Text Classification
Transformers
Safetensors
xlm-roberta
naics
industry-classification
github
bge-m3
text-embeddings-inference
Instructions to use aquiro1994/naics-github-classifier-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aquiro1994/naics-github-classifier-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aquiro1994/naics-github-classifier-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aquiro1994/naics-github-classifier-multilingual") model = AutoModelForSequenceClassification.from_pretrained("aquiro1994/naics-github-classifier-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
BGE-M3 fine-tuned for NAICS classification, multilingual counterpart of the RoBERTa model
d0b38c1 verified Download training_config.json from aquiro1994/naics-github-classifier-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 665 Bytes
-
https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/training_config.json
- Command line
-
hf download hf://aquiro1994/naics-github-classifier-multilingual/training_config.json
-
curl -L -o training_config.json https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/training_config.json
665 Bytes
| { | |
| "model": "bge-m3", | |
| "model_id": "BAAI/bge-m3", | |
| "data": "data/raw/train_data_gpt_ab8_score_with_code.parquet", | |
| "max_seq_length": 512, | |
| "min_samples_per_class": 80, | |
| "test_size": 0.2, | |
| "val_size": 0.1, | |
| "seed": 42, | |
| "epochs": 8, | |
| "batch_size": 8, | |
| "gradient_accumulation_steps": 8, | |
| "effective_batch_size": 64, | |
| "learning_rate": 1.5e-05, | |
| "weight_decay": 0.02, | |
| "warmup_ratio": 0.15, | |
| "eval_steps": 100, | |
| "early_stopping_patience": 5, | |
| "optimizer_steps": 584, | |
| "num_classes": 19, | |
| "n_train": 4611, | |
| "n_validation": 659, | |
| "n_test": 1318, | |
| "val_f1": 0.850317445103508, | |
| "test_f1": 0.8638612353965833, | |
| "test_accuracy": 0.8694992412746586 | |
| } |