---
language: en
license: apache-2.0
datasets: climatebert/environmental_claims
tags:
- Env Claims
---

# Model Card for environmental-claims

## Model Description



## Climate Performance Model Card

| environmental-claims                                                     |                |
|--------------------------------------------------------------------------|----------------|
| 1. Is the resulting model publicly available?                            | Yes            |
| 2. How much time does the training of the final model take?              | < 5 min        |
| 3. How much time did all experiments take (incl. hyperparameter search)? | 60 hours       |
| 4. What was the power of GPU and CPU?                                    | 0.3 kW         |
| 5. At which geo location were the computations performed?                | Switzerland    |
| 6. What was the energy mix at the geo location?                          | 89 gCO2eq/kWh  |
| 7. How much CO2eq was emitted to train the final model?                  | 2.2 g          |
| 8. How much CO2eq was emitted for all experiments?                       | 1.6 kg         |
| 9. What is the average CO2eq emission for the inference of one sample?   | 0.0067 mg      |
| 10. Which positive environmental impact can be expected from this work?  | This work can help detect and evaluate environmental claims and thus have a positive impact on the environment in the future. |
| 11. Comments                                                             | - |

## Citation Information


## 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/environmental_claims"



dataset = datasets.load_dataset(dataset_name, split="test")

model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_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)
```