Theoreticallyhugo
commited on
Commit
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Parent(s):
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trainer: training complete at 2024-04-23 14:17:17.109666.
Browse files- README.md +30 -60
- meta_data/README_s42_e20.md +99 -0
- model.safetensors +1 -1
README.md
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name: essays_su_g
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type: essays_su_g
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config: spans
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split: train[
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args: spans
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- B: {'precision': 0.
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- I: {'precision': 0.
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- Accuracy: 0.
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- Macro avg: {'precision': 0.
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- Weighted avg: {'precision': 0.
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## Model description
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- 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
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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| No log | 1.0 | 81 | 0.
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| No log | 2.0 | 162 | 0.
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| No log | 3.0 | 243 | 0.
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| No log | 4.0 | 324 | 0.
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| No log | 5.0 | 405 | 0.
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| 0.0058 | 21.0 | 1701 | 0.3749 | {'precision': 0.868252516010979, 'recall': 0.909875359539789, 'f1-score': 0.8885767790262172, 'support': 1043.0} | {'precision': 0.9537903271531439, 'recall': 0.9695677233429395, 'f1-score': 0.9616143138880155, 'support': 17350.0} | {'precision': 0.9420632242096973, 'recall': 0.9076522870149577, 'f1-score': 0.9245376759591498, 'support': 9226.0} | 0.9466 | {'precision': 0.9213686891246068, 'recall': 0.9290317899658954, 'f1-score': 0.9249095896244609, 'support': 27619.0} | {'precision': 0.9466427045463329, 'recall': 0.9466309424671422, 'f1-score': 0.9464708542988716, 'support': 27619.0} |
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| 0.0058 | 22.0 | 1782 | 0.3899 | {'precision': 0.8754789272030651, 'recall': 0.8763183125599233, 'f1-score': 0.875898418782942, 'support': 1043.0} | {'precision': 0.9475306911822412, 'recall': 0.9742363112391931, 'f1-score': 0.9606979453806588, 'support': 17350.0} | {'precision': 0.94757326007326, 'recall': 0.897246910903967, 'f1-score': 0.9217236387930074, 'support': 9226.0} | 0.9448 | {'precision': 0.9235276261528554, 'recall': 0.9159338449010278, 'f1-score': 0.919440000985536, 'support': 27619.0} | {'precision': 0.9448239585256736, 'recall': 0.9448205945182664, 'f1-score': 0.9444764001104068, 'support': 27619.0} |
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| 0.0058 | 23.0 | 1863 | 0.4506 | {'precision': 0.8712121212121212, 'recall': 0.8820709491850431, 'f1-score': 0.8766079085278704, 'support': 1043.0} | {'precision': 0.9385663638378019, 'recall': 0.9765417867435159, 'f1-score': 0.9571775605897973, 'support': 17350.0} | {'precision': 0.9515920573375631, 'recall': 0.877845220030349, 'f1-score': 0.9132322264193494, 'support': 9226.0} | 0.9400 | {'precision': 0.9204568474624955, 'recall': 0.9121526519863027, 'f1-score': 0.9156725651790056, 'support': 27619.0} | {'precision': 0.9403739808105457, 'recall': 0.9400050689742568, 'f1-score': 0.939455202786939, 'support': 27619.0} |
