Text Classification
Transformers
TensorBoard
Safetensors
bert
glue
rte
max_length_128
dropout_0.4
Generated from Trainer
text-embeddings-inference
Instructions to use ipeksnmz/bert-base-uncased-finetuned-rte-run_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ipeksnmz/bert-base-uncased-finetuned-rte-run_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ipeksnmz/bert-base-uncased-finetuned-rte-run_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ipeksnmz/bert-base-uncased-finetuned-rte-run_3") model = AutoModelForSequenceClassification.from_pretrained("ipeksnmz/bert-base-uncased-finetuned-rte-run_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 125 Bytes
e9815ca | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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