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longformer-spans

Token Classification
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longformer
Generated from Trainer
Eval Results (legacy)
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trainer: training complete at 2024-10-26 20:58:24.147472.

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  1. README.md +23 -37
  2. meta_data/README_s42_e5.md +26 -24
  3. meta_data/meta_s42_e5_cvi0.json +1 -1
  4. model.safetensors +1 -1
README.md CHANGED
@@ -1,10 +1,11 @@
1
  ---
 
2
  license: apache-2.0
3
  base_model: allenai/longformer-base-4096
4
  tags:
5
  - generated_from_trainer
6
  datasets:
7
- - essays_su_g
8
  metrics:
9
  - accuracy
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  model-index:
@@ -14,15 +15,15 @@ model-index:
14
  name: Token Classification
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  type: token-classification
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  dataset:
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- name: essays_su_g
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- type: essays_su_g
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  config: spans
20
  split: train[0%:20%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
- value: 0.9275243744460353
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,15 +31,15 @@ should probably proofread and complete it, then remove this comment. -->
30
 
31
  # longformer-spans
32
 
33
- This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 0.5306
36
- - B: {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0}
37
- - I: {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0}
38
- - O: {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0}
39
- - Accuracy: 0.9275
40
- - Macro avg: {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0}
41
- - Weighted avg: {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0}
42
 
43
  ## Model description
44
 
@@ -63,37 +64,22 @@ The following hyperparameters were used during training:
63
  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
66
- - num_epochs: 20
67
 
