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+ ---
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+ license: apache-2.0
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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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+ model-index:
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+ - name: fnet-large-Financial_Sentiment_Analysis_v3
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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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+ # fnet-large-Financial_Sentiment_Analysis_v3
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+
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+ This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4741
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+ - Accuracy: 0.8248
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+ - Weighted f1: 0.8194
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+ - Micro f1: 0.8248
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+ - Macro f1: 0.7369
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+ - Weighted recall: 0.8248
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+ - Micro recall: 0.8248
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+ - Macro recall: 0.7269
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+ - Weighted precision: 0.8163
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+ - Micro precision: 0.8248
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+ - Macro precision: 0.7515
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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: 2e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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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
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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 | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
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+ | 0.6757 | 1.0 | 134 | 0.5890 | 0.5855 | 0.4739 | 0.5855 | 0.3628 | 0.5855 | 0.5855 | 0.4298 | 0.5912 | 0.5855 | 0.5210 |
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+ | 0.4815 | 2.0 | 268 | 0.3994 | 0.7827 | 0.7789 | 0.7827 | 0.7156 | 0.7827 | 0.7827 | 0.7039 | 0.7878 | 0.7827 | 0.7388 |
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+ | 0.314 | 3.0 | 402 | 0.3560 | 0.7991 | 0.7977 | 0.7991 | 0.7368 | 0.7991 | 0.7991 | 0.7252 | 0.8101 | 0.7991 | 0.7612 |
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+ | 0.235 | 4.0 | 536 | 0.3278 | 0.8201 | 0.8217 | 0.8201 | 0.7549 | 0.8201 | 0.8201 | 0.7509 | 0.8274 | 0.8201 | 0.7631 |
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+ | 0.1986 | 5.0 | 670 | 0.3574 | 0.8618 | 0.8655 | 0.8618 | 0.8209 | 0.8618 | 0.8618 | 0.8401 | 0.8723 | 0.8618 | 0.8084 |
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+ | 0.1605 | 6.0 | 804 | 0.3886 | 0.7995 | 0.7803 | 0.7995 | 0.6588 | 0.7995 | 0.7995 | 0.6469 | 0.7781 | 0.7995 | 0.6987 |
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+ | 0.1436 | 7.0 | 938 | 0.4040 | 0.8230 | 0.8207 | 0.8230 | 0.7442 | 0.8230 | 0.8230 | 0.7336 | 0.8210 | 0.8230 | 0.7576 |
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+ | 0.1373 | 8.0 | 1072 | 0.4517 | 0.8169 | 0.8076 | 0.8169 | 0.7123 | 0.8169 | 0.8169 | 0.7020 | 0.8030 | 0.8169 | 0.7323 |
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+ | 0.1271 | 9.0 | 1206 | 0.4533 | 0.8070 | 0.7945 | 0.8070 | 0.6892 | 0.8070 | 0.8070 | 0.6768 | 0.7906 | 0.8070 | 0.7169 |
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+ | 0.1199 | 10.0 | 1340 | 0.4741 | 0.8248 | 0.8194 | 0.8248 | 0.7369 | 0.8248 | 0.8248 | 0.7269 | 0.8163 | 0.8248 | 0.7515 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.4
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+ - Pytorch 2.0.0
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3
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