'recall': 0.8947368421052632, 'f1': 0.8723150357995225, 'number': 817} | {'precision': 0.6477272727272727, 'recall': 0.4789915966386555, 'f1': 0.5507246376811594, 'number': 119}  | {'precision': 0.8776408450704225, 'recall': 0.9257195914577531, 'f1': 0.9010393131495708, 'number': 1077} | 0.8569            | 0.8867         | 0.8716     | 0.8039           |
+| 0.0005        | 94.7368  | 1800 | 1.7397          | {'precision': 0.8578199052132701, 'recall': 0.8861689106487148, 'f1': 0.8717639975918122, 'number': 817} | {'precision': 0.5740740740740741, 'recall': 0.5210084033613446, 'f1': 0.5462555066079295, 'number': 119}  | {'precision': 0.8785971223021583, 'recall': 0.9071494893221913, 'f1': 0.8926450433988122, 'number': 1077} | 0.8542            | 0.8758         | 0.8649     | 0.7925           |
+| 0.0003        | 105.2632 | 2000 | 1.6680          | {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} | {'precision': 0.6122448979591837, 'recall': 0.5042016806722689, 'f1': 0.5529953917050692, 'number': 119}  | {'precision': 0.8774250440917107, 'recall': 0.9238625812441968, 'f1': 0.9000452284034374, 'number': 1077} | 0.8614            | 0.8862         | 0.8737     | 0.8011           |
+| 0.0002        | 115.7895 | 2200 | 1.6812          | {'precision': 0.8494252873563218, 'recall': 0.9045287637698899, 'f1': 0.8761114404267932, 'number': 817} | {'precision': 0.6704545454545454, 'recall': 0.4957983193277311, 'f1': 0.5700483091787439, 'number': 119}  | {'precision': 0.8914798206278027, 'recall': 0.9229340761374187, 'f1': 0.906934306569343, 'number': 1077}  | 0.8644            | 0.8902         | 0.8771     | 0.8051           |
+| 0.0004        | 126.3158 | 2400 | 1.7187          | {'precision': 0.8569767441860465, 'recall': 0.9020807833537332, 'f1': 0.8789505068574837, 'number': 817} | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119}  | {'precision': 0.8962693357597816, 'recall': 0.914577530176416, 'f1': 0.9053308823529412, 'number': 1077}  | 0.8671            | 0.8882         | 0.8775     | 0.7998           |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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