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| 0.0058 | 24.0 | 1944 | 0.4239 | {'precision': 0.8859649122807017, 'recall': 0.8715244487056567, 'f1-score': 0.8786853552440792, 'support': 1043.0} | {'precision': 0.9575452599919023, 'recall': 0.954178674351585, 'f1-score': 0.9558590028580501, 'support': 17350.0} | {'precision': 0.9121883061049011, 'recall': 0.9199002818122697, 'f1-score': 0.9160280626011873, 'support': 9226.0} | 0.9396 | {'precision': 0.9185661594591683, 'recall': 0.9152011349565038, 'f1-score': 0.9168574735677723, 'support': 27619.0} | {'precision': 0.9396908279261412, 'recall': 0.9396067924255042, 'f1-score': 0.9396392856607877, 'support': 27619.0} |
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| 0.0026 | 25.0 | 2025 | 0.3982 | {'precision': 0.88124410933082, 'recall': 0.8964525407478428, 'f1-score': 0.8887832699619772, 'support': 1043.0} | {'precision': 0.9556860955857192, 'recall': 0.9658213256484149, 'f1-score': 0.9607269808508199, 'support': 17350.0} | {'precision': 0.9340647163120568, 'recall': 0.9136137004118795, 'f1-score': 0.9237260273972602, 'support': 9226.0} | 0.9458 | {'precision': 0.9236649737428652, 'recall': 0.9252958556027124, 'f1-score': 0.9244120927366858, 'support': 27619.0} | {'precision': 0.9456523566073828, 'recall': 0.9457619754516818, 'f1-score': 0.9456501103261954, 'support': 27619.0} |
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| 0.0026 | 26.0 | 2106 | 0.4067 | {'precision': 0.8740601503759399, 'recall': 0.8916586768935763, 'f1-score': 0.8827717133364974, 'support': 1043.0} | {'precision': 0.9489364103142809, 'recall': 0.9693371757925072, 'f1-score': 0.9590283123770422, 'support': 17350.0} | {'precision': 0.9393115942028986, 'recall': 0.8991979189247779, 'f1-score': 0.9188171447557869, 'support': 9226.0} | 0.9430 | {'precision': 0.9207693849643731, 'recall': 0.9200645905369539, 'f1-score': 0.9202057234897755, 'support': 27619.0} | {'precision': 0.942893668268613, 'recall': 0.9429740396104132, 'f1-score': 0.9427162132687112, 'support': 27619.0} |
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| 0.0026 | 27.0 | 2187 | 0.4415 | {'precision': 0.8786407766990292, 'recall': 0.8676893576222435, 'f1-score': 0.8731307284129282, 'support': 1043.0} | {'precision': 0.9424291543234028, 'recall': 0.9718155619596541, 'f1-score': 0.9568967963451661, 'support': 17350.0} | {'precision': 0.9418257070590941, 'recall': 0.8879254281378712, 'f1-score': 0.9140816781968311, 'support': 9226.0} | 0.9399 | {'precision': 0.920965212693842, 'recall': 0.9091434492399229, 'f1-score': 0.9147030676516418, 'support': 27619.0} | {'precision': 0.9398186802902107, 'recall': 0.9398602411383468, 'f1-score': 0.9394312730137688, 'support': 27619.0} |
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| 0.0026 | 28.0 | 2268 | 0.4068 | {'precision': 0.8871745419479267, 'recall': 0.8820709491850431, 'f1-score': 0.8846153846153846, 'support': 1043.0} | {'precision': 0.9559995446265938, 'recall': 0.9680115273775216, 'f1-score': 0.9619680394066098, 'support': 17350.0} | {'precision': 0.9367650321721767, 'recall': 0.9152395404292217, 'f1-score': 0.9258771929824562, 'support': 9226.0} | 0.9471 | {'precision': 0.9266463729155657, 'recall': 0.9217740056639289, 'f1-score': 0.9241535390014834, 'support': 27619.0} | {'precision': 0.9469752465094172, 'recall': 0.9471378398928274, 'f1-score': 0.9469909233612611, 'support': 27619.0} |
