File size: 9,183 Bytes
141f598 55a8b23 141f598 55a8b23 141f598 55a8b23 141f598 55a8b23 141f598 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
datasets:
- essays_su_g
metrics:
- accuracy
model-index:
- name: longformer-simple
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: essays_su_g
type: essays_su_g
config: simple
split: train[80%:100%]
args: simple
metrics:
- name: Accuracy
type: accuracy
value: 0.8472790470328397
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-simple
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.
It achieves the following results on the evaluation set:
- Loss: 0.4453
- Claim: {'precision': 0.6184998801821232, 'recall': 0.6192418426103646, 'f1-score': 0.6188706390121088, 'support': 4168.0}
- Majorclaim: {'precision': 0.7669435942282467, 'recall': 0.8150557620817844, 'f1-score': 0.7902680783960352, 'support': 2152.0}
- O: {'precision': 0.9382436260623229, 'recall': 0.897463689572946, 'f1-score': 0.9174006980222702, 'support': 9226.0}
- Premise: {'precision': 0.8744932706340198, 'recall': 0.8933984925039344, 'f1-score': 0.8838447986233458, 'support': 12073.0}
- Accuracy: 0.8473
- Macro avg: {'precision': 0.7995450927766782, 'recall': 0.8062899466922574, 'f1-score': 0.80259605351344, 'support': 27619.0}
- Weighted avg: {'precision': 0.8487766778592197, 'recall': 0.8472790470328397, 'f1-score': 0.8477753293690524, 'support': 27619.0}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 41 | 0.5604 | {'precision': 0.4942473633748802, 'recall': 0.2473608445297505, 'f1-score': 0.3297089862488008, 'support': 4168.0} | {'precision': 0.5518606492478226, 'recall': 0.6477695167286245, 'f1-score': 0.5959811885421119, 'support': 2152.0} | {'precision': 0.9073273343461493, 'recall': 0.8415347929763711, 'f1-score': 0.8731934994095485, 'support': 9226.0} | {'precision': 0.784083044982699, 'recall': 0.9384577155636544, 'f1-score': 0.8543528258492629, 'support': 12073.0} | 0.7791 | {'precision': 0.6843795979878877, 'recall': 0.6687807174496001, 'f1-score': 0.663309125012431, 'support': 27619.0} | {'precision': 0.7634187956291506, 'recall': 0.7791375502371556, 'f1-score': 0.761340507058846, 'support': 27619.0} |
| No log | 2.0 | 82 | 0.4518 | {'precision': 0.577831617201696, 'recall': 0.45777351247600767, 'f1-score': 0.510843373493976, 'support': 4168.0} | {'precision': 0.6824378508420208, 'recall': 0.7908921933085502, 'f1-score': 0.7326732673267327, 'support': 2152.0} | {'precision': 0.9419194900247905, 'recall': 0.8648384998916107, 'f1-score': 0.9017347573034978, 'support': 9226.0} | {'precision': 0.8292390653085681, 'recall': 0.917087716391949, 'f1-score': 0.8709537856440511, 'support': 12073.0} | 0.8205 | {'precision': 0.7578570058442688, 'recall': 0.7576479805170293, 'f1-score': 0.7540512959420644, 'support': 27619.0} | {'precision': 0.8175010277688459, 'recall': 0.8204858973894783, 'f1-score': 0.8161170924715856, 'support': 27619.0} |
| No log | 3.0 | 123 | 0.4276 | {'precision': 0.5879345603271984, 'recall': 0.5518234165067178, 'f1-score': 0.5693069306930693, 'support': 4168.0} | {'precision': 0.6929858183211959, 'recall': 0.8401486988847584, 'f1-score': 0.7595043058181055, 'support': 2152.0} | {'precision': 0.944093567251462, 'recall': 0.8749187079991328, 'f1-score': 0.9081908190819081, 'support': 9226.0} | {'precision': 0.8596589097864201, 'recall': 0.8934813219580883, 'f1-score': 0.8762438568701515, 'support': 12073.0} | 0.8316 | {'precision': 0.7711682139215691, 'recall': 0.7900930363371743, 'f1-score': 0.7783114781158085, 'support': 27619.0} | {'precision': 0.8338711031458205, 'recall': 0.831565226836598, 'f1-score': 0.8314995160611283, 'support': 27619.0} |
