Text Classification
Transformers
PyTorch
Safetensors
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Jorgeutd/bert-base-uncased-finetuned-surveyclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jorgeutd/bert-base-uncased-finetuned-surveyclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jorgeutd/bert-base-uncased-finetuned-surveyclassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification") model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b41d1b1fd0e9ee57cfe3582ab64e78b5acd80a8bf11a74bee2061bf8838517a9
- Size of remote file:
- 3.12 kB
- SHA256:
- b9e26d73b9eddc45d7f6be81ebab878fcd08ca1ea78fb63efe5e027147b1095d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.