68
  ### Training results
69
 
70
  | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
  |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
- | No log | 1.0 | 81 | 0.2620 | {'precision': 0.7461594732991953, 'recall': 0.9002647837599294, 'f1-score': 0.816, 'support': 1133.0} | {'precision': 0.9024103768767235, 'recall': 0.9638902525500463, 'f1-score': 0.9321376763813793, 'support': 18333.0} | {'precision': 0.931782945736434, 'recall': 0.7917511147142278, 'f1-score': 0.8560784528570645, 'support': 9868.0} | 0.9035 | {'precision': 0.860117598637451, 'recall': 0.8853020503414012, 'f1-score': 0.8680720430794812, 'support': 29334.0} | {'precision': 0.9062562975065145, 'recall': 0.9035249198881844, 'f1-score': 0.9020655278480035, 'support': 29334.0} |
73
- | No log | 2.0 | 162 | 0.2253 | {'precision': 0.8167072181670721, 'recall': 0.8887908208296558, 'f1-score': 0.8512256973795435, 'support': 1133.0} | {'precision': 0.9152551099212274, 'recall': 0.9696721758577429, 'f1-score': 0.9416781438711729, 'support': 18333.0} | {'precision': 0.9380041484212952, 'recall': 0.8248885285772193, 'f1-score': 0.8778173190984578, 'support': 9868.0} | 0.9178 | {'precision': 0.8899888255031981, 'recall': 0.8944505084215394, 'f1-score': 0.8902403867830581, 'support': 29334.0} | {'precision': 0.9191015935430046, 'recall': 0.9178427763005387, 'f1-score': 0.9167016237671239, 'support': 29334.0} |
74
- | No log | 3.0 | 243 | 0.2279 | {'precision': 0.8050117462803446, 'recall': 0.9073256840247131, 'f1-score': 0.8531120331950207, 'support': 1133.0} | {'precision': 0.9280963603037444, 'recall': 0.9666721213112965, 'f1-score': 0.9469915571230095, 'support': 18333.0} | {'precision': 0.9353938852934612, 'recall': 0.8495135792460479, 'f1-score': 0.8903876792352629, 'support': 9868.0} | 0.9250 | {'precision': 0.8895006639591835, 'recall': 0.9078371281940192, 'f1-score': 0.8968304231844311, 'support': 29334.0} | {'precision': 0.9257972230878861, 'recall': 0.9249676143724006, 'f1-score': 0.9243239165827936, 'support': 29334.0} |
75
- | No log | 4.0 | 324 | 0.2390 | {'precision': 0.8217179902755267, 'recall': 0.8949691085613416, 'f1-score': 0.8567807351077312, 'support': 1133.0} | {'precision': 0.9432635621180161, 'recall': 0.9512900234549719, 'f1-score': 0.9472597903427299, 'support': 18333.0} | {'precision': 0.9099989595255437, 'recall': 0.8862991487636805, 'f1-score': 0.8979927100980543, 'support': 9868.0} | 0.9273 | {'precision': 0.8916601706396955, 'recall': 0.910852760259998, 'f1-score': 0.9006777451828384, 'support': 29334.0} | {'precision': 0.9273787107073643, 'recall': 0.9272516533715143, 'f1-score': 0.9271915992526735, 'support': 29334.0} |
76
- | No log | 5.0 | 405 | 0.2539 | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9335059992600032, 'recall': 0.9633447880870561, 'f1-score': 0.948190701170407, 'support': 18333.0} | {'precision': 0.9265359193845487, 'recall': 0.8665383056343737, 'f1-score': 0.8955333298423835, 'support': 9868.0} | 0.9277 | {'precision': 0.9010707463497579, 'recall': 0.9041652089397999, 'f1-score': 0.9020549127380568, 'support': 29334.0} | {'precision': 0.9276721180179627, 'recall': 0.9276607349832958, 'f1-score': 0.9271646670996412, 'support': 29334.0} |
77
- | No log | 6.0 | 486 | 0.2930 | {'precision': 0.841927303465765, 'recall': 0.8790820829655781, 'f1-score': 0.8601036269430052, 'support': 1133.0} | {'precision': 0.9452679589509693, 'recall': 0.9495990836197021, 'f1-score': 0.9474285714285714, 'support': 18333.0} | {'precision': 0.9045613314156564, 'recall': 0.8922780705310093, 'f1-score': 0.8983777165595348, 'support': 9868.0} | 0.9276 | {'precision': 0.8972521979441302, 'recall': 0.9069864123720964, 'f1-score': 0.9019699716437038, 'support': 29334.0} | {'precision': 0.9275827485063247, 'recall': 0.9275925547146656, 'f1-score': 0.9275549436263691, 'support': 29334.0} |
78