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| 0.0026 | 29.0 | 2349 | 0.4006 | {'precision': 0.8782527881040892, 'recall': 0.9060402684563759, 'f1-score': 0.8919301557338367, 'support': 1043.0} | {'precision': 0.9576391652925457, 'recall': 0.9707204610951009, 'f1-score': 0.964135443798838, 'support': 17350.0} | {'precision': 0.944059848146494, 'recall': 0.9164318231086062, 'f1-score': 0.9300406995930042, 'support': 9226.0} | 0.9501 | {'precision': 0.9266506005143763, 'recall': 0.9310641842200277, 'f1-score': 0.9287020997085597, 'support': 27619.0} | {'precision': 0.9501051209246456, 'recall': 0.9501430174879612, 'f1-score': 0.9500195009517102, 'support': 27619.0} |
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| 0.0026 | 30.0 | 2430 | 0.4661 | {'precision': 0.8726415094339622, 'recall': 0.8868648130393096, 'f1-score': 0.8796956728483118, 'support': 1043.0} | {'precision': 0.947067399349557, 'recall': 0.9734870317002882, 'f1-score': 0.9600954979536152, 'support': 17350.0} | {'precision': 0.9477363896848138, 'recall': 0.8962714068935617, 'f1-score': 0.9212857222438862, 'support': 9226.0} | 0.9444 | {'precision': 0.922481766156111, 'recall': 0.9188744172110531, 'f1-score': 0.9203589643486044, 'support': 27619.0} | {'precision': 0.9444802637418637, 'recall': 0.9444223179695137, 'f1-score': 0.9440950631702127, 'support': 27619.0} |
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| 0.0015 | 31.0 | 2511 | 0.4837 | {'precision': 0.8710900473933649, 'recall': 0.8811121764141898, 'f1-score': 0.8760724499523355, 'support': 1043.0} | {'precision': 0.9430224794124193, 'recall': 0.9768299711815562, 'f1-score': 0.9596285601041844, 'support': 17350.0} | {'precision': 0.953328677839851, 'recall': 0.8878170388033817, 'f1-score': 0.9194073408912335, 'support': 9226.0} | 0.9435 | {'precision': 0.9224804015485452, 'recall': 0.9152530621330426, 'f1-score': 0.9183694503159178, 'support': 27619.0} | {'precision': 0.9437487714612122, 'recall': 0.9434809370360984, 'f1-score': 0.943037445605214, 'support': 27619.0} |
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| 0.0015 | 32.0 | 2592 | 0.4702 | {'precision': 0.868421052631579, 'recall': 0.8859060402684564, 'f1-score': 0.8770764119601329, 'support': 1043.0} | {'precision': 0.9465066726477515, 'recall': 0.9729106628242075, 'f1-score': 0.9595270577535243, 'support': 17350.0} | {'precision': 0.9464510950579063, 'recall': 0.8946455668762194, 'f1-score': 0.919819468434836, 'support': 9226.0} | 0.9435 | {'precision': 0.9204596067790788, 'recall': 0.9178207566562945, 'f1-score': 0.9188076460494976, 'support': 27619.0} | {'precision': 0.9435392929265168, 'recall': 0.9434809370360984, 'f1-score': 0.943149265559139, 'support': 27619.0} |
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| 0.0015 | 33.0 | 2673 | 0.4380 | {'precision': 0.8738317757009346, 'recall': 0.8964525407478428, 'f1-score': 0.8849976336961666, 'support': 1043.0} | {'precision': 0.9569585569128896, 'recall': 0.9662247838616714, 'f1-score': 0.9615693472524951, 'support': 17350.0} | {'precision': 0.9359982283246595, 'recall': 0.9162150444396271, 'f1-score': 0.9260009859232076, 'support': 9226.0} | 0.9469 | {'precision': 0.9222628536461612, 'recall': 0.9262974563497138, 'f1-score': 0.9241893222906231, 'support': 27619.0} | {'precision': 0.946817667512148, 'recall': 0.9468843911799848, 'f1-score': 0.9467962563055651, 'support': 27619.0} |
