Text Classification
Transformers
PyTorch
TensorBoard
ONNX
Safetensors
English
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use usvsnsp/code-vs-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usvsnsp/code-vs-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="usvsnsp/code-vs-nl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("usvsnsp/code-vs-nl") model = AutoModelForSequenceClassification.from_pretrained("usvsnsp/code-vs-nl") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "distilbert-base-uncased", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "id2label": { | |
| "0": "Natural Language", | |
| "1": "Code" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "Code": 1, | |
| "Natural Language": 0 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.25.1", | |
| "vocab_size": 30522 | |
| } | |