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glacierscopessegmentation
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glacier_segmentation_transformer

Transformers
PyTorch
Safetensors
segformer
Generated from Trainer
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aashraychegu commited on
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End of training

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  1. README.md +32 -32
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. training_args.bin +0 -0
README.md CHANGED
@@ -14,12 +14,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0282
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- - Mean Iou: 0.9476
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- - Mean Accuracy: 0.9713
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- - Overall Accuracy: 0.9770
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- - Per Category Iou: [0.9615266525679848, 0.9111778317508353, 0.9702425642603907]
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- - Per Category Accuracy: [0.982983320714055, 0.9432032649898906, 0.987791939813118]
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  ## Model description
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@@ -51,36 +51,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------:|:------------------------------------------------------------:|
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- | 0.0828 | 1.0 | 352 | 0.0592 | 0.9208 | 0.9565 | 0.9653 | [0.9469681453924418, 0.8581945798277895, 0.9570958935975151] | [0.9804346868205148, 0.9098953383424603, 0.979223227765012] |
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- | 0.0653 | 2.0 | 704 | 0.0545 | 0.9243 | 0.9585 | 0.9668 | [0.9491763751018332, 0.8652493086329979, 0.9583893801723153] | [0.9756058281282557, 0.9177942565930071, 0.9822091134529419] |
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- | 0.0615 | 3.0 | 1056 | 0.0520 | 0.9245 | 0.9576 | 0.9673 | [0.9488521139512287, 0.8629337582590615, 0.9615905436256834] | [0.9798485904989809, 0.9082150871992817, 0.9846862114785438] |
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- | 0.0568 | 4.0 | 1408 | 0.0497 | 0.9266 | 0.9586 | 0.9682 | [0.949606783690876, 0.8679034925410897, 0.962356135274943] | [0.9740655396584907, 0.9135029366787574, 0.9881750267882855] |
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- | 0.0553 | 5.0 | 1760 | 0.0448 | 0.9317 | 0.9624 | 0.9703 | [0.9518683104854363, 0.8791552266868088, 0.9640612798278586] | [0.9766581269222775, 0.9247480628978334, 0.9858310012185293] |
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- | 0.0531 | 6.0 | 2112 | 0.0440 | 0.9324 | 0.9633 | 0.9704 | [0.9519743922792153, 0.8820510654279622, 0.9633058622325834] | [0.978891790566398, 0.9277520457319947, 0.9833093882099169] |
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- | 0.0516 | 7.0 | 2464 | 0.0415 | 0.9342 | 0.9635 | 0.9713 | [0.9529858938673607, 0.8844842403407042, 0.9650597333255734] | [0.9767157615257003, 0.9266858712956193, 0.9871889860088424] |
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- | 0.0496 | 8.0 | 2816 | 0.0406 | 0.9359 | 0.9648 | 0.9719 | [0.9555651950006734, 0.8876507922287321, 0.964435754608489] | [0.9797262558665514, 0.9292800603938921, 0.9853461182114515] |
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- | 0.0493 | 9.0 | 3168 | 0.0393 | 0.9365 | 0.9648 | 0.9724 | [0.9556626924479936, 0.8881594759577742, 0.9657764420138888] | [0.9795360289173738, 0.927896526148365, 0.9869706285329666] |
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- | 0.0463 | 10.0 | 3520 | 0.0391 | 0.9371 | 0.9657 | 0.9724 | [0.9536840950074162, 0.8916567818261698, 0.9658607405632947] | [0.9765900676045612, 0.9343429939730185, 0.9862598369089146] |
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- | 0.046 | 11.0 | 3872 | 0.0379 | 0.9383 | 0.9669 | 0.9729 | [0.9544427772914603, 0.8947383943199397, 0.965840875611707] | [0.9764990841923821, 0.938799387304811, 0.9853293009539879] |
