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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Model Card for Model ID
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  <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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  - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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+ base_model: FacebookAI/roberta-large
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+ language: en
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+ license: apache-2.0
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+ model_name: climate-mitigation-classifier
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+ pipeline_tag: text-classification
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+ tags:
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+ - CRS
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+ - OECD CRS
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+ - text-classification
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+ - lora
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+ - transformers
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+ funded_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
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+ tasks:
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+ - text-classification
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+ shared_by: DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
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  ---
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  # Model Card for Model ID
 
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  <!-- Provide a longer summary of what this model is. -->
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+ ### Model description
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+ This model identifies the relevance of CRS projects to climate-change mitigation. It is trained on manually annotated CRS data using the standard Rio Marker classification. Labels 0, 1, and 2 indicate whether a project has no, significant, or primary focus on climate-change mitigation. (RIO Marker)
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+
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+ ### Evaluation metrics
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+
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+ | |precision|recall|f1-score|support|
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+ |--|--|--|--|--|
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+ |0|0.87|0.91|0.89|311|
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+ |1|0.53|0.60|0.57|65|
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+ |2|0.76|0.79|0.78|87|
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+ |3|0.52|0.25|0.34|51|
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+ |--|--|--|--|--|
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+ |accuracy| | |0.78|514|
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+ |macro|avg|0.67|0.64|0.64|514|
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+ |weighted|avg|0.77|0.78|0.77|514|
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+
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+
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+ ### Usage
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+
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+ ```python## How to Use
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+
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+ ```python
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+
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+ model = AutoModelForSequenceClassification.from_pretrained("namespace/my-model")
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+ tokenizer = AutoTokenizer.from_pretrained("namespace/my-model")
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+
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+ inputs = tokenizer("hello world", return_tensors="pt")
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+ outputs = model(**inputs)
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+ print(outputs)"
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+ ```
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+ or
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+
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+ ```python
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+ from transformers import TextClassificationPipeline
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+
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+ model = TextClassificationPipeline("namespace/my-model")
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+ outputs = model("Hello World!")
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+ print(outputs)"
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+ ```
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+ ```
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+
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+
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  - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
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+ - **Shared by [optional]:** DEval - Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit gGmbH
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  - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** en
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+ - **License:** apache-2.0
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+ - **Finetuned from model [optional]:** FacebookAI/roberta-large
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  ### Model Sources [optional]
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