Instructions to use Shreyagg2202/bert-base-uncased-CustomSentiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shreyagg2202/bert-base-uncased-CustomSentiments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shreyagg2202/bert-base-uncased-CustomSentiments")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shreyagg2202/bert-base-uncased-CustomSentiments") model = AutoModelForSequenceClassification.from_pretrained("Shreyagg2202/bert-base-uncased-CustomSentiments", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results1
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1141
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Shreyagg2202/bert-base-uncased-CustomSentiments
Base model
google-bert/bert-base-uncased