|
--- |
|
license: apache-2.0 |
|
--- |
|
# Model Card for transition-physical |
|
|
|
## Model Description |
|
|
|
This is the fine-tuned ClimateBERT language model with a classification head for detecting sentences that are either related to emission net zero or reduction targets. |
|
We use the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as a starting point and fine-tuned it on our human-annotated dataset. |
|
|
|
## Citation Information |
|
|
|
```bibtex |
|
@article{schimanski2023detecting, |
|
title={ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets}, |
|
author={Tobias Schimanski and Julia Bingler and Camilla Hyslop and Mathias Kraus and Markus Leippold}, |
|
year={2023} |
|
} |
|
``` |
|
|
|
## How to Get Started With the Model |
|
You can use the model with a pipeline for text classification: |
|
|
|
```python |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
|
from transformers.pipelines.pt_utils import KeyDataset |
|
import datasets |
|
from tqdm.auto import tqdm |
|
|
|
dataset_name = "climatebert/climate_detection" |
|
tokenizer_name = “"climatebert/distilroberta-base-climate-f" |
|
model_name = "climatebert/netzero-reduction" |
|
|
|
# If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading |
|
dataset = datasets.load_dataset(dataset_name, split="test") |
|
|
|
model = AutoModelForSequenceClassification.from_pretrained(model_name) |
|
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512) |
|
|
|
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) |
|
|
|
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline |
|
for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): |
|
print(out) |
|
``` |
