# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gunkaynar/bertweet-base-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("gunkaynar/bertweet-base-sentiment-analysis")Quick Links
bertweet-base-sentiment-analysis
This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5717
- Accuracy: 0.6746
- F1: 0.7127
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
Training results
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.11.0
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gunkaynar/bertweet-base-sentiment-analysis")