| No log | 4.0 | 164 | 0.4280 | {'precision': 0.6108695652173913, 'recall': 0.5393474088291746, 'f1-score': 0.5728848114169215, 'support': 4168.0} | {'precision': 0.803450078410873, 'recall': 0.7142193308550185, 'f1-score': 0.7562115621156212, 'support': 2152.0} | {'precision': 0.9037745879851143, 'recall': 0.921309343160633, 'f1-score': 0.9124577317374268, 'support': 9226.0} | {'precision': 0.8595990808969178, 'recall': 0.89861674811563, 'f1-score': 0.8786749817769499, 'support': 12073.0} | 0.8376 | {'precision': 0.7944233281275741, 'recall': 0.7683732077401141, 'f1-score': 0.7800572717617298, 'support': 27619.0} | {'precision': 0.832444801368096, 'recall': 0.8376117889858431, 'f1-score': 0.8342709462203975, 'support': 27619.0} |
| No log | 5.0 | 205 | 0.4388 | {'precision': 0.6131295414683037, 'recall': 0.5870921305182342, 'f1-score': 0.5998284103444048, 'support': 4168.0} | {'precision': 0.746058798466127, 'recall': 0.8136617100371747, 'f1-score': 0.7783951989330962, 'support': 2152.0} | {'precision': 0.935686543294494, 'recall': 0.8878170388033817, 'f1-score': 0.911123470522803, 'support': 9226.0} | {'precision': 0.8646922647082302, 'recall': 0.8972086473950137, 'f1-score': 0.8806504065040651, 'support': 12073.0} | 0.8408 | {'precision': 0.7898917869842887, 'recall': 0.796444881688451, 'f1-score': 0.7924993715760923, 'support': 27619.0} | {'precision': 0.841200486020365, 'recall': 0.8407617944168869, 'f1-score': 0.840483318700404, 'support': 27619.0} |
| No log | 6.0 | 246 | 0.4455 | {'precision': 0.61596495497688, 'recall': 0.6072456813819578, 'f1-score': 0.6115742418750755, 'support': 4168.0} | {'precision': 0.7737881508078994, 'recall': 0.8011152416356877, 'f1-score': 0.7872146118721461, 'support': 2152.0} | {'precision': 0.9405251141552512, 'recall': 0.8930197268588771, 'f1-score': 0.9161570110085622, 'support': 9226.0} | {'precision': 0.8682319118351701, 'recall': 0.9005218255611696, 'f1-score': 0.8840821305143322, 'support': 12073.0} | 0.8460 | {'precision': 0.7996275329438002, 'recall': 0.800475618859423, 'f1-score': 0.7997569988175289, 'support': 27619.0} | {'precision': 0.8469525546784674, 'recall': 0.8460118034686267, 'f1-score': 0.846124603720218, 'support': 27619.0} |
| No log | 7.0 | 287 | 0.4453 | {'precision': 0.6184998801821232, 'recall': 0.6192418426103646, 'f1-score': 0.6188706390121088, 'support': 4168.0} | {'precision': 0.7669435942282467, 'recall': 0.8150557620817844, 'f1-score': 0.7902680783960352, 'support': 2152.0} | {'precision': 0.9382436260623229, 'recall': 0.897463689572946, 'f1-score': 0.9174006980222702, 'support': 9226.0} | {'precision': 0.8744932706340198, 'recall': 0.8933984925039344, 'f1-score': 0.8838447986233458, 'support': 12073.0} | 0.8473 | {'precision': 0.7995450927766782, 'recall': 0.8062899466922574, 'f1-score': 0.80259605351344, 'support': 27619.0} | {'precision': 0.8487766778592197, 'recall': 0.8472790470328397, 'f1-score': 0.8477753293690524, 'support': 27619.0} |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|