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| 0.0015 | 34.0 | 2754 | 0.4419 | {'precision': 0.880838894184938, 'recall': 0.8859060402684564, 'f1-score': 0.8833652007648184, 'support': 1043.0} | {'precision': 0.9541201156921681, 'recall': 0.9696829971181556, 'f1-score': 0.9618386073235572, 'support': 17350.0} | {'precision': 0.9406959829920555, 'recall': 0.9112291350531108, 'f1-score': 0.9257281286131146, 'support': 9226.0} | 0.9470 | {'precision': 0.9252183309563873, 'recall': 0.9222727241465742, 'f1-score': 0.9236439789004968, 'support': 27619.0} | {'precision': 0.9468684642086502, 'recall': 0.9469930120569173, 'f1-score': 0.9468126092923719, 'support': 27619.0} |
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| 0.0015 | 35.0 | 2835 | 0.4607 | {'precision': 0.8798076923076923, 'recall': 0.8772770853307766, 'f1-score': 0.8785405664906385, 'support': 1043.0} | {'precision': 0.945586592178771, 'recall': 0.9755619596541787, 'f1-score': 0.9603404255319149, 'support': 17350.0} | {'precision': 0.9508007835004033, 'recall': 0.8944287882072404, 'f1-score': 0.9217537000837756, 'support': 9226.0} | 0.9447 | {'precision': 0.9253983559956221, 'recall': 0.9157559443973985, 'f1-score': 0.920211564035443, 'support': 27619.0} | {'precision': 0.9448443037746955, 'recall': 0.9447481806003114, 'f1-score': 0.9443616289800995, 'support': 27619.0} |
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| 0.0015 | 36.0 | 2916 | 0.4413 | {'precision': 0.8901960784313725, 'recall': 0.8705656759348035, 'f1-score': 0.8802714493456132, 'support': 1043.0} | {'precision': 0.9574089997133849, 'recall': 0.9626512968299712, 'f1-score': 0.9600229918091681, 'support': 17350.0} | {'precision': 0.9270264365304784, 'recall': 0.9197918924777801, 'f1-score': 0.9233949945593034, 'support': 9226.0} | 0.9449 | {'precision': 0.924877171558412, 'recall': 0.9176696217475183, 'f1-score': 0.9212298119046949, 'support': 27619.0} | {'precision': 0.9447216249053674, 'recall': 0.9448568014772439, 'f1-score': 0.944775851745562, 'support': 27619.0} |
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| 0.0015 | 37.0 | 2997 | 0.4391 | {'precision': 0.8872832369942196, 'recall': 0.8830297219558965, 'f1-score': 0.8851513695338779, 'support': 1043.0} | {'precision': 0.9576338928856915, 'recall': 0.966685878962536, 'f1-score': 0.9621385956860945, 'support': 17350.0} | {'precision': 0.9353700231609132, 'recall': 0.9192499458053327, 'f1-score': 0.9272399278412508, 'support': 9226.0} | 0.9477 | {'precision': 0.9267623843469414, 'recall': 0.9229885155745885, 'f1-score': 0.9248432976870743, 'support': 27619.0} | {'precision': 0.9475400373450994, 'recall': 0.9476809442774902, 'f1-score': 0.9475735214106575, 'support': 27619.0} |
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| 0.0012 | 38.0 | 3078 | 0.4364 | {'precision': 0.8781869688385269, 'recall': 0.8916586768935763, 'f1-score': 0.8848715509039011, 'support': 1043.0} | {'precision': 0.9536389977842168, 'recall': 0.9674351585014409, 'f1-score': 0.9604875396984349, 'support': 17350.0} | {'precision': 0.9374930237749749, 'recall': 0.9103620203771949, 'f1-score': 0.9237283475391805, 'support': 9226.0} | 0.9455 | {'precision': 0.9231063301325729, 'recall': 0.9231519519240706, 'f1-score': 0.9230291460471722, 'support': 27619.0} | {'precision': 0.9453961496579408, 'recall': 0.9455085267388392, 'f1-score': 0.9453527490407726, 'support': 27619.0} |