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- | 0.0452 | 12.0 | 4224 | 0.0358 | 0.9402 | 0.9671 | 0.9739 | [0.958365142157537, 0.8951548166027431, 0.9670090163868024] | [0.9826695002020712, 0.9327967021491497, 0.985958578753622] |
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- | 0.0452 | 13.0 | 4576 | 0.0353 | 0.9421 | 0.9697 | 0.9742 | [0.9566153552692265, 0.9041663510518988, 0.9654341965129734] | [0.9781511412717715, 0.947970829250211, 0.9829622282166149] |
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- | 0.0433 | 14.0 | 4928 | 0.0352 | 0.9405 | 0.9668 | 0.9742 | [0.9595360452886308, 0.8947929690736943, 0.9672666207041147] | [0.9845407781679891, 0.9292349314756656, 0.9867658421113708] |
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- | 0.0431 | 15.0 | 5280 | 0.0332 | 0.9429 | 0.9683 | 0.9751 | [0.9590021679605092, 0.9009428341327679, 0.9686646519537535] | [0.9808890258011358, 0.9357498662877368, 0.9883632103243261] |
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- | 0.0416 | 16.0 | 5632 | 0.0327 | 0.9434 | 0.9688 | 0.9753 | [0.9597417369530555, 0.901854332222541, 0.9686551773513613] | [0.9831348612466031, 0.9360674756485265, 0.9871674788190165] |
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- | 0.0414 | 17.0 | 5984 | 0.0319 | 0.9442 | 0.9695 | 0.9755 | [0.9600470959530699, 0.9043351787260081, 0.9681169153162615] | [0.982250790235736, 0.9399363046395433, 0.9864601258953718] |
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- | 0.0404 | 18.0 | 6336 | 0.0316 | 0.9446 | 0.9701 | 0.9755 | [0.9589776803135457, 0.9068715131060119, 0.9679329085470226] | [0.9802443827939673, 0.9437927758334433, 0.9862343091520727] |
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- | 0.0398 | 19.0 | 6688 | 0.0314 | 0.9446 | 0.9699 | 0.9757 | [0.9588814103780426, 0.905637545251067, 0.9692458875824727] | [0.98042751220199, 0.9420745529130178, 0.987227866073119] |
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- | 0.0386 | 20.0 | 7040 | 0.0303 | 0.9455 | 0.9698 | 0.9762 | [0.9601095729129755, 0.9062431533997868, 0.9700239091770606] | [0.9816721896884353, 0.9390483599830692, 0.9887402730113751] |
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- | 0.0389 | 21.0 | 7392 | 0.0293 | 0.9467 | 0.9709 | 0.9766 | [0.9608462864680724, 0.9092665750526754, 0.9699517538184947] | [0.9825323474452577, 0.9427535729381092, 0.9874417479885407] |
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- | 0.0381 | 22.0 | 7744 | 0.0289 | 0.9470 | 0.9711 | 0.9767 | [0.9613365594140901, 0.9099925770562975, 0.969649897217598] | [0.9831566934116577, 0.9431333306388411, 0.9870399712829142] |
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- | 0.0376 | 23.0 | 8096 | 0.0286 | 0.9473 | 0.9713 | 0.9769 | [0.9609977973725258, 0.9109702591251677, 0.970032992637466] | [0.9821836494233457, 0.9442533383545785, 0.98751595129339] |
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- | 0.0375 | 24.0 | 8448 | 0.0283 | 0.9472 | 0.9709 | 0.9769 | [0.9616875865568749, 0.9094524903163835, 0.9705179691807947] | [0.9833220629090078, 0.940861432490088, 0.9883972648331962] |
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- | 0.0363 | 25.0 | 8800 | 0.0282 | 0.9476 | 0.9713 | 0.9770 | [0.9615266525679848, 0.9111778317508353, 0.9702425642603907] | [0.982983320714055, 0.9432032649898906, 0.987791939813118] |
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  ### Framework versions
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- - Transformers 4.45.1
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  - Pytorch 2.4.1+cu121
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  - Datasets 3.0.1
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  - Tokenizers 0.20.0
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
16
  It achieves the following results on the evaluation set:
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+ - Loss: 0.0290
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+ - Mean Iou: 0.9471
