Hugging Face's logo

bathmaraj
/
lilt-en-funsd

Token Classification
Transformers
TensorBoard
Safetensors
lilt
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
bathmaraj commited on
Commit
a3b5d2d
•
1 Parent(s): 090cc10

End of training

Browse files
Files changed (4) hide show
  1. README.md +22 -22
  2. logs/events.out.tfevents.1715762042.da8236381df3.3467.0 +2 -2
  3. logs/events.out.tfevents.1715763458.da8236381df3.3467.1 +3 -0
  4. model.safetensors +1 -1
README.md CHANGED
@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  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.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 1.6214
19
- - Answer: {'precision': 0.8792899408284024, 'recall': 0.9094247246022031, 'f1': 0.8941034897713599, 'number': 817}
20
- - Header: {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}
21
- - Question: {'precision': 0.8960573476702509, 'recall': 0.9285051067780873, 'f1': 0.9119927040583674, 'number': 1077}
22
- - Overall Precision: 0.8749
23
- - Overall Recall: 0.8967
24
- - Overall F1: 0.8857
25
- - Overall Accuracy: 0.8129
26
 
27
  ## Model description
28
 
@@ -52,20 +52,20 @@ The following hyperparameters were used during training:
52
 
53
  ### Training results
54
 
55
- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
- |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
- | 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 |
58
- | 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 |
59
- | 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 |
60
- | 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 |
61
- | 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 |
62
- | 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 |
63
- | 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 |
64
- | 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 |
65
- | 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 |
66
- | 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 |
67
- | 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 |
68
- | 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 |
69
 
70
 
71
  ### Framework versions
 
15
 
16
  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.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 1.6209
19
+ - Answer: {'precision': 0.8577827547592385, 'recall': 0.9375764993880049, 'f1': 0.895906432748538, 'number': 817}
20
+ - Header: {'precision': 0.6464646464646465, 'recall': 0.5378151260504201, 'f1': 0.5871559633027523, 'number': 119}
21
+ - Question: {'precision': 0.9123951537744641, 'recall': 0.9090064995357474, 'f1': 0.9106976744186047, 'number': 1077}
22
+ - Overall Precision: 0.8760
23
+ - Overall Recall: 0.8987
24
+ - Overall F1: 0.8872
25
+ - Overall Accuracy: 0.8046
26
 