- | 0.1621 | 7.0 | 567 | 0.3149 | {'precision': 0.8406639004149378, 'recall': 0.8940864960282436, 'f1-score': 0.8665526090675792, 'support': 1133.0} | {'precision': 0.9382959450098577, 'recall': 0.9605083728795069, 'f1-score': 0.9492722371967655, 'support': 18333.0} | {'precision': 0.9227729117709891, 'recall': 0.8754560194568302, 'f1-score': 0.8984919396775871, 'support': 9868.0} | 0.9293 | {'precision': 0.9005775857319281, 'recall': 0.9100169627881934, 'f1-score': 0.9047722619806439, 'support': 29334.0} | {'precision': 0.9293030221719495, 'recall': 0.9293311515647371, 'f1-score': 0.9289946986889036, 'support': 29334.0} |
79
- | 0.1621 | 8.0 | 648 | 0.3477 | {'precision': 0.8284552845528456, 'recall': 0.8993821712268314, 'f1-score': 0.8624629707998307, 'support': 1133.0} | {'precision': 0.9356556940449557, 'recall': 0.9581628756886489, 'f1-score': 0.9467755410030451, 'support': 18333.0} | {'precision': 0.9191854233654877, 'recall': 0.8690717470612079, 'f1-score': 0.8934263985831857, 'support': 9868.0} | 0.9259 | {'precision': 0.8944321339877629, 'recall': 0.9088722646588961, 'f1-score': 0.9008883034620205, 'support': 29334.0} | {'precision': 0.9259745494680297, 'recall': 0.9259221381332242, 'f1-score': 0.9255723133682386, 'support': 29334.0} |
80
- | 0.1621 | 9.0 | 729 | 0.3808 | {'precision': 0.8464135021097047, 'recall': 0.8852603706972639, 'f1-score': 0.8654012079378774, 'support': 1133.0} | {'precision': 0.9316216786166175, 'recall': 0.9638902525500463, 'f1-score': 0.9474813007694164, 'support': 18333.0} | {'precision': 0.9268053588933667, 'recall': 0.8622821240372922, 'f1-score': 0.8933802299333298, 'support': 9868.0} | 0.9267 | {'precision': 0.9016135132065629, 'recall': 0.9038109157615342, 'f1-score': 0.9020875795468745, 'support': 29334.0} | {'precision': 0.9267103706800465, 'recall': 0.9266721210881571, 'f1-score': 0.9261113508073029, 'support': 29334.0} |
81
- | 0.1621 | 10.0 | 810 | 0.4663 | {'precision': 0.8380872483221476, 'recall': 0.881729920564872, 'f1-score': 0.8593548387096774, 'support': 1133.0} | {'precision': 0.9158687080751703, 'recall': 0.9756177385043364, 'f1-score': 0.9447995351539802, 'support': 18333.0} | {'precision': 0.9469406710786021, 'recall': 0.8265099310903932, 'f1-score': 0.8826362209837131, 'support': 9868.0} | 0.9218 | {'precision': 0.9002988758253068, 'recall': 0.8946191967198672, 'f1-score': 0.8955968649491236, 'support': 29334.0} | {'precision': 0.9233171207368492, 'recall': 0.9218313220154087, 'f1-score': 0.9205874800198834, 'support': 29334.0} |
82
- | 0.1621 | 11.0 | 891 | 0.3998 | {'precision': 0.8421052631578947, 'recall': 0.8755516328331863, 'f1-score': 0.8585028126352229, 'support': 1133.0} | {'precision': 0.941814648890808, 'recall': 0.9517809414716631, 'f1-score': 0.9467715680954965, 'support': 18333.0} | {'precision': 0.906636203136359, 'recall': 0.8846777462505067, 'f1-score': 0.8955223880597014, 'support': 9868.0} | 0.9263 | {'precision': 0.8968520383950206, 'recall': 0.9040034401851186, 'f1-score': 0.9002655895968069, 'support': 29334.0} | {'precision': 0.9261293813943774, 'recall': 0.9262630394763756, 'f1-score': 0.9261219666592889, 'support': 29334.0} |
83
- | 0.1621 | 12.0 | 972 | 0.4524 | {'precision': 0.8503401360544217, 'recall': 0.8826125330979699, 'f1-score': 0.8661758336942399, 'support': 1133.0} | {'precision': 0.9383342231713828, 'recall': 0.9586537937053401, 'f1-score': 0.9483851819874267, 'support': 18333.0} | {'precision': 0.9182223165040305, 'recall': 0.8772800972841508, 'f1-score': 0.8972844112769486, 'support': 9868.0} | 0.9283 | {'precision': 0.9022988919099451, 'recall': 0.906182141362487, 'f1-score': 0.9039484756528716, 'support': 29334.0} | {'precision': 0.9281698543264606, 'recall': 0.9283425376695984, 'f1-score': 0.9280195449455239, 'support': 29334.0} |
84
- | 0.0212 | 13.0 | 1053 | 0.4537 | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9365968111768783, 'recall': 0.9580537827960508, 'f1-score': 0.94720379658092, 'support': 18333.0} | {'precision': 0.9167642362959021, 'recall': 0.8728212403729225, 'f1-score': 0.8942532315838654, 'support': 9868.0} | 0.9265 | {'precision': 0.8988437892925006, 'recall': 0.9044958520889811, 'f1-score': 0.9012992451220551, 'support': 29334.0} | {'precision': 0.926316588126141, 'recall': 0.9264675802822663, 'f1-score': 0.9261172500595471, 'support': 29334.0} |
85
