Instructions to use umairalipathan/finetuning-sentiment-model-surrender-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use umairalipathan/finetuning-sentiment-model-surrender-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="umairalipathan/finetuning-sentiment-model-surrender-final")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("umairalipathan/finetuning-sentiment-model-surrender-final") model = AutoModelForSequenceClassification.from_pretrained("umairalipathan/finetuning-sentiment-model-surrender-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finetuning-sentiment-model-surrender-final
This model is a fine-tuned version of umairalipathan/autotrain-sisu_surrender-2206370778 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2072
- eval_accuracy: 0.9556
- eval_f1: 0.9714
- eval_runtime: 8.4
- eval_samples_per_second: 5.357
- eval_steps_per_second: 0.357
- step: 0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cpu
- Datasets 2.6.1
- Tokenizers 0.13.2
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