27
  ## Model description
28
 
 
52
 
53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
+ |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
+ | 0.4014 | 10.5263 | 200 | 0.9835 | {'precision': 0.835920177383592, 'recall': 0.9228886168910648, 'f1': 0.8772542175683536, 'number': 817} | {'precision': 0.6018518518518519, 'recall': 0.5462184873949579, 'f1': 0.5726872246696034, 'number': 119} | {'precision': 0.8773168578993822, 'recall': 0.9229340761374187, 'f1': 0.8995475113122172, 'number': 1077} | 0.8460 | 0.9006 | 0.8725 | 0.7938 |
58
+ | 0.0415 | 21.0526 | 400 | 1.3303 | {'precision': 0.8456299659477866, 'recall': 0.9118727050183598, 'f1': 0.877502944640754, 'number': 817} | {'precision': 0.5476190476190477, 'recall': 0.5798319327731093, 'f1': 0.5632653061224491, 'number': 119} | {'precision': 0.8757875787578758, 'recall': 0.903435468895079, 'f1': 0.8893967093235832, 'number': 1077} | 0.8437 | 0.8877 | 0.8652 | 0.7961 |
59
+ | 0.0116 | 31.5789 | 600 | 1.4921 | {'precision': 0.8661137440758294, 'recall': 0.8947368421052632, 'f1': 0.8801926550270921, 'number': 817} | {'precision': 0.5416666666666666, 'recall': 0.5462184873949579, 'f1': 0.5439330543933054, 'number': 119} | {'precision': 0.8771300448430494, 'recall': 0.9080779944289693, 'f1': 0.8923357664233577, 'number': 1077} | 0.8533 | 0.8813 | 0.8671 | 0.7981 |
60
+ | 0.0065 | 42.1053 | 800 | 1.3978 | {'precision': 0.8383167220376523, 'recall': 0.9265605875152999, 'f1': 0.8802325581395348, 'number': 817} | {'precision': 0.6, 'recall': 0.5294117647058824, 'f1': 0.5625, 'number': 119} | {'precision': 0.9031954887218046, 'recall': 0.8922934076137419, 'f1': 0.897711349836525, 'number': 1077} | 0.8596 | 0.8847 | 0.8720 | 0.8121 |
61
+ | 0.0046 | 52.6316 | 1000 | 1.4918 | {'precision': 0.8400447427293065, 'recall': 0.9192166462668299, 'f1': 0.8778492109877265, 'number': 817} | {'precision': 0.59375, 'recall': 0.4789915966386555, 'f1': 0.5302325581395348, 'number': 119} | {'precision': 0.9015009380863039, 'recall': 0.8922934076137419, 'f1': 0.8968735417638825, 'number': 1077} | 0.8604 | 0.8788 | 0.8695 | 0.8016 |
62
+ | 0.0028 | 63.1579 | 1200 | 1.5552 | {'precision': 0.8537142857142858, 'recall': 0.9143206854345165, 'f1': 0.8829787234042553, 'number': 817} | {'precision': 0.632183908045977, 'recall': 0.46218487394957986, 'f1': 0.5339805825242718, 'number': 119} | {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} | 0.8664 | 0.8862 | 0.8762 | 0.8015 |
63
+ | 0.0011 | 73.6842 | 1400 | 1.6209 | {'precision': 0.8577827547592385, 'recall': 0.9375764993880049, 'f1': 0.895906432748538, 'number': 817} | {'precision': 0.6464646464646465, 'recall': 0.5378151260504201, 'f1': 0.5871559633027523, 'number': 119} | {'precision': 0.9123951537744641, 'recall': 0.9090064995357474, 'f1': 0.9106976744186047, 'number': 1077} | 0.8760 | 0.8987 | 0.8872 | 0.8046 |
64
+ | 0.001 | 84.2105 | 1600 | 1.5894 | {'precision': 0.8468368479467259, 'recall': 0.9339045287637698, 'f1': 0.8882421420256112, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119} | {'precision': 0.9112149532710281, 'recall': 0.9052924791086351, 'f1': 0.9082440614811365, 'number': 1077} | 0.8716 | 0.8937 | 0.8825 | 0.8068 |
65
+ | 0.0006 | 94.7368 | 1800 | 1.6071 | {'precision': 0.8571428571428571, 'recall': 0.9033047735618115, 'f1': 0.8796185935637664, 'number': 817} | {'precision': 0.6530612244897959, 'recall': 0.5378151260504201, 'f1': 0.5898617511520737, 'number': 119} | {'precision': 0.8816621499548328, 'recall': 0.9062209842154132, 'f1': 0.8937728937728938, 'number': 1077} | 0.8606 | 0.8833 | 0.8718 | 0.8016 |
66
+ | 0.0006 | 105.2632 | 2000 | 1.6146 | {'precision': 0.8605990783410138, 'recall': 0.9143206854345165, 'f1': 0.886646884272997, 'number': 817} | {'precision': 0.5945945945945946, 'recall': 0.5546218487394958, 'f1': 0.5739130434782609, 'number': 119} | {'precision': 0.899260628465804, 'recall': 0.903435468895079, 'f1': 0.9013432144511349, 'number': 1077} | 0.8666 | 0.8872 | 0.8768 | 0.8005 |
67
+ | 0.0002 | 115.7895 | 2200 | 1.6401 | {'precision': 0.8394241417497231, 'recall': 0.9277845777233782, 'f1': 0.8813953488372093, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119} | {'precision': 0.8986988847583643, 'recall': 0.8978644382544104, 'f1': 0.8982814677194613, 'number': 1077} | 0.8596 | 0.8882 | 0.8737 | 0.8016 |
68
+ | 0.0002 | 126.3158 | 2400 | 1.6308 | {'precision': 0.8416945373467113, 'recall': 0.9241126070991432, 'f1': 0.8809801633605602, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.8970588235294118, 'recall': 0.9062209842154132, 'f1': 0.9016166281755196, 'number': 1077} | 0.8601 | 0.8917 | 0.8756 | 0.7994 |
69
 
70
 
71
  ### Framework versions
logs/events.out.tfevents.1715762042.da8236381df3.3467.0 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:f5b0571fdcd714a576145628c38444827ee813e462a78d5979106c360feb4526
3
- size 13646
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:95c22fb3055bec2f98a30125955260e5646fd1155b79bd828fbd95fab318a196
3
+ size 14000
logs/events.out.tfevents.1715763458.da8236381df3.3467.1 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d390247879204c471b3ac2093024956b37b0755f79671af04ef0934f5d6a8a35
3
+ size 592
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:290a223effd7959f70ed724844f5666fea031d2474371d4eed6efcbd065e79dc
3
  size 520727564
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c87d7aed98dbe74638b59dd60f7cb8ba2410e08d5fe61f2b353a62f7cf2008c6
3
  size 520727564