Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("rdpatilds/con-nlu")
model = AutoModelForSequenceClassification.from_pretrained("rdpatilds/con-nlu")Quick Links
con-nlu
This model is a fine-tuned version of bert-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 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rdpatilds/con-nlu")