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add model
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README.md
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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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model_index:
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- name: hackMIT-finetuned-sst2
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results:
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name: glue
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type: glue
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args: sst2
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metric:
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name: Accuracy
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type: accuracy
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value: 0.7935779816513762
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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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# hackMIT-finetuned-sst2
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 1.4316
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- Accuracy: 0.7936
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## Model description
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- lr_scheduler_type: linear
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.0956 | 1.0 | 8419 | 1.3969 | 0.7970 |
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| 0.0658 | 2.0 | 16838 | 1.4316 | 0.7936 |
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### Framework versions
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- Transformers 4.9.2
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- generated_from_trainer
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datasets:
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- glue
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model_index:
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- name: hackMIT-finetuned-sst2
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results:
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name: glue
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type: glue
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args: sst2
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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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# hackMIT-finetuned-sst2
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This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
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## Model description
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- lr_scheduler_type: linear
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- num_epochs: 2
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### Framework versions
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- Transformers 4.9.2
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"BertForSequenceClassification"
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],
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{
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"_name_or_path": "Blaine-Mason/hackMIT-finetuned-sst2",
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"architectures": [
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"BertForSequenceClassification"
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],
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pytorch_model.bin
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runs/Aug23_17-48-36_1fec14368006/events.out.tfevents.1629751666.1fec14368006.75.36
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