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README.md

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+# DynaSent: Dynamic Sentiment Analysis Dataset
+
+DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent).
+
+## Contents
+
+* [Citation](#Citation)
+* [Dataset files](#dataset-files)
+* [Quick start](#quick-start)
+* [Data format](#data-format)
+* [Models](#models)
+* [Other files](#other-files)
+* [License](#license)
+
+
+## Citation
+
+[Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. [DynaSent: A dynamic benchmark for sentiment analysis](https://arxiv.org/abs/2012.15349). Ms., Stanford University and Facebook AI Research.
+
+```stex
+  @article{potts-etal-2020-dynasent,
+    title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
+    author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe},
+    journal={arXiv preprint arXiv:2012.15349},
+    url={https://arxiv.org/abs/2012.15349},
+    year={2020}}
+```
+
+## Dataset files
+
+The dataset is [dynasent-v1.1.zip](dynasent-v1.1.zip), which is included in this repository. `v1.1` differs from `v1` only in that `v1.1` has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata.
+
+The dataset consists of two rounds, each with a train/dev/test split:
+
+
+### Round 1: Naturally occurring sentences
+
+* `dynasent-v1.1-round01-yelp-train.jsonl`
+* `dynasent-v1.1-round01-yelp-dev.jsonl`
+* `dynasent-v1.1-round01-yelp-test.jsonl`
+
+
+### Round 1: Sentences crowdsourced using Dynabench
+
+* `dynasent-v1.1-round02-dynabench-train.jsonl`
+* `dynasent-v1.1-round02-dynabench-dev.jsonl`
+* `dynasent-v1.1-round02-dynabench-test.jsonl`
+
+
+### SST-dev revalidation
+
+The dataset also contains a version of the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) dev set in our format with labels from our validation task:
+
+* `sst-dev-validated.jsonl`
+
+
+## Quick start
+
+This function can be used to load any subset of the files:
+
+```python
+import json
+
+def load_dataset(*src_filenames, labels=None):
+    data = []
+    for filename in src_filenames:
+        with open(filename) as f:
+            for line in f:
+                d = json.loads(line)
+                if labels is None or d['gold_label'] in labels:
+                    data.append(d)
+    return data
+```
+
+For example, to create a Round 1 train set restricting to examples with ternary gold labels:
+
+```python
+import os
+
+r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl')
+
+ternary_labels = ('positive', 'negative', 'neutral')
+
+r1_train = load_dataset(r1_train_filename, labels=ternary_labels)
+
+X_train, y_train = zip(*[(d['sentence'], d['gold_label']) for d in r1_train])
+```
+
+## Data format
+
+### Round 1 format
+
+```python
+{'hit_ids': ['y5238'],
+ 'sentence': 'Roto-Rooter is always good when you need someone right away.',
+ 'indices_into_review_text': [0, 60],
+ 'model_0_label': 'positive',
+ 'model_0_probs': {'negative': 0.01173639390617609,
+  'positive': 0.7473671436309814,
+  'neutral': 0.24089649319648743},
+ 'text_id': 'r1-0000001',
+ 'review_id': 'IDHkeGo-nxhqX4Exkdr08A', 
+ 'review_rating': 1,
+ 'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'],
+  'negative': [],
+  'neutral': [],
+  'mixed': []},
+ 'gold_label': 'positive'}
+```
+
+Details:
+
+* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
+* `'sentence'`: The example text.
+* `'indices_into_review_text':` indices of `'sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
+* `'model_0_label'`: prediction of Model 0 as described in the paper. The possible values are `'positive'`, `'negative'`, and `'neutral'`.
+* `'model_0_probs'`: probability distribution predicted by Model 0. The keys are `('positive', 'negative', 'neutral')` and the values are floats.
+* `'text_id'`: unique identifier for this entry.
+* `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'sentence'`.
+* `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). The possible values are `1`, `2`, `3`, `4`, and `5`.
+* `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
+* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. 
+
+Here is some code one could use to augment a dataset, as loaded by `load_dataset`, with a field giving the full review text from the [Yelp Academic Dataset](https://www.yelp.com/dataset):
+
+```python
+import json
+
+def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'):
+    index = {}
+    with open(yelp_src_filename) as f:
+        for line in f:
+            d = json.loads(line)
+            index[d['review_id']] = d['text']
+    return index
+
+yelp_index = index_yelp_reviews()
+
+def add_review_text_round1(dataset, yelp_index):
+    for d in dataset:
+        review_text = yelp_index[d['text_id']]
+        # Check that we can find the sentence as expected:
+        start, end = d['indices_into_review_text']
+        assert review_text[start: end] == d['sentence']
+        d['review_text'] = review_text
+    return dataset
+```
+
+### Round 2 format
+
+```python
+{'hit_ids': ['y22661'],
+ 'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.",
+ 'sentence_author': 'w250',
+ 'has_prompt': True,
+ 'prompt_data': {'indices_into_review_text': [2093, 2213],
+  'review_rating': 5,
+  'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.",
+  'review_id': 'Krm4kSIb06BDHternF4_pA'},
+ 'model_1_label': 'positive',
+ 'model_1_probs': {'negative': 0.29140257835388184,
+  'positive': 0.6788994669914246,
+  'neutral': 0.029697999358177185},
+ 'text_id': 'r2-0000001',
+ 'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'],
+  'negative': [],
+  'neutral': [],
+  'mixed': ['w174']},
+ 'gold_label': 'positive'}
+ ```
+
+Details:
+
+* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
+* `'sentence'`: The example text.
