Instructions to use ty1413/NetZeroTarget_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ty1413/NetZeroTarget_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ty1413/NetZeroTarget_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ty1413/NetZeroTarget_Classification") model = AutoModelForSequenceClassification.from_pretrained("ty1413/NetZeroTarget_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 730 Bytes
bd81b64 8c4da2f bd81b64 8c4da2f bd81b64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | Description: - trained on text classification of type of net zero target. Text is from company ESG reports, data is labelled by Net Zero Tracker. - text was truncated to 128 tokens before tokenization. - Problems: - keeps outputting the same label regardless of input - The text column is quite unstructured, varies in lenghth, some include/don't include URL, some include excerpts from ESG report, etc... - truncation might have resulted in loss of data - should try text generation task instead - too many labels makes model behave poorly. Moving Forward: - better text preprocessing, remove urls, etc... - change task to text generation. Might perform better (This means ClimateBert cannot be used as base model.) - |