Initial migration from CUNY-CL/abstractness.

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README.md

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+# Yoyodyne 🪀
+
+Yoyodyne provides neural models for small-vocabulary sequence-to-sequence
+generation with and without feature conditioning.
+
+These models are implemented using PyTorch and PyTorch Lightning.
+
+While we provide classic `lstm` and `transformer` models, some of the provided
+models are particularly well-suited for problems where the source-target
+alignments are roughly monotonic (e.g., `transducer`) and/or where source and
+target vocabularies are not disjoint and substrings of the source are copied
+into the target (e.g., `pointer_generator_lstm`).
+
+## Philosophy
+
+Yoyodyne is inspired by [FairSeq](https://github.com/facebookresearch/fairseq)
+but differs on several key points of design:
+
+-   It is for small-vocabulary sequence-to-sequence generation, and therefore
+    includes no affordances for machine translation or language modeling.
+    Because of this:
+    -  It has no plugin interface and the architectures provided are intended
+       to be reasonably exhaustive.
+    -  There is little need for data preprocessing; it works with TSV files.
+-   It has support for using features to condition decoding, with
+    architecture-specific code to handle this feature information.
+-   🚧 UNDER CONSTRUCTION 🚧: It has exhaustive test suites.
+-   🚧 UNDER CONSTRUCTION 🚧: It has performance benchmark.
+-   🚧 UNDER CONSTRUCTION 🚧: Releases are made regularly.
+
+## Install
+
+First install dependencies:
+
+    pip install -r requirements.txt
+
+Then install:
+
+    python setup.py install
+
+Or:
+
+    python setup.py develop
+
+The latter creates a Python module in your environment that updates as you
+update the code. It can then be imported like a regular Python module:
+
+```python
+import yoyodyne
+```
+
+## Usage
+
+For examples, see [`experiments`](experiments). See
+[`train.py`](yoyodyne/train.py) and [`predict.py`](yoyodyne/predict.py) for all
+model options.
+
+## Architectures
+
+The user specifies the model using the `--arch` flag (and in some cases
+additional flags).
+
+-   `feature_invariant_transformer`: This is a variant of the `transformer`
+    which uses a learned embedding to distinguish input symbols from features.
+    It may be superior to the vanilla transformer when using features.
+-   `lstm`: This is an LSTM encoder-decoder, with the initial hidden state
+    treated as a learned parameter. By default, the encoder is connected to the
+    decoder by an attention mechanism; one can disable this (with `--no-attn`),
+    in which case the last non-padding hidden state of the encoder is
+    concatenated with the decoder hidden state.
+-   `pointer_generator_lstm`: This is an attentive pointer-generator with an
+    LSTM backend. Since this model contains a copy mechanism, it may be superior
+    to the `lstm` when the input and output vocabularies overlap significantly.
+-   `transducer`: This is a transducer with an LSTM backend. On model creation,
+    expectation maximization is used to learn a sequence of edit operations, and
+    imitation learning is used to train the model to implement the oracle
+    policy, with roll-in controlled by the `--oracle-factor` flag (default: 1).
+    Since this model assumes monotonic alignment, it may be superior to
+    attentive models when the alignment between input and output is roughly
+    monotonic and when input and output vocabularies overlap significantly.
+-   `transformer`: This is a transformer encoder-decoder with positional
+    encoding and layer normalization. The user may wish to specify the number of
+    attention heads (with `--nheads`; default: 4).
+
+For all models, the user may also wish to specify:
+
+-   `--dec-layers` (default: 1): number of decoder layers
+-   `--embedding` (default: 128): embedding size
+-   `--enc-layers` (default: 1): number of encoder layers
+-   `--hidden-size` (default: 256): hidden layer size
+
+By default, the `lstm`, `pointer_generator_lstm`, and `transducer` models use an
+LSTM bidirectional encoder. One can disable this with the `--no-bidirectional`
+flag.
+
+## Training options
+
+-   `--batch-size` (default: 16)
+-   `--beta1` (default: .9): $\beta_1$ hyperparameter for the Adam optimizer
+    (`--optimizer adam`)
+-   `--beta2` (default: .99): $\beta_2$ hyperparameter for the Adam optimizer
+    (`--optimizer adam`)
+-   `--dropout` (default: .1): dropout probability
+-   `--epochs` (default: 20)
+-   `--gradient-clip` (default: 0.0)
+-   `--label-smoothing` (default: not enabled)
+-   `--learning-rate` (required)
+-   `--lr-scheduler` (default: not enabled)
+-   `--optimizer` (default: "adadelta")
+-   `--patience` (default: not enabled)
+-   `--wandb` (default: False): enables [Weights &
+    Biases](https://wandb.ai/site) tracking
+-   `--warmup-steps` (default: 1): warm-up parameter for a linear warm-up
+    followed by inverse square root decay schedule (only valid with
+    `--lr-scheduler warmupinvsq`)
+
+## Data format
+
+The default data format is based on the SIGMORPHON 2017 shared tasks:
+
+    source   target    feat1;feat2;...
+
+That is, the first column is the source (a lemma), the second is the target (the
+inflection), and the third contains semi-colon delimited feature strings.
+
+For the SIGMORPHON 2016 shared task data format:
+
+    source   feat1,feat2,...    target
+
+one instead specifies `--target-col 3 --features-col 2 --features-sep ,`
+
+Finally, to perform transductions without features (whether or not a feature
+column exists in the data), one specifies `--features-col 0`.

requirements.txt

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+black>=22.3.0
+click>=8.1.3
+flake8>=3.9.2
+maxwell>=0.2.0
+numpy>=1.20.1
+pytorch-lightning==1.6.4
+scipy>=1.6
+torch>=1.11.0
+tqdm>=4.64.1
+wandb