Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/bert_laxo_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/bert_laxo_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/bert_laxo_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/bert_laxo_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/bert_laxo_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8e37acc1ef3399b19af1e2aa72cdda8933a075f02cf0e0abc83df9e92526f37
- Size of remote file:
- 3.9 kB
- SHA256:
- 39bd17eac5389cbaa080e723eedff1bba0149c12eeeb00013bfae001dc8cf35d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.