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alex2awesome
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source-affiliation-model

Transformers
PyTorch
roberta
Generated from Trainer
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ model-index:
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+ - name: source-affiliation-model
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+ results: []
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # source-affiliation-model
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.3321
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+ - F1: 0.5348
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 5
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+ - eval_batch_size: 5
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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: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | No log | 0.12 | 100 | 1.4535 | 0.2435 |
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+ | No log | 0.25 | 200 | 1.3128 | 0.3899 |
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+ | No log | 0.37 | 300 | 1.2888 | 0.4413 |
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+ | No log | 0.49 | 400 | 1.1560 | 0.4614 |
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+ | 1.4848 | 0.62 | 500 | 1.0988 | 0.4477 |
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+ | 1.4848 | 0.74 | 600 | 1.1211 | 0.4583 |
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+ | 1.4848 | 0.86 | 700 | 1.1152 | 0.4693 |
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+ | 1.4848 | 0.99 | 800 | 1.0176 | 0.5018 |
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+ | 1.4848 | 1.11 | 900 | 1.0942 | 0.4774 |
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+ | 1.1019 | 1.23 | 1000 | 1.1785 | 0.5119 |
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+ | 1.1019 | 1.35 | 1100 | 1.0751 | 0.4797 |
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+ | 1.1019 | 1.48 | 1200 | 1.0759 | 0.5206 |
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+ | 1.1019 | 1.6 | 1300 | 1.0756 | 0.5231 |
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+ | 1.1019 | 1.72 | 1400 | 1.1329 | 0.4547 |
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+ | 0.9431 | 1.85 | 1500 | 1.0617 | 0.4852 |
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+ | 0.9431 | 1.97 | 1600 | 1.1046 | 0.5254 |
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+ | 0.9431 | 2.09 | 1700 | 1.2489 | 0.5069 |
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+ | 0.9431 | 2.22 | 1800 | 1.2113 | 0.5363 |
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+ | 0.9431 | 2.34 | 1900 | 1.1782 | 0.5546 |
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+ | 0.7589 | 2.46 | 2000 | 1.0453 | 0.5862 |
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+ | 0.7589 | 2.59 | 2100 | 1.0810 | 0.5223 |
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+ | 0.7589 | 2.71 | 2200 | 1.1470 | 0.5872 |
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+ | 0.7589 | 2.83 | 2300 | 1.1522 | 0.5553 |
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+ | 0.7589 | 2.96 | 2400 | 1.0712 | 0.6273 |
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+ | 0.6875 | 3.08 | 2500 | 1.3458 | 0.5768 |
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+ | 0.6875 | 3.2 | 2600 | 1.7052 | 0.5491 |
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+ | 0.6875 | 3.33 | 2700 | 1.5080 | 0.6582 |
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+ | 0.6875 | 3.45 | 2800 | 1.5851 | 0.5965 |
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+ | 0.6875 | 3.57 | 2900 | 1.4771 | 0.5691 |
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+ | 0.5391 | 3.69 | 3000 | 1.6717 | 0.5350 |
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+ | 0.5391 | 3.82 | 3100 | 1.5607 | 0.5448 |
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+ | 0.5391 | 3.94 | 3200 | 1.5464 | 0.6062 |
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+ | 0.5391 | 4.06 | 3300 | 1.7645 | 0.5755 |
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+ | 0.5391 | 4.19 | 3400 | 1.6715 | 0.5504 |
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+ | 0.4928 | 4.31 | 3500 | 1.7604 | 0.5626 |
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+ | 0.4928 | 4.43 | 3600 | 1.8984 | 0.5142 |