+* `'sentence_author'`:  Anonymized MTurk id of the worker who wrote `'sentence'`. These are from the same family of ids as used in `'label_distribution'`, but this id is never one of the ids in `'label_distribution'` for this example.
+* `'has_prompt'`: `True` if the `'sentence'` was written with a Prompt else `False`.
+* `'prompt_data'`: None if `'has_prompt'` is False, else:
+  * `'indices_into_review_text'`: indices of `'prompt_sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
+  * `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
+  * `'prompt_sentence'`: The prompt text.
+  * `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'prompt_sentence'`.
+* `'model_1_label'`: prediction of Model 1 as described in the paper. The possible values are `'positive'`, `'negative'`, and '`neutral'`.
+* `'model_1_probs'`: probability distribution predicted by Model 1. The keys are `('positive', 'negative', 'neutral')` and the values are floats.  
+* `'text_id'`: unique identifier for this entry.
+* `'label_distribution'`: response distribution from the MTurk validation task. The keys are  `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
+* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. 
+
+To add the review texts to the `'prompt_data'` field, one can extend the code above for Round 1 with the following function:
+
+```python
+def add_review_text_round2(dataset, yelp_index):
+    for d in dataset:
+        if d['has_prompt']:
+            prompt_data = d['prompt_data']
+            review_text = yelp_index[prompt_data['review_id']]
+            # Check that we can find the sentence as expected:
+            start, end = prompt_data['indices_into_review_text']
+            assert review_text[start: end] == prompt_data['prompt_sentence']
+            prompt_data['review_text'] = review_text
+    return dataset
+```
+
+### SST-dev format
+
+```python
+{'hit_ids': ['s20533'],
+ 'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.',
+ 'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))',
+ 'text_id': 'sst-dev-validate-0000437',
+ 'sst_label': '4',
+ 'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'],
+  'negative': [],
+  'neutral': [],
+  'mixed': []},
+ 'gold_label': 'positive'}
+```
+
+Details:
+
+* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
+* `'sentence'`: The example text.
+* `'tree'`: The parsetree for the example as given in the SST distribution.
+* `'text_id'`: A new identifier for this example.
+* `'sst_label'`: The root-node label from the SST. Possible values `'0'`, `'1'` `'2'`, `'3'`, and `'4'`.
+* `'label_distribution':` response distribution from the MTurk validation task. The keys are  `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
+* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
+
+
+## Models
+
+Model 0 and Model 1 from the paper are available here:
+
+https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing
+
+This repository includes a Python module `dynasent_models.py` that provides a [Hugging Face](https://huggingface.co)-based wrapper around these ([PyTorch](https://pytorch.org)) models. Simple examples:
+
+```python
+import os
+from dynasent_models import DynaSentModel
+
+# `dynasent_model0` should be downloaded from the above Google Drive link and 
+# placed in the `models` directory. `dynasent_model1` works the same way.
+model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
+
+examples = [
+    "superb",
+    "They said the experience would be amazing, and they were right!",
+    "They said the experience would be amazing, and they were wrong!"]
+
+model.predict(examples)
+```
+This should return the list `['positive', 'positive', 'negative']`.
+
+The `predict_proba` method provides access to the predicted distribution over the class labels; see the demo at the bottom of `dynasent_models.py` for details.
+
+The following code uses `load_dataset` from above to reproduce the Round 2 dev-set report on Model 0 from the paper:
+
+```python
+import os
+from sklearn.metrics import classification_report
+from dynasent_models import DynaSentModel
+
+dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl')
+
+dev = load_dataset(dev_filename)
+
+X_dev, y_dev = zip(*[(d['sentence'], d['gold_label']) for d in dev])
+
+model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
+
+preds = model.predict(X_dev)
+
+print(classification_report(y_dev, preds, digits=3))
+```
+For a fuller report on these models, see our paper and [our model card](dynasent_modelcard.md).
+
+
+## Other files
+
+
+### Analysis notebooks
+
+The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper:
+
+* `analyses_comparative.ipynb`
+* `analysis_round1.ipynb`
+* `analysis_round2.ipynb`
+* `analysis_sst_dev_revalidate.ipynb`
+
+The Python module `dynasent_utils.py` contains functions that support those notebooks, and `dynasent.mplstyle` helps with styling the plots.
+
+
+### Datasheet
+
+The [Datasheet](https://arxiv.org/abs/1803.09010) for our dataset:
+
+* [dynasent_datasheet.md](dynasent_datasheet.md)
+
+
+### Model Card
+
+The [Model Card](https://arxiv.org/pdf/1810.03993.pdf) for our models:
+
+* [dynasent_modelcard.md](dynasent_modelcard.md)
+
+
+### Tests
+
+The module `test_dataset.py` contains PyTest tests for the dataset. To use it, run
+
+```
+py.test -vv test_dataset.py
+```
+
+in the root directory of this repository.
+
+
+### Validation HIT code
+
+The file `validation-hit-contents.html` contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window.
+
+
+## License
+
+DynaSent has a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).