--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer datasets: - essays_su_g metrics: - accuracy model-index: - name: longformer-full_labels results: - task: name: Token Classification type: token-classification dataset: name: essays_su_g type: essays_su_g config: full_labels split: train[0%:20%] args: full_labels metrics: - name: Accuracy type: accuracy value: 0.8413786050317038 --- # longformer-full_labels 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: 1.3507 - B-claim: {'precision': 0.5516129032258065, 'recall': 0.602112676056338, 'f1-score': 0.5757575757575757, 'support': 284.0} - B-majorclaim: {'precision': 0.7669172932330827, 'recall': 0.723404255319149, 'f1-score': 0.7445255474452555, 'support': 141.0} - B-premise: {'precision': 0.78125, 'recall': 0.8121468926553672, 'f1-score': 0.796398891966759, 'support': 708.0} - I-claim: {'precision': 0.6005767844268205, 'recall': 0.6129506990434143, 'f1-score': 0.6067006554989075, 'support': 4077.0} - I-majorclaim: {'precision': 0.8202247191011236, 'recall': 0.7574110671936759, 'f1-score': 0.7875674287182123, 'support': 2024.0} - I-premise: {'precision': 0.8752274705277316, 'recall': 0.9043492478744277, 'f1-score': 0.8895500784045676, 'support': 12232.0} - O: {'precision': 0.9212523719165086, 'recall': 0.885589785164167, 'f1-score': 0.9030691329957631, 'support': 9868.0} - Accuracy: 0.8414 - Macro avg: {'precision': 0.7595802203472962, 'recall': 0.7568520890437913, 'f1-score': 0.757652758683863, 'support': 29334.0} - Weighted avg: {'precision': 0.8428208089588236, 'recall': 0.8413786050317038, 'f1-score': 0.8417659194402047, 'support': 29334.0} ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | B-claim | B-majorclaim | B-premise | I-claim | I-majorclaim | I-premise | O | Accuracy | Macro avg | Weighted avg | 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| No log | 1.0 | 81 | 0.6160 | {'precision': 0.5, 'recall': 0.0035211267605633804, 'f1-score': 0.006993006993006993, 'support': 284.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0} | {'precision': 0.5871459694989106, 'recall': 0.7612994350282486, 'f1-score': 0.6629766297662977, 'support': 708.0} | {'precision': 0.4543010752688172, 'recall': 0.4145204807456463, 'f1-score': 0.43350006412722847, 'support': 4077.0} | {'precision': 0.504, 'recall': 0.5291501976284585, 'f1-score': 0.5162689804772235, 'support': 2024.0} | {'precision': 0.8338601495644129, 'recall': 0.8842380640941792, 'f1-score': 0.8583105185890568, 'support': 12232.0} | {'precision': 0.8758074598874765, 'recall': 0.8518443453587353, 'f1-score': 0.86365971437378, 'support': 9868.0} | 0.7678 | {'precision': 0.5364449506028024, 'recall': 0.4920819499451187, 'f1-score': 0.4773869877609419, 'support': 29334.0} | {'precision': 0.7592631824475936, 'recall': 0.767812095179655, 'f1-score': 0.760384876614678, 'support': 29334.0} | | No log | 2.0 | 162 | 0.5309 | {'precision': 0.4292803970223325, 'recall': 0.6091549295774648, 'f1-score': 0.5036390101892285, 'support': 284.0} | {'precision': 0.5398230088495575, 'recall': 0.4326241134751773, 'f1-score': 0.4803149606299212, 'support': 141.0} | {'precision': 0.7637130801687764, 'recall': 0.7669491525423728, 'f1-score': 0.7653276955602537, 'support': 708.0} | {'precision': 0.48918217221981825, 'recall': 0.5545744419916605, 'f1-score': 0.5198298655017818, 'support': 4077.0} | {'precision': 0.5565157276183892, 'recall': 0.7954545454545454, 'f1-score': 0.654870856213138, 'support': 2024.0} | {'precision': 0.8733752620545073, 'recall': 0.8514551994767822, 'f1-score': 0.8622759448607029, 'support': 12232.0} | {'precision': 0.9375793238721588, 'recall': 0.8234698013781921, 'f1-score': 0.8768276234151606, 'support': 9868.0} | 0.7905 | {'precision': 0.6556384245436485, 'recall': 0.6905260262708851, 'f1-score': 0.6661551366243125, 'support': 29334.0} | {'precision': 0.811163907411967, 'recall': 0.790516124633531, 'f1-score': 0.7976174138140826, 'support': 29334.0} | | No log | 3.0 | 243 | 0.5676 | {'precision': 0.46946564885496184, 'recall': 0.43309859154929575, 'f1-score': 0.45054945054945056, 'support': 284.0} | {'precision': 0.7363636363636363, 'recall': 0.574468085106383, 'f1-score': 0.6454183266932271, 'support': 141.0} | {'precision': 0.680968096809681, 'recall': 0.8742937853107344, 'f1-score': 0.7656153370439085, 'support': 708.0} | {'precision': 0.5428742157155662, 'recall': 0.4456708363993132, 'f1-score': 