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Repository: dtsip/in-context-learning
Branch: main
Commit: 338337192195
Files: 21
Total size: 747.5 KB
Directory structure:
gitextract_kmfnflhw/
├── .gitignore
├── LICENSE
├── README.md
├── environment.yml
└── src/
├── base_models.py
├── conf/
│ ├── base.yaml
│ ├── decision_tree.yaml
│ ├── linear_regression.yaml
│ ├── relu_2nn_regression.yaml
│ ├── sparse_linear_regression.yaml
│ ├── toy.yaml
│ └── wandb.yaml
├── curriculum.py
├── eval.ipynb
├── eval.py
├── models.py
├── plot_utils.py
├── samplers.py
├── schema.py
├── tasks.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
.ipynb_checkpoints
__pycache__
models.zip
models
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2022 Dimitris Tsipras, Shivam Garg
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
This repository contains the code and models for our paper:
**What Can Transformers Learn In-Context? A Case Study of Simple Function Classes** <br>
*Shivam Garg\*, Dimitris Tsipras\*, Percy Liang, Gregory Valiant* <br>
Paper: http://arxiv.org/abs/2208.01066 <br><br>

```bibtex
@InProceedings{garg2022what,
title={What Can Transformers Learn In-Context? A Case Study of Simple Function Classes},
author={Shivam Garg and Dimitris Tsipras and Percy Liang and Gregory Valiant},
year={2022},
booktitle={arXiv preprint}
}
```
## Getting started
You can start by cloning our repository and following the steps below.
1. Install the dependencies for our code using Conda. You may need to adjust the environment YAML file depending on your setup.
```
conda env create -f environment.yml
conda activate in-context-learning
```
2. Download [model checkpoints](https://github.com/dtsip/in-context-learning/releases/download/initial/models.zip) and extract them in the current directory.
```
wget https://github.com/dtsip/in-context-learning/releases/download/initial/models.zip
unzip models.zip
```
3. [Optional] If you plan to train, populate `conf/wandb.yaml` with you wandb info.
That's it! You can now explore our pre-trained models or train your own. The key entry points
are as follows (starting from `src`):
- The `eval.ipynb` notebook contains code to load our own pre-trained models, plot the pre-computed metrics, and evaluate them on new data.
- `train.py` takes as argument a configuration yaml from `conf` and trains the corresponding model. You can try `python train.py --config conf/toy.yaml` for a quick training run.
# Maintainers
* [Shivam Garg](https://cs.stanford.edu/~shivamg/)
* [Dimitris Tsipras](https://dtsipras.com/)
================================================
FILE: environment.yml
================================================
name: in-context-learning
channels:
- pytorch
- defaults
dependencies:
- pip=21.2.4
- python=3.8.12
- pytorch=1.11.0
- pip:
- jupyter==1.0.0
- matplotlib==3.5.2
- numpy==1.22.3
- pandas==1.4.2
- quinine==0.3.0
- scikit-learn==1.0.2
- seaborn==0.11.2
- tqdm==4.64.0
- transformers==4.17.0
- wandb==0.12.11
- xgboost==1.6.1
- protobuf==3.20.1
================================================
FILE: src/base_models.py
================================================
import torch
from torch import nn
class NeuralNetwork(nn.Module):
def __init__(self, in_size=50, hidden_size=1000, out_size=1):
super(NeuralNetwork, self).__init__()
self.net = nn.Sequential(
nn.Linear(in_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, out_size),
)
def forward(self, x):
out = self.net(x)
return out
class ParallelNetworks(nn.Module):
def __init__(self, num_models, model_class, **model_class_init_args):
super(ParallelNetworks, self).__init__()
self.nets = nn.ModuleList(
[model_class(**model_class_init_args) for i in range(num_models)]
)
def forward(self, xs):
assert xs.shape[0] == len(self.nets)
for i in range(len(self.nets)):
out = self.nets[i](xs[i])
if i == 0:
outs = torch.zeros(
[len(self.nets)] + list(out.shape), device=out.device
)
outs[i] = out
return outs
================================================
FILE: src/conf/base.yaml
================================================
inherit:
- models/standard.yaml
- wandb.yaml
model:
n_dims: 20
n_positions: 101
training:
data: gaussian
task_kwargs: {}
batch_size: 64
learning_rate: 0.0001
save_every_steps: 1000
keep_every_steps: 100000
train_steps: 500001
curriculum:
dims:
start: 5
end: 20
inc: 1
interval: 2000
================================================
FILE: src/conf/decision_tree.yaml
================================================
inherit:
- base.yaml
training:
task: decision_tree