- | 0.0212 | 14.0 | 1134 | 0.4902 | {'precision': 0.8573883161512027, 'recall': 0.880847308031774, 'f1-score': 0.8689595124074879, 'support': 1133.0} | {'precision': 0.9300970873786408, 'recall': 0.9667266677575956, 'f1-score': 0.9480582004921365, 'support': 18333.0} | {'precision': 0.9303346132748217, 'recall': 0.8593433319821646, 'f1-score': 0.8934309645472265, 'support': 9868.0} | 0.9273 | {'precision': 0.9059400056015551, 'recall': 0.9023057692571781, 'f1-score': 0.9034828924822836, 'support': 29334.0} | {'precision': 0.9273686789700647, 'recall': 0.9272857435058294, 'f1-score': 0.9266264019680934, 'support': 29334.0} |
86
- | 0.0212 | 15.0 | 1215 | 0.4631 | {'precision': 0.8514090520922288, 'recall': 0.8799646954986761, 'f1-score': 0.865451388888889, 'support': 1133.0} | {'precision': 0.943136407819419, 'recall': 0.9526536846124475, 'f1-score': 0.9478711568207105, 'support': 18333.0} | {'precision': 0.9084499740798341, 'recall': 0.8879205512768544, 'f1-score': 0.8980679546968688, 'support': 9868.0} | 0.9281 | {'precision': 0.9009984779971606, 'recall': 0.9068463104626593, 'f1-score': 0.9037968334688228, 'support': 29334.0} | {'precision': 0.9279249527781314, 'recall': 0.9280698165950774, 'f1-score': 0.9279338964530544, 'support': 29334.0} |
87
- | 0.0212 | 16.0 | 1296 | 0.4685 | {'precision': 0.8621291448516579, 'recall': 0.8720211827007943, 'f1-score': 0.8670469504168494, 'support': 1133.0} | {'precision': 0.9403208556149732, 'recall': 0.9591447117220313, 'f1-score': 0.949639510706667, 'support': 18333.0} | {'precision': 0.917685497470489, 'recall': 0.8823469801378192, 'f1-score': 0.8996693531721429, 'support': 9868.0} | 0.9299 | {'precision': 0.9067118326457067, 'recall': 0.9045042915202149, 'f1-score': 0.9054519380985532, 'support': 29334.0} | {'precision': 0.9296862022276206, 'recall': 0.9299447739824095, 'f1-score': 0.9296394123443894, 'support': 29334.0} |
88
- | 0.0212 | 17.0 | 1377 | 0.5305 | {'precision': 0.8462823725981621, 'recall': 0.8940864960282436, 'f1-score': 0.8695278969957082, 'support': 1133.0} | {'precision': 0.9287246847035429, 'recall': 0.9680357824687722, 'f1-score': 0.9479728646973986, 'support': 18333.0} | {'precision': 0.9344262295081968, 'recall': 0.8548844750709363, 'f1-score': 0.892887383573243, 'support': 9868.0} | 0.9271 | {'precision': 0.9031444289366339, 'recall': 0.9056689178559841, 'f1-score': 0.9034627150887832, 'support': 29334.0} | {'precision': 0.927458430681484, 'recall': 0.9271152928342538, 'f1-score': 0.9264121612086422, 'support': 29334.0} |
89
- | 0.0212 | 18.0 | 1458 | 0.5198 | {'precision': 0.847972972972973, 'recall': 0.8861429832303619, 'f1-score': 0.8666378938282262, 'support': 1133.0} | {'precision': 0.9337531086300862, 'recall': 0.9625811378388698, 'f1-score': 0.9479480017189514, 'support': 18333.0} | {'precision': 0.9244406010161064, 'recall': 0.866639643291447, 'f1-score': 0.8946074585490874, 'support': 9868.0} | 0.9274 | {'precision': 0.9020555608730553, 'recall': 0.9051212547868929, 'f1-score': 0.9030644513654217, 'support': 29334.0} | {'precision': 0.9273071851680879, 'recall': 0.9273539237744597, 'f1-score': 0.9268636343554685, 'support': 29334.0} |
90
- | 0.0055 | 19.0 | 1539 | 0.5277 | {'precision': 0.8447986577181208, 'recall': 0.8887908208296558, 'f1-score': 0.8662365591397849, 'support': 1133.0} | {'precision': 0.9328933474128828, 'recall': 0.9637811596574484, 'f1-score': 0.9480857457140558, 'support': 18333.0} | {'precision': 0.9267550532492936, 'recall': 0.8642075395216863, 'f1-score': 0.8943890928159414, 'support': 9868.0} | 0.9274 | {'precision': 0.9014823527934324, 'recall': 0.9055931733362635, 'f1-score': 0.9029037992232607, 'support': 29334.0} | {'precision': 0.9274258363257326, 'recall': 0.9273880139087748, 'f1-score': 0.9268607610823233, 'support': 29334.0} |
91
- | 0.0055 | 20.0 | 1620 | 0.5306 | {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0} | {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0} | {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0} | 0.9275 | {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0} | {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0} |
92
 
