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fnet-large-finetuned-qqp

Text Classification
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Add checkpoints

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  1. README.md +82 -0
  2. all_results.json +16 -0
  3. checkpoint-181924/config.json +36 -0
  4. checkpoint-181924/optimizer.pt +3 -0
  5. checkpoint-181924/pytorch_model.bin +3 -0
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  33. config.json +36 -0
  34. eval_results.json +11 -0
  35. pytorch_model.bin +3 -0
  36. runs/Oct07_18-47-52_patrick-general-gpu/1633632529.3936458/events.out.tfevents.1633632529.patrick-general-gpu.7674.1 +3 -0
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  40. tokenizer.json +0 -0
  41. tokenizer_config.json +1 -0
  42. train_results.json +8 -0
  43. trainer_state.json +76 -0
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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - glue
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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: fnet-large-finetuned-qqp
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: GLUE QQP
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+ type: glue
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+ args: qqp
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8943111550828593
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+ - name: F1
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+ type: f1
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+ value: 0.8556565212985171
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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-finetuned-qqp
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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 GLUE QQP dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5515
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+ - Accuracy: 0.8943
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+ - F1: 0.8557
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+ - Combined Score: 0.8750
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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: 4
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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: linear
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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
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+ | 0.4574 | 1.0 | 90962 | 0.4946 | 0.8694 | 0.8297 | 0.8496 |
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+ | 0.3387 | 2.0 | 181924 | 0.4745 | 0.8874 | 0.8437 | 0.8655 |
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+ | 0.2029 | 3.0 | 272886 | 0.5515 | 0.8943 | 0.8557 | 0.8750 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.11.0.dev0
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+ - Pytorch 1.9.0
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+ - Datasets 1.12.1
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+ - Tokenizers 0.10.3
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