cotk is an open-source lightweight framework for model building and evaluation.
We provides standard dataset and evaluation suites in the domain of general language generation.
It easy to use and make you focus on designing your models!
Features included:
- Light-weight, easy to start. Don't bother your way to construct models.
- Predefined standard datasets, in the domain of language modeling, dialog generation and more.
- Predefined evaluation suites, test your model with multiple metrics in several lines.
- A dashboard to show experiments, compare your and others' models fairly.
- Long-term maintenance and consistent development.
This project is a part of dialtk (Toolkits for Dialog System by Tsinghua University), you can follow dialtk or cotk on our home page.
Note: master branch is the developing branch. The newest release is v0.1.0.
Quick links
Index
- python 3
- numpy >= 1.13
- nltk >= 3.4
- tqdm >= 4.30
- checksumdir >= 1.1
- pytorch >= 1.0.0 (optional, accelerating the calculation of some metrics)
- transformers (optional, used for pretrained models)
We support Unix, Windows, and macOS.
You can simply get the latest stable version from pip using
pip install cotk- Clone the cotk repository
git clone https://github.com/thu-coai/cotk.git- Install cotk via pip
cd cotk
pip install -e .Let's skim through the whole package to find what you want.
Load common used dataset and do preprocessing:
- Download online resources or import from local path
- Split training set, development set and test set
- Construct vocabulary list
>>> import cotk.dataloader
>>> # automatically download online resources
>>> dataloader = cotk.dataloader.MSCOCO("resources://MSCOCO_small")
>>> # or download from a url
>>> dl_url = cotk.dataloader.MSCOCO("http://cotk-data.s3-ap-northeast-1.amazonaws.com/mscoco_small.zip#MSCOCO")
>>> # or import from local file
>>> dl_zip = cotk.dataloader.MSCOCO("./MSCOCO.zip#MSCOCO")
>>> print("Dataset is split into:", dataloader.fields.keys())
dict_keys(['train', 'dev', 'test'])Inspect vocabulary list
>>> print("Vocabulary size:", dataloader.frequent_vocab_size)
Vocabulary size: 2597
>>> print("First 10 tokens in vocabulary:", dataloader.frequent_vocab_list[:10])
First 10 tokens in vocabulary: ['<pad>', '<unk>', '<go>', '<eos>', '.', 'a', 'A', 'on', 'of', 'in']Convert between ids and strings
>>> print("Convert string to ids", \
... dataloader.convert_tokens_to_ids(["<go>", "hello", "world", "<eos>"]))
Convert string to ids [2, 6107, 1875, 3]
>>> print("Convert ids to string", \
... dataloader.convert_ids_to_tokens([2, 1379, 1897, 3]))
Convert ids to string ['hello', 'world']Iterate over batches
>>> for data in dataloader.get_batches("train", batch_size=1):
... print(data)
{'sent':
array([[ 2, 181, 13, 26, 145, 177, 8, 22, 12, 5, 1, 1099, 4, 3]]),
# <go> This is an old photo of people and a <unk> wagon.
'sent_allvocabs':
array([[ 2, 181, 13, 26, 145, 177, 8, 22, 12, 5, 3755, 1099, 4, 3]]),
# <go> This is an old photo of people and a horse-drawn wagon.
'sent_length': array([14])}
......Or using while (another iteration method) if you like
>>> dataloader.restart("train", batch_size=1):
>>> while True:
... data = dataloader.get_next_batch("train")
... if data is None: break
... print(data)note: If you want to know more about Dataloader, please refer to docs of dataloader.
We found there are different versions of the same metric in different papers,
which leads to unfair comparison between models. For example, whether considering
unk, calculating the mean of NLL across sentences or tokens in
perplexity may introduce huge differences.
We provide a unified implementation for metrics, where hashvalue is provided for
checking whether the same data is used. The metric object receives mini-batches.