93
 
94
  ### Framework versions
95
 
96
- - Transformers 4.38.2
97
- - Pytorch 2.2.1+cu121
98
- - Datasets 2.18.0
99
- - Tokenizers 0.15.2
 
1
  ---
2
+ library_name: transformers
3
  license: apache-2.0
4
  base_model: allenai/longformer-base-4096
5
  tags:
6
  - generated_from_trainer
7
  datasets:
8
+ - stab-gurevych-essays
9
  metrics:
10
  - accuracy
11
  model-index:
 
15
  name: Token Classification
16
  type: token-classification
17
  dataset:
18
+ name: stab-gurevych-essays
19
+ type: stab-gurevych-essays
20
  config: spans
21
  split: train[0%:20%]
22
  args: spans
23
  metrics:
24
  - name: Accuracy
25
  type: accuracy
26
+ value: 0.9739926865110556
27
  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
32
  # longformer-spans
33
 
34
+ This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the stab-gurevych-essays dataset.
35
  It achieves the following results on the evaluation set:
36
+ - Loss: 0.0856
37
+ - B: {'precision': 0.8861301369863014, 'recall': 0.913503971756399, 'f1-score': 0.8996088657105606, 'support': 1133.0}
38
+ - I: {'precision': 0.9856182499448976, 'recall': 0.978661705969251, 'f1-score': 0.9821276595744681, 'support': 18277.0}
39
+ - O: {'precision': 0.963097033685269, 'recall': 0.9722870774540656, 'f1-score': 0.9676702364113963, 'support': 9851.0}
40
+ - Accuracy: 0.9740
41
+ - Macro avg: {'precision': 0.9449484735388226, 'recall': 0.9548175850599052, 'f1-score': 0.9498022538988083, 'support': 29261.0}
42
+ - Weighted avg: {'precision': 0.9741840360302777, 'recall': 0.9739926865110556, 'f1-score': 0.9740652601681857, 'support': 29261.0}
43
 
44
  ## Model description
45
 
 
64
  - seed: 42
65
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
66
  - lr_scheduler_type: linear
67
+ - num_epochs: 5
68
 
69
  ### Training results
70
 
71
  | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
72
  |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
73
+ | No log | 1.0 | 41 | 0.2075 | {'precision': 0.8258258258258259, 'recall': 0.7281553398058253, 'f1-score': 0.773921200750469, 'support': 1133.0} | {'precision': 0.9305934158104424, 'recall': 0.9712753734201456, 'f1-score': 0.9504992905522983, 'support': 18277.0} | {'precision': 0.9388199433921185, 'recall': 0.8754441173484926, 'f1-score': 0.9060251089982665, 'support': 9851.0} | 0.9296 | {'precision': 0.8984130616761289, 'recall': 0.8582916101914878, 'f1-score': 0.8768152001003445, 'support': 29261.0} | {'precision': 0.9293063047668868, 'recall': 0.9295991251153413, 'f1-score': 0.9286894365406706, 'support': 29261.0} |
74
+ | No log | 2.0 | 82 | 0.1039 | {'precision': 0.7817781043350478, 'recall': 0.93909973521624, 'f1-score': 0.8532477947072975, 'support': 1133.0} | {'precision': 0.9750846901977925, 'recall': 0.9764184494173004, 'f1-score': 0.9757511140271741, 'support': 18277.0} | {'precision': 0.9661387789122734, 'recall': 0.9413257537305857, 'f1-score': 0.9535708776800864, 'support': 9851.0} | 0.9632 | {'precision': 0.9076671911483712, 'recall': 0.9522813127880422, 'f1-score': 0.927523262138186, 'support': 29261.0} | {'precision': 0.9645880382085871, 'recall': 0.9631591538224941, 'f1-score': 0.9635405344487392, 'support': 29261.0} |
75
+ | No log | 3.0 | 123 | 0.0875 | {'precision': 0.8751054852320675, 'recall': 0.9152691968225949, 'f1-score': 0.8947368421052632, 'support': 1133.0} | {'precision': 0.9870288248337029, 'recall': 0.9742299064397877, 'f1-score': 0.9805876036016191, 'support': 18277.0} | {'precision': 0.9561578318055002, 'recall': 0.9741143031164349, 'f1-score': 0.9650525468899281, 'support': 9851.0} | 0.9719 | {'precision': 0.9394307139570902, 'recall': 0.9545378021262724, 'f1-score': 0.9467923308656035, 'support': 29261.0} | {'precision': 0.972302079469926, 'recall': 0.9719080004101022, 'f1-score': 0.9720333929990342, 'support': 29261.0} |
76
+ | No log | 4.0 | 164 | 0.0825 | {'precision': 0.8817021276595745, 'recall': 0.9143865842894969, 'f1-score': 0.8977469670710572, 'support': 1133.0} | {'precision': 0.9845409033393849, 'recall': 0.9791541281391913, 'f1-score': 0.9818401272837, 'support': 18277.0} | {'precision': 0.9638712281764052, 'recall': 0.9695462389605116, 'f1-score': 0.9667004048582996, 'support': 9851.0} | 0.9734 | {'precision': 0.9433714197251216, 'recall': 0.9543623171297333, 'f1-score': 0.9487624997376857, 'support': 29261.0} | {'precision': 0.9736002894548376, 'recall': 0.9734117084173474, 'f1-score': 0.9734870649777793, 'support': 29261.0} |
77
+ | No log | 5.0 | 205 | 0.0856 | {'precision': 0.8861301369863014, 'recall': 0.913503971756399, 'f1-score': 0.8996088657105606, 'support': 1133.0} | {'precision': 0.9856182499448976, 'recall': 0.978661705969251, 'f1-score': 0.9821276595744681, 'support': 18277.0} | {'precision': 0.963097033685269, 'recall': 0.9722870774540656, 'f1-score': 0.9676702364113963, 'support': 9851.0} | 0.9740 | {'precision': 0.9449484735388226, 'recall': 0.9548175850599052, 'f1-score': 0.9498022538988083, 'support': 29261.0} | {'precision': 0.9741840360302777, 'recall': 0.9739926865110556, 'f1-score': 0.9740652601681857, 'support': 29261.0} |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
78
 