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+ - Mean Accuracy: 0.9710
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+ - Overall Accuracy: 0.9768
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+ - Per Category Iou: [0.9608005440708357, 0.9099221006602166, 0.9704331381855318]
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+ - Per Category Accuracy: [0.9824206687592316, 0.9426348262866555, 0.987965611683444]
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------:|:------------------------------------------------------------:|
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+ | 0.0862 | 1.0 | 352 | 0.0576 | 0.9232 | 0.9592 | 0.9659 | [0.9447371839102795, 0.8673794852190165, 0.9574680953552409] | [0.968534005122562, 0.9283290191012513, 0.9806639382372657] |
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+ | 0.0656 | 2.0 | 704 | 0.0545 | 0.9238 | 0.9570 | 0.9669 | [0.9478803787782473, 0.8629377451136709, 0.9606714386330129] | [0.9722807746172916, 0.9111279737034862, 0.9876936131061734] |
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+ | 0.0618 | 3.0 | 1056 | 0.0534 | 0.9237 | 0.9564 | 0.9671 | [0.9508687060941174, 0.8596389521258784, 0.9607218269648252] | [0.9812787803688049, 0.9019564119732999, 0.9860254146647229] |
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+ | 0.0571 | 4.0 | 1408 | 0.0499 | 0.9275 | 0.9607 | 0.9682 | [0.9502329113612598, 0.8720582640084444, 0.9601695550481213] | [0.9753690914093931, 0.924181989944127, 0.9825173933819673] |
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+ | 0.0559 | 5.0 | 1760 | 0.0467 | 0.9299 | 0.9612 | 0.9695 | [0.9505140192865831, 0.8754764025227105, 0.9637205968987405] | [0.976237213827783, 0.9214627056007283, 0.9860167566645874] |
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+ | 0.0536 | 6.0 | 2112 | 0.0438 | 0.9326 | 0.9635 | 0.9704 | [0.9494054620742128, 0.8841375143227368, 0.9641436146464465] | [0.9716557991857033, 0.9326334654316629, 0.9860780801551877] |
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+ | 0.0523 | 7.0 | 2464 | 0.0430 | 0.9330 | 0.9628 | 0.9708 | [0.9528910425065712, 0.8812972916596699, 0.9647077920001537] | [0.9777607122771147, 0.9241165974328902, 0.9866203492096512] |
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+ | 0.0501 | 8.0 | 2816 | 0.0416 | 0.9343 | 0.9638 | 0.9713 | [0.9534610878112045, 0.8855375345037919, 0.9640451917096353] | [0.9785921522904953, 0.9270713187736989, 0.9856376115075716] |
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+ | 0.0495 | 9.0 | 3168 | 0.0397 | 0.9363 | 0.9648 | 0.9723 | [0.9545006031758507, 0.8881574910338527, 0.9661569365523052] | [0.97816237529845, 0.9291447035854092, 0.9871201770011974] |
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+ | 0.0464 | 10.0 | 3520 | 0.0387 | 0.9381 | 0.9667 | 0.9727 | [0.9535058676791107, 0.895511422338478, 0.9654228115593686] | [0.9749859398030879, 0.9391499873921522, 0.9858483740929799] |
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+ | 0.0469 | 11.0 | 3872 | 0.0375 | 0.9379 | 0.9655 | 0.9729 | [0.9549214957250483, 0.891720146026617, 0.9669602902499821] | [0.9773053391751129, 0.9304895573270372, 0.9885620293319832] |
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+ | 0.046 | 12.0 | 4224 | 0.0371 | 0.9390 | 0.9665 | 0.9733 | [0.9549188445906526, 0.894997463400181, 0.9672095088085364] | [0.9775963126700038, 0.9344942222656819, 0.9874873392060299] |
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+ | 0.0459 | 13.0 | 4576 | 0.0376 | 0.9393 | 0.9674 | 0.9732 | [0.9567727152562366, 0.8962463100368819, 0.9647967766370947] | [0.9818777935729291, 0.9374800577047236, 0.9829219394226549] |
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+ | 0.0442 | 14.0 | 4928 | 0.0375 | 0.9378 | 0.9654 | 0.9731 | [0.9582913046479526, 0.8885196793391749, 0.9665944535904781] | [0.9867372712504237, 0.9245328196189752, 0.9850417538509562] |