task_kwargs: {"depth": 4}
curriculum:
points:
start: 26
end: 101
inc: 5
interval: 2000
out_dir: ../models/decision_tree
wandb:
name: "decision_tree_standard"
================================================
FILE: src/conf/linear_regression.yaml
================================================
inherit:
- base.yaml
training:
task: linear_regression
curriculum:
points:
start: 11
end: 41
inc: 2
interval: 2000
out_dir: ../models/linear_regression
wandb:
name: "linear_regression_standard"
================================================
FILE: src/conf/relu_2nn_regression.yaml
================================================
inherit:
- base.yaml
training:
task: relu_2nn_regression
task_kwargs: {"hidden_layer_size": 100}
curriculum:
points:
start: 26
end: 101
inc: 5
interval: 2000
out_dir: ../models/relu_2nn_regression
wandb:
name: "relu_2nn_regression_standard"
================================================
FILE: src/conf/sparse_linear_regression.yaml
================================================
inherit:
- base.yaml
training:
task: sparse_linear_regression
task_kwargs: {"sparsity": 3}
curriculum:
points:
start: 11
end: 41
inc: 2
interval: 2000
out_dir: ../models/sparse_linear_regression
wandb:
name: "sparse_regression_standard"
================================================
FILE: src/conf/toy.yaml
================================================
inherit:
- models/standard.yaml
- wandb.yaml
model:
n_dims: 5
n_positions: 11
training:
task: linear_regression
data: gaussian
task_kwargs: {}
batch_size: 64
learning_rate: 0.0001
save_every_steps: 1000
keep_every_steps: 100000
train_steps: 5001
curriculum:
dims:
start: 5
end: 5
inc: 1
interval: 2000
points:
start: 11
end: 11
inc: 2
interval: 2000
out_dir: ../models/linear_regression
wandb:
name: "linear_regression_toy"
================================================
FILE: src/conf/wandb.yaml
================================================
wandb:
project: in-context-training
entity: your-entity
notes:
log_every_steps: 100
================================================
FILE: src/curriculum.py
================================================
import math
class Curriculum:
def __init__(self, args):
# args.dims and args.points each contain start, end, inc, interval attributes
# inc denotes the change in n_dims,
# this change is done every interval,
# and start/end are the limits of the parameter
self.n_dims_truncated = args.dims.start
self.n_points = args.points.start
self.n_dims_schedule = args.dims
self.n_points_schedule = args.points
self.step_count = 0
def update(self):
self.step_count += 1
self.n_dims_truncated = self.update_var(
self.n_dims_truncated, self.n_dims_schedule
)
self.n_points = self.update_var(self.n_points, self.n_points_schedule)
def update_var(self, var, schedule):
if self.step_count % schedule.interval == 0:
var += schedule.inc
return min(var, schedule.end)
# returns the final value of var after applying curriculum.
def get_final_var(init_var, total_steps, inc, n_steps, lim):
final_var = init_var + math.floor((total_steps) / n_steps) * inc
return min(final_var, lim)
================================================
FILE: src/eval.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ed6cfeb1",
"metadata": {},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
"import re\n",
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import torch\n",
"from tqdm.notebook import tqdm\n",
"\n",
"from eval import get_run_metrics, read_run_dir, get_model_from_run\n",
"from plot_utils import basic_plot, collect_results, relevant_model_names\n",
"\n",
"%matplotlib inline\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"sns.set_theme('notebook', 'darkgrid')\n",
"palette = sns.color_palette('colorblind')\n",
"\n",
"run_dir = \"../models\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0e8d018b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" run_id task \\\n",
"0 pretrained decision_tree \n",
"1 pretrained linear_regression \n",
"2 d1ee6875-d215-418b-b5ef-b7edb52cb4ac linear_regression \n",
"3 pretrained relu_2nn_regression \n",
"4 pretrained sparse_linear_regression \n",
"\n",
" model kwargs num_tasks num_examples n_dims \\\n",
"0 Transformer depth=4 -1 -1 20 \n",
"1 Transformer -1 -1 20 \n",
"2 Transformer -1 -1 5 \n",
"3 Transformer hidden_layer_size=100 -1 -1 20 \n",
"4 Transformer sparsity=3 -1 -1 20 \n",
"\n",
" n_layer n_head run_name \n",
"0 12 8 decision_tree_pretrained \n",
"1 12 8 linear_regression_pretrained \n",
"2 12 8 linear_regression_toy \n",
"3 12 8 relu_2nn_regression_pretrained \n",