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| 0.0012 | 39.0 | 3159 | 0.4426 | {'precision': 0.8866666666666667, 'recall': 0.8926174496644296, 'f1-score': 0.8896321070234113, 'support': 1043.0} | {'precision': 0.9584571428571429, 'recall': 0.9667435158501441, 'f1-score': 0.9625824964131994, 'support': 17350.0} | {'precision': 0.9358253390671518, 'recall': 0.9199002818122697, 'f1-score': 0.9277944793659471, 'support': 9226.0} | 0.9483 | {'precision': 0.9269830495303205, 'recall': 0.9264204157756145, 'f1-score': 0.9266696942675193, 'support': 27619.0} | {'precision': 0.9481860074636412, 'recall': 0.9482964625801079, 'f1-score': 0.9482068310592221, 'support': 27619.0} |
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| 0.0012 | 40.0 | 3240 | 0.4584 | {'precision': 0.8852772466539197, 'recall': 0.887823585810163, 'f1-score': 0.8865485878410723, 'support': 1043.0} | {'precision': 0.9512016660100185, 'recall': 0.9740634005763689, 'f1-score': 0.9624967964233846, 'support': 17350.0} | {'precision': 0.9486713604360664, 'recall': 0.905484500325168, 'f1-score': 0.9265749778172139, 'support': 9226.0} | 0.9479 | {'precision': 0.9283834243666682, 'recall': 0.9224571622372332, 'f1-score': 0.925206787360557, 'support': 27619.0} | {'precision': 0.947866868638148, 'recall': 0.9478981860313552, 'f1-score': 0.9476291806512029, 'support': 27619.0} |
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| 0.0012 | 41.0 | 3321 | 0.4565 | {'precision': 0.8815165876777251, 'recall': 0.8916586768935763, 'f1-score': 0.886558627264061, 'support': 1043.0} | {'precision': 0.9584047537424294, 'recall': 0.9668011527377521, 'f1-score': 0.9625846436359462, 'support': 17350.0} | {'precision': 0.9363275215184286, 'recall': 0.9196835031432907, 'f1-score': 0.927930883639545, 'support': 9226.0} | 0.9482 | {'precision': 0.9254162876461943, 'recall': 0.9260477775915397, 'f1-score': 0.9256913848465174, 'support': 27619.0} | {'precision': 0.9481263619938463, 'recall': 0.9482240486621528, 'f1-score': 0.9481376786914271, 'support': 27619.0} |
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| 0.0012 | 42.0 | 3402 | 0.4558 | {'precision': 0.8779564806054873, 'recall': 0.8897411313518696, 'f1-score': 0.8838095238095238, 'support': 1043.0} | {'precision': 0.9507137237270991, 'recall': 0.9750432276657061, 'f1-score': 0.9627247894377418, 'support': 17350.0} | {'precision': 0.9507299270072993, 'recall': 0.9035334923043572, 'f1-score': 0.9265310659108592, 'support': 9226.0} | 0.9479 | {'precision': 0.9264667104466285, 'recall': 0.9227726171073108, 'f1-score': 0.9243551263860416, 'support': 27619.0} | {'precision': 0.9479715421451187, 'recall': 0.9479343929903328, 'f1-score': 0.947654297555007, 'support': 27619.0} |
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| 0.0012 | 43.0 | 3483 | 0.4610 | {'precision': 0.875234521575985, 'recall': 0.8945349952061361, 'f1-score': 0.8847795163584636, 'support': 1043.0} | {'precision': 0.9506872370266479, 'recall': 0.9767146974063401, 'f1-score': 0.9635252309879175, 'support': 17350.0} | {'precision': 0.9546287809349221, 'recall': 0.9030999349663993, 'f1-score': 0.9281497159407374, 'support': 9226.0} | 0.9490 | {'precision': 0.9268501798458516, 'recall': 0.9247832091929585, 'f1-score': 0.9254848210957062, 'support': 27619.0} | {'precision': 0.949154506003899, 'recall': 0.9490206017596582, 'f1-score': 0.9487344607868312, 'support': 27619.0} |
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| 0.0008 | 44.0 | 3564 | 0.4501 | {'precision': 0.8805687203791469, 'recall': 0.8906999041227229, 'f1-score': 0.8856053384175404, 'support': 1043.0} | {'precision': 0.9565341070717415, 'recall': 0.9690489913544669, 'f1-score': 0.9627508804077075, 'support': 17350.0} | {'precision': 0.9403582953154557, 'recall': 0.9159982657706481, 'f1-score': 0.9280184483610608, 'support': 9226.0} | 0.9484 | {'precision': 0.9258203742554482, 'recall': 0.9252490537492793, 'f1-score': 0.9254582223954363, 'support': 27619.0} | {'precision': 0.9482619054140469, 'recall': 0.9483688764980629, 'f1-score': 0.9482353578197027, 'support': 27619.0} |
