Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_10_v2_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_10_v2_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_10_v2_wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v2_wnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v2_wnli") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 512, | |
| "dropout": 0.1, | |
| "finetuning_task": "wnli", | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "entailment": 1, | |
| "not_entailment": 0 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 8, | |
| "n_layers": 4, | |
| "n_topics": 10, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.50.2", | |
| "vocab_size": 30522 | |
| } | |