Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Ibrahim-Alam/finetuning-bert-base-uncased-on-tweet_sentiment_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ibrahim-Alam/finetuning-bert-base-uncased-on-tweet_sentiment_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ibrahim-Alam/finetuning-bert-base-uncased-on-tweet_sentiment_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ibrahim-Alam/finetuning-bert-base-uncased-on-tweet_sentiment_binary") model = AutoModelForSequenceClassification.from_pretrained("Ibrahim-Alam/finetuning-bert-base-uncased-on-tweet_sentiment_binary") - Notebooks
- Google Colab
- Kaggle
finetuning-bert-base-uncased-on-tweet_sentiment_binary
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: 0.2384
- Accuracy: 0.9326
- F1: 0.9355
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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