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spencerbraunspencerbraun
committedFeb 24, 2022
model updates to reflect ICLR 2022 revisions
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‎README.md

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# Anomaly Transformer in PyTorch
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This is an implementation of [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://arxiv.org/abs/2110.02642). This paper is currently [under review](https://openreview.net/forum?id=LzQQ89U1qm_) and in need of some clarification around the attention mechanism. This repo will be updated as more information is provided.
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This is an implementation of [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://arxiv.org/abs/2110.02642). This paper has been accepted as a [Spotlight Paper at ICLR 2022](https://openreview.net/forum?id=LzQQ89U1qm_).
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Repository currently a work in progress.
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## Usage
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(env) $ pip install -r requirements.txt
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```
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Written with python version `3.8.11`
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### Data and Configuration
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Custom datasets can be placed in the `data/` dir. Edits should be made to the `conf/data/default.yaml` file to reflect the correct properties of the data. All other configuration hyperparameters can be set in the hydra configs.

‎archive/exploration.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"p = torch.from_numpy(np.abs(np.indices((100,100))[0] - np.indices((100,100))[1]))\n",
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"sigma = torch.ones(100).view(100, 1) * 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"P = torch.ones(10,10) * torch.arange(10).view(10,1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"S = torch.ones(10,10) * torch.arange(10).view(1,10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/spencerbraun/.pyenv/versions/3.8.11/envs/dl/lib/python3.8/site-packages/torch/nn/functional.py:2747: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"tensor(3.4057)"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lambda row: F.kl_div(P[row,:], S[row,:]) + F.kl_div(S[row,:], P[row,:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
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" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
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" [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
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" [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n",
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" [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
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" [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.],\n",
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" [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
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" [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],\n",
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" [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.],\n",
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" [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"P"
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]
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},
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"torch.Size([100, 100])"
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"metadata": {},
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"output_type": "execute_result"
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"ename": "NameError",
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"evalue": "name 'sigma' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/var/folders/8w/r6kg1v9x7bbfzf9dw9gjslc80000gn/T/ipykernel_95641/3924366364.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'sigma' is not defined"
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{
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"ename": "NameError",
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"evalue": "name 'p' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/var/folders/8w/r6kg1v9x7bbfzf9dw9gjslc80000gn/T/ipykernel_95641/3546984218.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgaussian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgaussian\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mgaussian\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'p' is not defined"
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]
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}
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],
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"source": [
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"gaussian /= gaussian.sum(dim=-1).view(-1, 1)"
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"ename": "NameError",
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"evalue": "name 'gaussian' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/var/folders/8w/r6kg1v9x7bbfzf9dw9gjslc80000gn/T/ipykernel_95641/494294515.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgaussian\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'gaussian' is not defined"
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"source": []
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‎conf/config.yaml

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defaults:
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- model: default
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- data: default
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- evaluate: default
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- train: default
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rephrase: true
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nq_path: ${hydra:runtime.cwd}/data/nq
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wiki_webtext: true
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n_edits: 1
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‎conf/evaluate/default.yaml

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‎conf/model/default.yaml

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‎conf/model/transformers.yaml

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‎conf/train/default.yaml

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‎conf/train/lightning.yaml

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