ClimateBERT: distilroberta-base-climate-f
| Property | Value |
|---|---|
| Base Architecture | DistilRoBERTa |
| Training Duration | 48 hours |
| CO2 Footprint | 15.79 kg |
| Paper | ClimateBERT Paper |
| Location | Germany |
What is distilroberta-base-climate-f?
ClimateBERT is a specialized language model built on DistilRoBERTa and further pre-trained on a massive corpus of climate-related text data. The model processes over 2M paragraphs of climate research abstracts, corporate news, and company reports. It represents the FULL-SELECT sample selection strategy variant, which is recommended as the primary choice for climate-related NLP tasks.
Implementation Details
The model was trained in Germany using GPU power of 0.7 kW, with a total training time of 48 hours for the final model. The environmental impact has been carefully documented, with the training process emitting 15.79 kg CO2eq. The inference footprint is remarkably low at 0.62 mg CO2eq per sample.
- Pre-trained on >2M climate-related paragraphs
- Based on DistilRoBERTa architecture
- Optimized for climate-domain text analysis
- Energy-efficient inference (0.62 mg CO2eq/sample)
Core Capabilities
- Climate research text analysis
- Corporate sustainability report processing
- Environmental news comprehension
- Climate-specific language understanding
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on climate-related text and its documented environmental impact. It's the only variant that receives regular updates and is recommended over other sample selection strategies.
Q: What are the recommended use cases?
The model is ideal for processing climate research papers, analyzing corporate sustainability reports, and understanding climate-related news. It serves as a building block tool for developing climate-focused NLP applications.
