Instructions to use HuggingFaceFW/fineweb-edu-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceFW/fineweb-edu-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceFW/fineweb-edu-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceFW/fineweb-edu-classifier") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceFW/fineweb-edu-classifier", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 558 Bytes
172e214 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Educational value classifier
### 1. Finetune a model for educational value regression
* edit `train_edu_bert.slurm`
```bash
--base_model_name="Snowflake/snowflake-arctic-embed-m" \ # BERT-like base model
--dataset_name="HuggingFaceTB/LLM_juries_fineweb_430k_annotations" \ # Llama3-annotated eduational value dataset
--target_column="score"
```
* run the training script on a SLURM cluster:
```bash
sbatch train_edu_bert.slurm
```
### 2. Annotate a dataset with the educational scores predicted by the model
```bash
sbatch run_edu_bert.slurm
``` |