Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use schematise/ICAT-text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schematise/ICAT-text-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="schematise/ICAT-text-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("schematise/ICAT-text-classifier") model = AutoModelForSequenceClassification.from_pretrained("schematise/ICAT-text-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 779 Bytes
6330029 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"data_path": "NonCompete-Test/autotrain-data",
"model": "nlpaueb/legal-bert-base-uncased",
"lr": 5e-05,
"epochs": 100,
"max_seq_length": 128,
"batch_size": 20,
"warmup_ratio": 0.1,
"gradient_accumulation": 1,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.0,
"max_grad_norm": 1.0,
"seed": 42,
"train_split": "train",
"valid_split": "validation",
"text_column": "autotrain_text",
"target_column": "autotrain_label",
"logging_steps": -1,
"project_name": "NonCompete-Test",
"auto_find_batch_size": false,
"mixed_precision": "fp16",
"save_total_limit": 1,
"push_to_hub": true,
"evaluation_strategy": "epoch",
"username": "sankalps",
"log": "tensorboard"
} |