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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: Twitter/twhin-bert-large
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: financial-twhin-bert-large-3labels-tt
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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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+ # financial-twhin-bert-large-3labels-tt
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+
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+ This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4655
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+ - Accuracy: 0.8033
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+ - F1: 0.7707
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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: 1.6446264239848265e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.9671 | 0.08 | 100 | 0.9526 | 0.6772 | 0.5469 |
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+ | 0.8284 | 0.15 | 200 | 0.6263 | 0.7190 | 0.6712 |
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+ | 0.69 | 0.23 | 300 | 0.6105 | 0.7003 | 0.6022 |
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+ | 0.7725 | 0.3 | 400 | 0.5843 | 0.7774 | 0.7304 |
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+ | 0.7058 | 0.38 | 500 | 0.5746 | 0.7774 | 0.7422 |
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+ | 0.7028 | 0.46 | 600 | 0.6724 | 0.7435 | 0.7204 |
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+ | 0.7257 | 0.53 | 700 | 0.6853 | 0.6542 | 0.6738 |
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+ | 0.6422 | 0.61 | 800 | 0.5631 | 0.7702 | 0.7575 |
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+ | 0.5796 | 0.68 | 900 | 0.6995 | 0.6376 | 0.6359 |
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+ | 0.6303 | 0.76 | 1000 | 0.7513 | 0.6376 | 0.6475 |
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+ | 0.5102 | 0.83 | 1100 | 0.6074 | 0.7269 | 0.7073 |
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+ | 0.589 | 0.91 | 1200 | 0.5675 | 0.7752 | 0.7327 |
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+ | 0.5131 | 0.99 | 1300 | 0.6960 | 0.5901 | 0.5939 |
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+ | 0.4298 | 1.06 | 1400 | 0.5223 | 0.7767 | 0.7384 |
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+ | 0.4798 | 1.14 | 1500 | 0.4950 | 0.7968 | 0.7596 |
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+ | 0.4033 | 1.21 | 1600 | 0.5231 | 0.7277 | 0.7069 |
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+ | 0.3675 | 1.29 | 1700 | 0.4929 | 0.7702 | 0.7598 |
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+ | 0.3561 | 1.37 | 1800 | 0.4951 | 0.7788 | 0.7509 |
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+ | 0.3933 | 1.44 | 1900 | 0.4655 | 0.8033 | 0.7707 |
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+ | 0.3836 | 1.52 | 2000 | 0.4898 | 0.7875 | 0.7594 |
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+ | 0.3925 | 1.59 | 2100 | 0.5211 | 0.7687 | 0.7488 |
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+ | 0.3484 | 1.67 | 2200 | 0.4916 | 0.7860 | 0.7588 |
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+ | 0.4128 | 1.75 | 2300 | 0.4786 | 0.7968 | 0.7708 |
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+ | 0.3847 | 1.82 | 2400 | 0.4803 | 0.7932 | 0.7651 |
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+ | 0.3146 | 1.9 | 2500 | 0.4805 | 0.7932 | 0.7651 |
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+ | 0.4201 | 1.97 | 2600 | 0.4799 | 0.7932 | 0.7651 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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