|
116 |
-
| 0.0008 | 45.0 | 3645 | 0.4542 | {'precision': 0.8861480075901328, 'recall': 0.8954937679769894, 'f1-score': 0.8907963757749165, 'support': 1043.0} | {'precision': 0.9586123991069895, 'recall': 0.9651873198847263, 'f1-score': 0.9618886240271116, 'support': 17350.0} | {'precision': 0.9334872471416007, 'recall': 0.9203338391502276, 'f1-score': 0.9268638794891386, 'support': 9226.0} | 0.9476 | {'precision': 0.9260825512795744, 'recall': 0.9270049756706479, 'f1-score': 0.9265162930970557, 'support': 27619.0} | {'precision': 0.9474829225732714, 'recall': 0.9475723234005576, 'f1-score': 0.9475040515214316, 'support': 27619.0} |
|
117 |
-
| 0.0008 | 46.0 | 3726 | 0.4560 | {'precision': 0.882186616399623, 'recall': 0.8974113135186961, 'f1-score': 0.8897338403041826, 'support': 1043.0} | {'precision': 0.9587003775311749, 'recall': 0.9659942363112392, 'f1-score': 0.9623334864492421, 'support': 17350.0} | {'precision': 0.9353239312472454, 'recall': 0.9201170604812486, 'f1-score': 0.9276581794339416, 'support': 9226.0} | 0.9481 | {'precision': 0.9254036417260144, 'recall': 0.927840870103728, 'f1-score': 0.926575168729122, 'support': 27619.0} | {'precision': 0.9480021282724854, 'recall': 0.9480792208262429, 'f1-score': 0.9480087167815329, 'support': 27619.0} |
|
118 |
-
| 0.0008 | 47.0 | 3807 | 0.4668 | {'precision': 0.8813559322033898, 'recall': 0.8974113135186961, 'f1-score': 0.8893111638954868, 'support': 1043.0} | {'precision': 0.961000057607005, 'recall': 0.9614985590778098, 'f1-score': 0.9612492437120056, 'support': 17350.0} | {'precision': 0.9279191128506197, 'recall': 0.925102969867765, 'f1-score': 0.9265089014329135, 'support': 9226.0} | 0.9469 | {'precision': 0.9234250342203382, 'recall': 0.9280042808214236, 'f1-score': 0.9256897696801353, 'support': 27619.0} | {'precision': 0.9469418506075343, 'recall': 0.9469205981389623, 'f1-score': 0.9469277326103895, 'support': 27619.0} |
|
119 |
-
| 0.0008 | 48.0 | 3888 | 0.4643 | {'precision': 0.882186616399623, 'recall': 0.8974113135186961, 'f1-score': 0.8897338403041826, 'support': 1043.0} | {'precision': 0.9605293440736479, 'recall': 0.9621902017291066, 'f1-score': 0.961359055571552, 'support': 17350.0} | {'precision': 0.9289605578557419, 'recall': 0.9241274658573596, 'f1-score': 0.9265377091936535, 'support': 9226.0} | 0.9470 | {'precision': 0.9238921727763376, 'recall': 0.9279096603683875, 'f1-score': 0.9258768683564625, 'support': 27619.0} | {'precision': 0.9470254124826993, 'recall': 0.9470292190158949, 'f1-score': 0.947022300395537, 'support': 27619.0} |
|
120 |
-
| 0.0008 | 49.0 | 3969 | 0.4590 | {'precision': 0.8819641170915958, 'recall': 0.8954937679769894, 'f1-score': 0.8886774500475738, 'support': 1043.0} | {'precision': 0.958698516354471, 'recall': 0.9646109510086456, 'f1-score': 0.961645645990749, 'support': 17350.0} | {'precision': 0.9327694166758211, 'recall': 0.9203338391502276, 'f1-score': 0.9265099023405532, 'support': 9226.0} | 0.9472 | {'precision': 0.9244773500406293, 'recall': 0.9268128527119542, 'f1-score': 0.9256109994596254, 'support': 27619.0} | {'precision': 0.9471392328153709, 'recall': 0.9472102538107824, 'f1-score': 0.9471531517192171, 'support': 27619.0} |
|
121 |
-
