Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Vivek2324/tweets-sentiment-using-bertmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vivek2324/tweets-sentiment-using-bertmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vivek2324/tweets-sentiment-using-bertmodel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vivek2324/tweets-sentiment-using-bertmodel") model = AutoModelForSequenceClassification.from_pretrained("Vivek2324/tweets-sentiment-using-bertmodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tweets-sentiment-using-bertmodel
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.44.2
- TensorFlow 2.20.0
- Datasets 4.0.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Vivek2324/tweets-sentiment-using-bertmodel
Base model
distilbert/distilbert-base-uncased