>>> import cotk.metric
>>> metric = cotk.metric.SelfBleuCorpusMetric(dataloader, gen_key="gen")
>>> metric.forward({
... "gen":
... [[2, 181, 13, 26, 145, 177, 8, 22, 12, 5, 3755, 1099, 4, 3],
... [2, 46, 145, 500, 1764, 207, 11, 5, 93, 7, 31, 4, 3]]
... })
>>> print(metric.close())
{'self-bleu': 0.02253475750490193, 'self-bleu hashvalue': 'f7d75c0d0dbf53ffba4b845d1f61487fd2d6d3c0594b075c43111816c84c65fc'}You can merge multiple metrics together by cotk.metric.MetricChain.
>>> metric = cotk.metric.MetricChain()
>>> metric.add_metric(cotk.metric.SelfBleuCorpusMetric(dataloader, gen_key="gen"))
>>> metric.add_metric(cotk.metric.FwBwBleuCorpusMetric(dataloader, reference_test_list=dataloader.get_all_batch('test')['sent_allvocabs'], gen_key="gen"))
>>> metric.forward({
... "gen":
... [[2, 181, 13, 26, 145, 177, 8, 22, 12, 5, 3755, 1099, 4, 3],
... [2, 46, 145, 500, 1764, 207, 11, 5, 93, 7, 31, 4, 3]]
... })
>>> print(metric.close())
100%|██████████| 1000/1000 [00:00<00:00, 5281.95it/s]
{'self-bleu': 0.02253475750490193, 'self-bleu hashvalue': 'f7d75c0d0dbf53ffba4b845d1f61487fd2d6d3c0594b075c43111816c84c65fc', 'fw-bleu': 0.28135593382545376, 'bw-bleu': 0.027021522872801896, 'fw-bw-bleu': 0.04930753293488745, 'fw-bw-bleu hashvalue': '60a39f381e065e8df6fb5eb272984128c9aea7dee4ba50a43bfb768395a70762'}We also provide recommended metrics for selected dataloader.
>>> metric = dataloader.get_inference_metric(gen_key="gen")
>>> metric.forward({
... "gen":
... [[2, 181, 13, 26, 145, 177, 8, 22, 12, 5, 3755, 1099, 4, 3],
... [2, 46, 145, 500, 1764, 207, 11, 5, 93, 7, 31, 4, 3]]
... })
>>> print(metric.close())
100%|██████████| 1000/1000 [00:00<00:00, 4857.36it/s]
100%|██████████| 1250/1250 [00:00<00:00, 4689.29it/s]
{'self-bleu': 0.02253475750490193, 'self-bleu hashvalue': 'f7d75c0d0dbf53ffba4b845d1f61487fd2d6d3c0594b075c43111816c84c65fc', 'fw-bleu': 0.3353037449663603, 'bw-bleu': 0.027327995838287513, 'fw-bw-bleu': 0.050537105917262654, 'fw-bw-bleu hashvalue': 'c254aa4008ae11b1bc4955e7cd1f7f3aad34b664178a585a218b1474970e3f23', 'gen': [['inside', 'is', 'an', 'elephant', 'shirt', 'of', 'people', 'and', 'a', 'grasslands', 'pulls', '.'], ['An', 'elephant', 'girls', 'baggage', 'sidewalk', 'with', 'a', 'clock', 'on', 'it', '.']]}note: If you want to know more about metrics, please refer to docs of metrics.
We have provided some baselines for the classical tasks, see Model Zoo in docs for details.
You can also use cotk download thu-coai/MODEL_NAME/master to get the codes.
You are welcome to create an issue if you want to request a feature, report a bug or ask a general question.
We welcome contributions from community.
- If you want to make a big change, we recommend first creating an issue with your design.
- Small contributions can be directly made by a pull request.
- If you like make contributions for our library, see issues to find what we need.
cotk is maintained and developed by Tsinghua university conversational AI group (THU-coai). Check our main pages (In Chinese).
Apache License 2.0
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