0.4894935344827586, 'support': 4077.0} | {'precision': 0.8010204081632653, 'recall': 0.6205533596837944, 'f1-score': 0.6993318485523384, 'support': 2024.0} | {'precision': 0.8058998684301641, 'recall': 0.9514388489208633, 'f1-score': 0.872642747347505, 'support': 12232.0} | {'precision': 0.938254570541566, 'recall': 0.8269152817186867, 'f1-score': 0.8790735254511176, 'support': 9868.0} | 0.8077 | {'precision': 0.7106923492684059, 'recall': 0.675205541241296, 'f1-score': 0.6860178243029009, 'support': 29334.0} | {'precision': 0.8069239689738901, 'recall': 0.8077316424626713, 'f1-score': 0.8018337806950748, 'support': 29334.0} | | No log | 4.0 | 324 | 0.4962 | {'precision': 0.4985835694050991, 'recall': 0.6197183098591549, 'f1-score': 0.5525902668759811, 'support': 284.0} | {'precision': 0.6645569620253164, 'recall': 0.7446808510638298, 'f1-score': 0.7023411371237457, 'support': 141.0} | {'precision': 0.78125, 'recall': 0.7768361581920904, 'f1-score': 0.7790368271954674, 'support': 708.0} | {'precision': 0.5629032258064516, 'recall': 0.599215109148884, 'f1-score': 0.5804918617084472, 'support': 4077.0} | {'precision': 0.7616530514175877, 'recall': 0.783102766798419, 'f1-score': 0.7722289890377587, 'support': 2024.0} | {'precision': 0.880485223479606, 'recall': 0.8841563113145847, 'f1-score': 0.882316948806853, 'support': 12232.0} | {'precision': 0.9191715347849176, 'recall': 0.8769760843129307, 'f1-score': 0.8975781776694498, 'support': 9868.0} | 0.8293 | {'precision': 0.7240862238455684, 'recall': 0.7549550843842704, 'f1-score': 0.7380834583453861, 'support': 29334.0} | {'precision': 0.8340303897149666, 'recall': 0.8293447876184632, 'f1-score': 0.8313555252889799, 'support': 29334.0} | | No log | 5.0 | 405 | 0.5210 | {'precision': 0.5313432835820896, 'recall': 0.6267605633802817, 'f1-score': 0.5751211631663974, 'support': 284.0} | {'precision': 0.7202797202797203, 'recall': 0.7304964539007093, 'f1-score': 0.7253521126760565, 'support': 141.0} | {'precision': 0.7905308464849354, 'recall': 0.7782485875706214, 'f1-score': 0.784341637010676, 'support': 708.0} | {'precision': 0.5978571428571429, 'recall': 0.6158940397350994, 'f1-score': 0.6067415730337079, 'support': 4077.0} | {'precision': 0.7871878393051032, 'recall': 0.7164031620553359, 'f1-score': 0.7501293326435593, 'support': 2024.0} | {'precision': 0.889124063822859, 'recall': 0.8929038587311968, 'f1-score': 0.8910099526839615, 'support': 12232.0} | {'precision': 0.9011491914980169, 'recall': 0.8979529793271179, 'f1-score': 0.8995482462819145, 'support': 9868.0} | 0.8378 | {'precision': 0.7453531554042667, 'recall': 0.7512370921000519, 'f1-score': 0.7474634310708962, 'support': 29334.0} | {'precision': 0.8389989193759403, 'recall': 0.8377991409286153, 'f1-score': 0.8382234245346452, 'support': 29334.0} | | No log | 6.0 | 486 | 0.5965 | {'precision': 0.5095890410958904, 'recall': 0.6549295774647887, 'f1-score': 0.5731895223420647, 'support': 284.0} | {'precision': 0.7323943661971831, 'recall': 0.7375886524822695, 'f1-score': 0.734982332155477, 'support': 141.0} | {'precision': 0.7795163584637269, 'recall': 0.7740112994350282, 'f1-score': 0.7767540751240255, 'support': 708.0} | {'precision': 0.543696981653186, 'recall': 0.6759872455236694, 'f1-score': 0.6026678329324294, 'support': 4077.0} | {'precision': 0.8293785310734463, 'recall': 0.7252964426877471, 'f1-score': 0.7738534528202425, 'support': 2024.0} | {'precision': 0.8986964300928687, 'recall': 0.8623283191628516, 'f1-score': 0.8801368434227544, 'support': 12232.0} | {'precision': 0.9166317553414327, 'recall': 0.8869071747061208, 'f1-score': 0.9015245158632056, 'support': 9868.0} | 0.8305 | {'precision': 0.7442719234168191, 'recall': 0.7595783873517822, 'f1-score': 0.7490155106657428, 'support': 29334.0} | {'precision': 0.8431642500441561, 'recall': 0.8305038521851776, 'f1-score': 0.8352694536382972, 'support': 29334.0} | | 0.4098 | 7.0 | 567 | 0.7630 | {'precision': 0.5288135593220339, 'recall': 0.5492957746478874, 'f1-score': 0.538860103626943, 'support': 284.0} | {'precision': 0.7894736842105263, 'recall': 0.6382978723404256, 'f1-score': 0.7058823529411764, 'support': 141.0} | {'precision': 0.7407862407862408, 'recall': 0.8516949152542372, 'f1-score': 0.7923784494086726, 'support': 708.0} | {'precision': 0.5571654373024236, 'recall': 0.5187637969094923, 'f1-score': 0.537279309030865, 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