
# Model Card for distilroberta-base-climate-commitment

## Model Description

This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.

Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our [climatebert/climate_commitments_actions](https://huggingface.co/climatebert/climate_commitments_actions) dataset.

*Note: This model is trained on paragraphs. It may not perform well on sentences.*

## Citation Information

```bibtex
@techreport{bingler2023cheaptalk,
    title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
    author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
    type={Working paper},
    institution={Available at SSRN 3998435},
    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_commitments_actions"
model_name = "climatebert/distilroberta-base-climate-commitment"

# 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(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)
```
{"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_commitments_actions"], "metrics": ["accuracy"]}
transformers pytorch safetensors roberta text-classification en dataset:climatebert/climate_commitments_actions license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Model Card for distilroberta-base-climate-commitment

## Model Description

This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.

Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.

*Note: This model is trained on paragraphs. It may not perform well on sentences.*





## How to Get Started With the Model

You can use the model with a pipeline for text classification:


# Model Card for distilroberta-base-climate-commitment ## Model Description

This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.

Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.

*Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model

You can use the model with a pipeline for text classification:
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for distilroberta-base-climate-commitment ## Model Description

This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.

Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.

*Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model

You can use the model with a pipeline for text classification:
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Model Card for distilroberta-base-climate-commitment## Model Description

This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.

Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.

*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model

You can use the model with a pipeline for text classification: