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license: unknown |
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viewer: false |
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**==========================================** |
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**_IN PROGRESS - NOT READY FOR LOADING OR USE_** |
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**==========================================** |
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# Dataset Card for climate-fever-nli-stsb |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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The CLIMATE-FEVER dataset modified to supply NLI-style (**cf-nli**) features or STSb-style (**cf-stsb**) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets. |
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There are two **cf-nli** datasets: one derived from only SUPPORTS and REFUTES evidence (**cf-nli**), and one that also derived data from NOT_ENOUGH_INFO evidence based on the annotator votes (**cf-nli-nei**). |
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The feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for `load_dataset` examples. |
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### Usage |
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Load the **cf-nli** dataset |
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```python |
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# if datasets not already in your environment |
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!pip install datasets |
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from datasets import load_dataset |
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# all splits... |
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dd = load_dataset('climate-fever-nli-stsb', 'cf-nli') |
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# ... or specific split (only 'train' is available) |
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ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli', split='train') |
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## ds_train can now be injected into SentenceBERT training scripts at the point |
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## where individual sentence pairs are aggregated into |
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## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts |
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## for further processing into training samples |
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``` |
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Load the **cf-nli-nei** dataset |
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```python |
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# if datasets not already in your environment |
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!pip install datasets |
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from datasets import load_dataset |
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# all splits... |
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dd = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei') |
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# ... or specific split (only 'train' is available) |
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ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei', split='train') |
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## ds_train can now be injected into SentenceBERT training scripts at the point |
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## where individual sentence pairs are aggregated into |
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## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts |
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## for further processing into training samples |
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``` |
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Load the **cf-stsb** dataset |
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```python |
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# if datasets not already in your environment |
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!pip install datasets |
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from datasets import load_dataset |
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# all splits... |
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dd = load_dataset('climate-fever-nli-stsb', 'cf-stsb') |
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# ... or specific split ('train', 'dev', 'test' available) |
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ds_dev = load_dataset('climate-fever-nli-stsb', 'cf-stsb', split='dev') |
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## ds_dev (or test) can now be injected into SentenceBERT training scripts at the point |
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## where individual sentence pairs are aggregated into |
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## a list of dev (or test) samples |
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``` |
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<!-- |
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### Supported Tasks and Leaderboards |
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[More Information Needed] |
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### Languages |
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[More Information Needed] |
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--> |
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## Dataset Structure |
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### Data Instances |
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[More Information Needed] |
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### Data Fields |
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[More Information Needed] |
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### Data Splits |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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SentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, _intrinsic_ STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets. |
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So, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other _labeled_ datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT. |
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This modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge _intrinsic_ model performance on STSb. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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see CLIMATE-FEVER |
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#### Who are the source language producers? |
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see CLIMATE-FEVER |
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### Annotations |
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--> |
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### Annotation process |
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#### **cf-nli** |
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For each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset: |
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| split | dataset | sentence1 | sentence2 | label | |
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| {'train', 'test'} | 'climate-fever' | claim | evidence | evidence_label SUPPORTS -> 'entailment', REFUTES -> 'contradiction' | |
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> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER. |
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### **cf-nli-nei** |
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This dataset uses the list of annotator 'votes' to cast a NOT_ENOUGH_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs. |
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| votes | effective evidence_label | |
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| SUPPORTS > REFUTES | _SUPPORTS_ | |
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| SUPPORTS < REFUTES | _REFUTES_ | |
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In addition to all the claims in **cf-nli**, any Claims that have, |
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* **_at least one_** SUPPORTS or REFUTES evidence, AND |
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* NEI evidences that can be cast to effective _SUPPORTS_ or _REFUTES_ |
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are included in the datasset. |
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### **cf-stsb** |
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For each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset: |
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| split | dataset | score | sentence1 | sentence2 | |
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| {'train', 'dev', 'test'} | 'climate-fever' | cos_sim score | claim | evidence | |
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This dataset uses 'evidence_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion: |
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> `mean(entropy)` refers to the average entropy within the defined group of evidence |
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| evidence_label | votes | similarity score | |
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| SUPPORTS | SUPPORTS > 0, REFUTES == 0, NOT_ENOUGH_INFO (NEI) == 0 | 1 | |
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| | SUPPORTS > 0, REFUTES == 0 | mean(entropy) | |
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| | SUPPORTS > 0, REFUTES > 0 | 1 - mean(entropy) | |
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| NEI | SUPPORTS > REFUTES | (1 - mean(entropy)) / 2| |
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| | SUPPORTS == REFUTES | 0 | |
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| | SUPPORTS < REFUTES | -(1 - mean(entropy)) / 2 | |
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| REFUTES | SUPPORTS > 0, REFUTES > 0 | -(1 - mean(entropy)) | |
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| | SUPPORTS == 0, REFUTES > 0 | -mean(entropy) | |
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| | SUPPORTS == 0, REFUTES > 0, NEI == 0 | -1 | |
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The above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1). |
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<!-- |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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[More Information Needed] |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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[More Information Needed] |
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### Contributions |
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[More Information Needed] |
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--> |
