# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("CodeChamp95/bert_sentiment_financial_model")
model = AutoModelForSequenceClassification.from_pretrained("CodeChamp95/bert_sentiment_financial_model", device_map="auto")Quick Links
bert_sentiment_financial_model
This model was trained from scratch 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 4.52.2
- TensorFlow 2.18.0
- Datasets 2.14.4
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CodeChamp95/bert_sentiment_financial_model")