79
 
80
  ### Framework versions
81
 
82
+ - Transformers 4.45.2
83
+ - Pytorch 2.5.0+cu124
84
+ - Datasets 2.19.1
85
+ - Tokenizers 0.20.1
meta_data/README_s42_e5.md CHANGED
@@ -1,9 +1,11 @@
1
  ---
 
 
2
  base_model: allenai/longformer-base-4096
3
  tags:
4
  - generated_from_trainer
5
  datasets:
6
- - essays_su_g
7
  metrics:
8
  - accuracy
9
  model-index:
@@ -13,15 +15,15 @@ model-index:
13
  name: Token Classification
14
  type: token-classification
15
  dataset:
16
- name: essays_su_g
17
- type: essays_su_g
18
  config: spans
19
- split: train[80%:100%]
20
  args: spans
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
- value: 0.935805061732865
25
  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,15 +31,15 @@ should probably proofread and complete it, then remove this comment. -->
29
 
30
  # longformer-spans
31
 
32
- This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
33
  It achieves the following results on the evaluation set:
34
- - Loss: 0.1821
35
- - B: {'precision': 0.8143972246313964, 'recall': 0.900287631831256, 'f1-score': 0.8551912568306012, 'support': 1043.0}
36
- - I: {'precision': 0.9392924896774913, 'recall': 0.9702593659942363, 'f1-score': 0.9545248355636199, 'support': 17350.0}
37
- - O: {'precision': 0.944873595505618, 'recall': 0.8750270973336224, 'f1-score': 0.9086100168823861, 'support': 9226.0}
38
- - Accuracy: 0.9358
39
- - Macro avg: {'precision': 0.8995211032715019, 'recall': 0.9151913650530382, 'f1-score': 0.9061087030922024, 'support': 27619.0}
40
- - Weighted avg: {'precision': 0.936440305345228, 'recall': 0.935805061732865, 'f1-score': 0.9354359822462803, 'support': 27619.0}
41
 