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+ | 0.4928 | 4.56 | 3700 | 1.8012 | 0.5763 |
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+ | 0.4928 | 4.68 | 3800 | 1.7107 | 0.5671 |
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+ | 0.4928 | 4.8 | 3900 | 1.7697 | 0.5598 |
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+ | 0.4233 | 4.93 | 4000 | 1.6296 | 0.6084 |
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+ | 0.4233 | 5.05 | 4100 | 2.0418 | 0.5343 |
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+ | 0.4233 | 5.17 | 4200 | 1.8203 | 0.5526 |
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+ | 0.4233 | 5.3 | 4300 | 1.9760 | 0.5292 |
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+ | 0.4233 | 5.42 | 4400 | 2.0136 | 0.5153 |
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+ | 0.2518 | 5.54 | 4500 | 2.0137 | 0.5121 |
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+ | 0.2518 | 5.67 | 4600 | 2.0053 | 0.5257 |
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+ | 0.2518 | 5.79 | 4700 | 1.9539 | 0.5423 |
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+ | 0.2518 | 5.91 | 4800 | 2.0159 | 0.5686 |
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+ | 0.2518 | 6.03 | 4900 | 2.0411 | 0.5817 |
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+ | 0.2234 | 6.16 | 5000 | 2.0025 | 0.5780 |
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+ | 0.2234 | 6.28 | 5100 | 2.1189 | 0.5413 |
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+ | 0.2234 | 6.4 | 5200 | 2.1936 | 0.5628 |
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+ | 0.2234 | 6.53 | 5300 | 2.1825 | 0.5210 |
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+ | 0.2234 | 6.65 | 5400 | 2.0767 | 0.5471 |
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+ | 0.1829 | 6.77 | 5500 | 1.9747 | 0.5587 |
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+ | 0.1829 | 6.9 | 5600 | 2.1182 | 0.5847 |
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+ | 0.1829 | 7.02 | 5700 | 2.1597 | 0.5437 |
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+ | 0.1829 | 7.14 | 5800 | 2.0307 | 0.5629 |
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+ | 0.1829 | 7.27 | 5900 | 2.0912 | 0.5450 |
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+ | 0.1226 | 7.39 | 6000 | 2.2383 | 0.5379 |
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+ | 0.1226 | 7.51 | 6100 | 2.2311 | 0.5834 |
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+ | 0.1226 | 7.64 | 6200 | 2.2456 | 0.5438 |
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+ | 0.1226 | 7.76 | 6300 | 2.2423 | 0.5860 |
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+ | 0.1226 | 7.88 | 6400 | 2.2922 | 0.5245 |
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+ | 0.0883 | 8.0 | 6500 | 2.3304 | 0.5650 |
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+ | 0.0883 | 8.13 | 6600 | 2.3929 | 0.5288 |
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+ | 0.0883 | 8.25 | 6700 | 2.3928 | 0.5344 |
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+ | 0.0883 | 8.37 | 6800 | 2.3854 | 0.5266 |
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+ | 0.0883 | 8.5 | 6900 | 2.4275 | 0.5339 |
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+ | 0.044 | 8.62 | 7000 | 2.3929 | 0.5380 |
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+ | 0.044 | 8.74 | 7100 | 2.3587 | 0.5339 |
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+ | 0.044 | 8.87 | 7200 | 2.3372 | 0.5423 |
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+ | 0.044 | 8.99 | 7300 | 2.3488 | 0.5424 |
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+ | 0.044 | 9.11 | 7400 | 2.3543 | 0.5818 |
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+ | 0.0558 | 9.24 | 7500 | 2.3397 | 0.5554 |
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+ | 0.0558 | 9.36 | 7600 | 2.3255 | 0.5394 |
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+ | 0.0558 | 9.48 | 7700 | 2.3184 | 0.5557 |
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+ | 0.0558 | 9.61 | 7800 | 2.3293 | 0.5669 |
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+ | 0.0558 | 9.73 | 7900 | 2.3358 | 0.5666 |
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+ | 0.0323 | 9.85 | 8000 | 2.3307 | 0.5344 |
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+ | 0.0323 | 9.98 | 8100 | 2.3321 | 0.5348 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3