| 0.0009 | 50.0 | 4050 | 0.4604 | {'precision': 0.8831908831908832, 'recall': 0.8916586768935763, 'f1-score': 0.8874045801526719, 'support': 1043.0} | {'precision': 0.9586151553364668, 'recall': 0.9639193083573487, 'f1-score': 0.9612599149327509, 'support': 17350.0} | {'precision': 0.93125, 'recall': 0.9205506178192066, 'f1-score': 0.9258693993241034, 'support': 9226.0} | 0.9467 | {'precision': 0.92435201284245, 'recall': 0.9253762010233771, 'f1-score': 0.924844631469842, 'support': 27619.0} | {'precision': 0.9466256394603638, 'recall': 0.9467033563850972, 'f1-score': 0.9466488134742982, 'support': 27619.0} |
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### Framework versions
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17 |
name: essays_su_g
|
18 |
type: essays_su_g
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19 |
config: spans
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20 |
+
split: train[0%:20%]
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21 |
args: spans
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metrics:
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23 |
- name: Accuracy
|
24 |
type: accuracy
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25 |
+
value: 0.9275243744460353
|
26 |
---
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27 |
|
28 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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 |
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63 |
- seed: 42
|
64 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
65 |
- 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} |
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### Framework versions
|
meta_data/README_s42_e20.md
ADDED
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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
|
10 |
+
model-index:
|
11 |
+
- name: longformer-spans
|
12 |
+
results:
|
13 |
+
- task:
|
14 |
+
name: Token Classification
|
15 |
+
type: token-classification
|
16 |
+
dataset:
|
17 |
+
name: essays_su_g
|
18 |
+
type: essays_su_g
|
19 |
+
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
|
29 |
+
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 |
+
|
45 |
+
More information needed
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46 |
+
|
47 |
+
## Intended uses & limitations
|
48 |
+
|
49 |
+
More information needed
|
50 |
+
|
51 |
+
## Training and evaluation data
|
52 |
+
|
53 |
+
More information needed
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54 |
+
|
55 |
+
## Training procedure
|
56 |
+
|
57 |
+
### Training hyperparameters
|
58 |
+
|
59 |
+
The following hyperparameters were used during training:
|
60 |
+
- learning_rate: 2e-05
|
61 |
+
- train_batch_size: 8
|
62 |
+
- eval_batch_size: 8
|
63 |
+
- seed: 42
|
64 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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65 |
+
- lr_scheduler_type: linear
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66 |
+
- num_epochs: 20
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67 |
+
|
68 |
+
### Training results
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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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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} |
|
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+
| 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
|
model.safetensors
CHANGED
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size 592318676
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