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--- |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: cardiffnlp/twitter-roberta-base-sentiment-latest |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: twitter-roberta-base-sentiment-latest-biden-stance-1 |
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results: [] |
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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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# twitter-roberta-base-sentiment-latest-biden-stance-1 |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4037 |
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- Accuracy: {'accuracy': 0.5688073394495413} |
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- Precision: {'precision': 0.5540838852097131} |
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- Recall: {'recall': 0.6640211640211641} |
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- F1 Score: {'f1': 0.6040914560770156} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:---------------------------------:|:-------------------:|:--------------------------:| |
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| 0.4339 | 1.0 | 3600 | 0.4173 | {'accuracy': 0.8925} | {'precision': 0.857630979498861} | {'recall': 0.94125} | {'f1': 0.8974970202622169} | |
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| 0.3848 | 2.0 | 7200 | 0.5757 | {'accuracy': 0.854375} | {'precision': 0.9341500765696784} | {'recall': 0.7625} | {'f1': 0.8396421197522368} | |
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| 0.4094 | 3.0 | 10800 | 0.3543 | {'accuracy': 0.904375} | {'precision': 0.8655367231638418} | {'recall': 0.9575} | {'f1': 0.9091988130563798} | |
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| 0.3937 | 4.0 | 14400 | 0.2576 | {'accuracy': 0.91125} | {'precision': 0.9092039800995025} | {'recall': 0.91375} | {'f1': 0.9114713216957606} | |
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| 0.3401 | 5.0 | 18000 | 0.2671 | {'accuracy': 0.91625} | {'precision': 0.9291237113402062} | {'recall': 0.90125} | {'f1': 0.9149746192893401} | |
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| 0.352 | 6.0 | 21600 | 0.2429 | {'accuracy': 0.91875} | {'precision': 0.9294871794871795} | {'recall': 0.90625} | {'f1': 0.9177215189873418} | |
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| 0.2883 | 7.0 | 25200 | 0.2857 | {'accuracy': 0.915625} | {'precision': 0.917189460476788} | {'recall': 0.91375} | {'f1': 0.915466499686913} | |
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| 0.2894 | 8.0 | 28800 | 0.2270 | {'accuracy': 0.92375} | {'precision': 0.9302030456852792} | {'recall': 0.91625} | {'f1': 0.9231738035264484} | |
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| 0.282 | 9.0 | 32400 | 0.2518 | {'accuracy': 0.92} | {'precision': 0.9189526184538653} | {'recall': 0.92125} | {'f1': 0.920099875156055} | |
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| 0.2485 | 10.0 | 36000 | 0.2351 | {'accuracy': 0.92375} | {'precision': 0.9269521410579346} | {'recall': 0.92} | {'f1': 0.9234629861982434} | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