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+ | 0.0441 | 15.0 | 5280 | 0.0351 | 0.9408 | 0.9668 | 0.9743 | [0.9573946237991694, 0.8963420497950646, 0.9685835277021041] | [0.9790010413152688, 0.9318911491290942, 0.9896500148911915] |
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+ | 0.0424 | 16.0 | 5632 | 0.0333 | 0.9430 | 0.9691 | 0.9750 | [0.9579484854107692, 0.9029111339543006, 0.9680899513313485] | [0.9799520026370737, 0.941103807023036, 0.9863855244712643] |
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+ | 0.0422 | 17.0 | 5984 | 0.0325 | 0.9440 | 0.9693 | 0.9755 | [0.960526018065147, 0.9033327928010478, 0.9680751487957986] | [0.9829515905300688, 0.9384147284099078, 0.9865726973968797] |
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+ | 0.0412 | 18.0 | 6336 | 0.0329 | 0.9438 | 0.9695 | 0.9752 | [0.9574622081054572, 0.9055448607955255, 0.9682452665078278] | [0.9790884406297427, 0.9425620270828011, 0.9868754080312248] |
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+ | 0.0408 | 19.0 | 6688 | 0.0314 | 0.9450 | 0.9703 | 0.9758 | [0.9586636871431746, 0.9072926807452817, 0.9690543615214298] | [0.9803788121503738, 0.9439052138194869, 0.9866635429617163] |
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+ | 0.0391 | 20.0 | 7040 | 0.0314 | 0.9441 | 0.9690 | 0.9757 | [0.959128342970837, 0.902939786042012, 0.9702891936150936] | [0.980902051876667, 0.9368478835131284, 0.9892370339721507] |
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+ | 0.0397 | 21.0 | 7392 | 0.0304 | 0.9456 | 0.9701 | 0.9763 | [0.9598212480415552, 0.906699930083766, 0.9703717586568048] | [0.9813465827619545, 0.9404109518551305, 0.9885344016054268] |
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+ | 0.0392 | 22.0 | 7744 | 0.0297 | 0.9464 | 0.9707 | 0.9765 | [0.9607482194751374, 0.9084845650132211, 0.9700107510946303] | [0.9827931258804484, 0.9417580815076976, 0.9875174387719378] |
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+ | 0.0383 | 23.0 | 8096 | 0.0296 | 0.9464 | 0.9709 | 0.9764 | [0.9596652006117967, 0.9096179359866197, 0.9698528705786332] | [0.9807504984978905, 0.9444144788383055, 0.9875228155693943] |
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+ | 0.0379 | 24.0 | 8448 | 0.0292 | 0.9467 | 0.9706 | 0.9767 | [0.9608055853550872, 0.9088181850778698, 0.9704877713838735] | [0.9827165687826889, 0.9406717333169016, 0.9884114527535798] |
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+ | 0.0365 | 25.0 | 8800 | 0.0290 | 0.9471 | 0.9710 | 0.9768 | [0.9608005440708357, 0.9099221006602166, 0.9704331381855318] | [0.9824206687592316, 0.9426348262866555, 0.987965611683444] |
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  ### Framework versions
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+ - Transformers 4.45.2
84
  - Pytorch 2.4.1+cu121
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  - Datasets 3.0.1
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  - Tokenizers 0.20.0
config.json CHANGED
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  2
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  ],
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  "torch_dtype": "float32",
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- "transformers_version": "4.45.1"
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  }
 
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  ],
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  "torch_dtype": "float32",
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+ "transformers_version": "4.45.2"
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  }
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:b08bcfda208ddf0153a0b982996fc4eae21ae42b26394cac092ebf8fac9fb09d
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  size 94992836
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:fbe66c88671698e6cc75c7bd5673465ba55bb3c9463e71b1c84a22acf0a4f26f
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  size 94992836
training_args.bin CHANGED
Binary files a/training_args.bin and b/training_args.bin differ