42
  ## Model description
43
 
@@ -66,18 +68,18 @@ The following hyperparameters were used during training:
66
 
67
  ### Training results
68
 
69
- | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
70
- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
71
- | No log | 1.0 | 41 | 0.3420 | {'precision': 0.7641196013289037, 'recall': 0.22051773729626079, 'f1-score': 0.3422619047619047, 'support': 1043.0} | {'precision': 0.8498853325356466, 'recall': 0.9825360230547551, 'f1-score': 0.9114093242087253, 'support': 17350.0} | {'precision': 0.9462809917355371, 'recall': 0.7446347279427704, 'f1-score': 0.8334344292126653, 'support': 9226.0} | 0.8743 | {'precision': 0.8534286418666958, 'recall': 0.6492294960979287, 'f1-score': 0.6957018860610984, 'support': 27619.0} | {'precision': 0.8788470144984097, 'recall': 0.8742894384300662, 'f1-score': 0.8638689664942287, 'support': 27619.0} |
72
- | No log | 2.0 | 82 | 0.2028 | {'precision': 0.7734241908006815, 'recall': 0.8705656759348035, 'f1-score': 0.8191249436175011, 'support': 1043.0} | {'precision': 0.9413330313154765, 'recall': 0.9580979827089338, 'f1-score': 0.9496415207518066, 'support': 17350.0} | {'precision': 0.9263601183701343, 'recall': 0.8821807934099285, 'f1-score': 0.9037308461025984, 'support': 9226.0} | 0.9294 | {'precision': 0.8803724468287641, 'recall': 0.903614817351222, 'f1-score': 0.8908324368239686, 'support': 27619.0} | {'precision': 0.9299905129226795, 'recall': 0.9294326369528223, 'f1-score': 0.9293764613990178, 'support': 27619.0} |
73
- | No log | 3.0 | 123 | 0.2004 | {'precision': 0.7942905121746432, 'recall': 0.9069990412272292, 'f1-score': 0.8469113697403761, 'support': 1043.0} | {'precision': 0.9219560115701577, 'recall': 0.9736599423631124, 'f1-score': 0.9471028508956354, 'support': 17350.0} | {'precision': 0.9505243676742752, 'recall': 0.835031432907002, 'f1-score': 0.8890427557555824, 'support': 9226.0} | 0.9248 | {'precision': 0.8889236304730254, 'recall': 0.9052301388324479, 'f1-score': 0.8943523254638647, 'support': 27619.0} | {'precision': 0.9266779977951141, 'recall': 0.9248343531626778, 'f1-score': 0.9239245260972333, 'support': 27619.0} |
74
- | No log | 4.0 | 164 | 0.1732 | {'precision': 0.8319928507596068, 'recall': 0.8926174496644296, 'f1-score': 0.8612395929694727, 'support': 1043.0} | {'precision': 0.9531670965892806, 'recall': 0.9583861671469741, 'f1-score': 0.9557695071130909, 'support': 17350.0} | {'precision': 0.9240198785201547, 'recall': 0.9068935616735313, 'f1-score': 0.9153766205349817, 'support': 9226.0} | 0.9387 | {'precision': 0.9030599419563474, 'recall': 0.9192990594949784, 'f1-score': 0.9107952402058485, 'support': 27619.0} | {'precision': 0.9388545953290572, 'recall': 0.9387016184510663, 'f1-score': 0.9387066347418453, 'support': 27619.0} |
75
- | No log | 5.0 | 205 | 0.1821 | {'precision': 0.8143972246313964, 'recall': 0.900287631831256, 'f1-score': 0.8551912568306012, 'support': 1043.0} | {'precision': 0.9392924896774913, 'recall': 0.9702593659942363, 'f1-score': 0.9545248355636199, 'support': 17350.0} | {'precision': 0.944873595505618, 'recall': 0.8750270973336224, 'f1-score': 0.9086100168823861, 'support': 9226.0} | 0.9358 | {'precision': 0.8995211032715019, 'recall': 0.9151913650530382, 'f1-score': 0.9061087030922024, 'support': 27619.0} | {'precision': 0.936440305345228, 'recall': 0.935805061732865, 'f1-score': 0.9354359822462803, 'support': 27619.0} |
76
 
77
 
78
  ### Framework versions
79
 
80
- - Transformers 4.37.2
81
- - Pytorch 2.2.0+cu121
82
- - Datasets 2.17.0
83
- - Tokenizers 0.15.2
 
1
  ---
2
+ library_name: transformers
3
+ license: apache-2.0
4
  base_model: allenai/longformer-base-4096
5
  tags:
6
  - generated_from_trainer
7
  datasets:
8
+ - stab-gurevych-essays
9
  metrics:
10
  - accuracy
11
  model-index:
 
15
  name: Token Classification
16
  type: token-classification
17
  dataset:
18
+ name: stab-gurevych-essays
19
+ type: stab-gurevych-essays
20
  config: spans
21
+ split: train[0%:20%]
22
  args: spans
23
  metrics:
24
  - name: Accuracy
25
  type: accuracy
26
+ value: 0.9739926865110556
27
  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
32
  # longformer-spans
33
 
34
+ This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the stab-gurevych-essays dataset.
35
  It achieves the following results on the evaluation set:
36
+ - Loss: 0.0856
37
+ - B: {'precision': 0.8861301369863014, 'recall': 0.913503971756399, 'f1-score': 0.8996088657105606, 'support': 1133.0}
38
+ - I: {'precision': 0.9856182499448976, 'recall': 0.978661705969251, 'f1-score': 0.9821276595744681, 'support': 18277.0}
39
+ - O: {'precision': 0.963097033685269, 'recall': 0.9722870774540656, 'f1-score': 0.9676702364113963, 'support': 9851.0}
40
+ - Accuracy: 0.9740
41
+ - Macro avg: {'precision': 0.9449484735388226, 'recall': 0.9548175850599052, 'f1-score': 0.9498022538988083, 'support': 29261.0}
42
+ - Weighted avg: {'precision': 0.9741840360302777, 'recall': 0.9739926865110556, 'f1-score': 0.9740652601681857, 'support': 29261.0}
43
 
44
  ## Model description
45
 
 
68
 
69
  ### Training results
70
 
71
+ | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
72
+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
73
+ | No log | 1.0 | 41 | 0.2075 | {'precision': 0.8258258258258259, 'recall': 0.7281553398058253, 'f1-score': 0.773921200750469, 'support': 1133.0} | {'precision': 0.9305934158104424, 'recall': 0.9712753734201456, 'f1-score': 0.9504992905522983, 'support': 18277.0} | {'precision': 0.9388199433921185, 'recall': 0.8754441173484926, 'f1-score': 0.9060251089982665, 'support': 9851.0} | 0.9296 | {'precision': 0.8984130616761289, 'recall': 0.8582916101914878, 'f1-score': 0.8768152001003445, 'support': 29261.0} | {'precision': 0.9293063047668868, 'recall': 0.9295991251153413, 'f1-score': 0.9286894365406706, 'support': 29261.0} |
74
+ | No log | 2.0 | 82 | 0.1039 | {'precision': 0.7817781043350478, 'recall': 0.93909973521624, 'f1-score': 0.8532477947072975, 'support': 1133.0} | {'precision': 0.9750846901977925, 'recall': 0.9764184494173004, 'f1-score': 0.9757511140271741, 'support': 18277.0} | {'precision': 0.9661387789122734, 'recall': 0.9413257537305857, 'f1-score': 0.9535708776800864, 'support': 9851.0} | 0.9632 | {'precision': 0.9076671911483712, 'recall': 0.9522813127880422, 'f1-score': 0.927523262138186, 'support': 29261.0} | {'precision': 0.9645880382085871, 'recall': 0.9631591538224941, 'f1-score': 0.9635405344487392, 'support': 29261.0} |
75
+ | No log | 3.0 | 123 | 0.0875 | {'precision': 0.8751054852320675, 'recall': 0.9152691968225949, 'f1-score': 0.8947368421052632, 'support': 1133.0} | {'precision': 0.9870288248337029, 'recall': 0.9742299064397877, 'f1-score': 0.9805876036016191, 'support': 18277.0} | {'precision': 0.9561578318055002, 'recall': 0.9741143031164349, 'f1-score': 0.9650525468899281, 'support': 9851.0} | 0.9719 | {'precision': 0.9394307139570902, 'recall': 0.9545378021262724, 'f1-score': 0.9467923308656035, 'support': 29261.0} | {'precision': 0.972302079469926, 'recall': 0.9719080004101022, 'f1-score': 0.9720333929990342, 'support': 29261.0} |
76
+ | No log | 4.0 | 164 | 0.0825 | {'precision': 0.8817021276595745, 'recall': 0.9143865842894969, 'f1-score': 0.8977469670710572, 'support': 1133.0} | {'precision': 0.9845409033393849, 'recall': 0.9791541281391913, 'f1-score': 0.9818401272837, 'support': 18277.0} | {'precision': 0.9638712281764052, 'recall': 0.9695462389605116, 'f1-score': 0.9667004048582996, 'support': 9851.0} | 0.9734 | {'precision': 0.9433714197251216, 'recall': 0.9543623171297333, 'f1-score': 0.9487624997376857, 'support': 29261.0} | {'precision': 0.9736002894548376, 'recall': 0.9734117084173474, 'f1-score': 0.9734870649777793, 'support': 29261.0} |
77
+ | No log | 5.0 | 205 | 0.0856 | {'precision': 0.8861301369863014, 'recall': 0.913503971756399, 'f1-score': 0.8996088657105606, 'support': 1133.0} | {'precision': 0.9856182499448976, 'recall': 0.978661705969251, 'f1-score': 0.9821276595744681, 'support': 18277.0} | {'precision': 0.963097033685269, 'recall': 0.9722870774540656, 'f1-score': 0.9676702364113963, 'support': 9851.0} | 0.9740 | {'precision': 0.9449484735388226, 'recall': 0.9548175850599052, 'f1-score': 0.9498022538988083, 'support': 29261.0} | {'precision': 0.9741840360302777, 'recall': 0.9739926865110556, 'f1-score': 0.9740652601681857, 'support': 29261.0} |
78
 
79
 
80
  ### Framework versions
81
 
82
+ - Transformers 4.45.2
83
+ - Pytorch 2.5.0+cu124
84
+ - Datasets 2.19.1
85
+ - Tokenizers 0.20.1
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