"4 12 8 sparse_regression_pretrained "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = read_run_dir(run_dir)\n",
"df # list all the runs in our run_dir"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a9980951",
"metadata": {},
"outputs": [],
"source": [
"task = \"linear_regression\"\n",
"#task = \"sparse_linear_regression\"\n",
"#task = \"decision_tree\"\n",
"#task = \"relu_2nn_regression\"\n",
"\n",
"run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n",
"\n",
"run_path = os.path.join(run_dir, task, run_id)\n",
"recompute_metrics = False\n",
"\n",
"if recompute_metrics:\n",
" get_run_metrics(run_path) # these are normally precomputed at the end of training"
]
},
{
"cell_type": "markdown",
"id": "f6d09964",
"metadata": {},
"source": [
"# Plot pre-computed metrics"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cd8e02c5",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"linear_regression_pretrained pretrained\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 137068.76it/s]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def valid_row(r):\n",
" return r.task == task and r.run_id == run_id\n",
"\n",
"metrics = collect_results(run_dir, df, valid_row=valid_row)\n",
"_, conf = get_model_from_run(run_path, only_conf=True)\n",
"n_dims = conf.model.n_dims\n",
"\n",
"models = relevant_model_names[task]\n",
"basic_plot(metrics[\"standard\"], models=models)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "31b4ecca",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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
gitextract_kmfnflhw/
├── .gitignore
├── LICENSE
├── README.md
├── environment.yml
└── src/
├── base_models.py
├── conf/
│ ├── base.yaml
│ ├── decision_tree.yaml
│ ├── linear_regression.yaml
│ ├── relu_2nn_regression.yaml
│ ├── sparse_linear_regression.yaml
│ ├── toy.yaml
│ └── wandb.yaml
├── curriculum.py
├── eval.ipynb
├── eval.py
├── models.py
├── plot_utils.py
├── samplers.py
├── schema.py
├── tasks.py
└── train.py
SYMBOL INDEX (110 symbols across 8 files)
FILE: src/base_models.py
class NeuralNetwork (line 5) | class NeuralNetwork(nn.Module):
method __init__ (line 6) | def __init__(self, in_size=50, hidden_size=1000, out_size=1):
method forward (line 15) | def forward(self, x):
class ParallelNetworks (line 20) | class ParallelNetworks(nn.Module):
method __init__ (line 21) | def __init__(self, num_models, model_class, **model_class_init_args):
method forward (line 27) | def forward(self, xs):
FILE: src/curriculum.py
class Curriculum (line 4) | class Curriculum:
method __init__ (line 5) | def __init__(self, args):
method update (line 16) | def update(self):
method update_var (line 23) | def update_var(self, var, schedule):
function get_final_var (line 31) | def get_final_var(init_var, total_steps, inc, n_steps, lim):
FILE: src/eval.py
function get_model_from_run (line 17) | def get_model_from_run(run_path, step=-1, only_conf=False):
function eval_batch (line 41) | def eval_batch(model, task_sampler, xs, xs_p=None):
function gen_standard (line 68) | def gen_standard(data_sampler, n_points, b_size):
function gen_opposite_quadrants (line 74) | def gen_opposite_quadrants(data_sampler, n_points, b_size):
function gen_random_quadrants (line 84) | def gen_random_quadrants(data_sampler, n_points, b_size):
function gen_orthogonal_train_test (line 94) | def gen_orthogonal_train_test(data_sampler, n_points, b_size):
function gen_overlapping_train_test (line 120) | def gen_overlapping_train_test(data_sampler, n_points, b_size):
function aggregate_metrics (line 134) | def aggregate_metrics(metrics, bootstrap_trials=1000):
function eval_model (line 151) | def eval_model(
function build_evals (line 192) | def build_evals(conf):
function compute_evals (line 265) | def compute_evals(all_models, evaluation_kwargs, save_path=None, recompu...
function get_run_metrics (line 290) | def get_run_metrics(
function conf_to_model_name (line 323) | def conf_to_model_name(conf):
function baseline_names (line 334) | def baseline_names(name):
function read_run_dir (line 354) | def read_run_dir(run_dir):
FILE: src/models.py
function build_model (line 14) | def build_model(conf):
function get_relevant_baselines (line 29) | def get_relevant_baselines(task_name):
class TransformerModel (line 80) | class TransformerModel(nn.Module):
method __init__ (line 81) | def __init__(self, n_dims, n_positions, n_embd=128, n_layer=12, n_head...
method _combine (line 102) | def _combine(xs_b, ys_b):
method forward (line 116) | def forward(self, xs, ys, inds=None):
class NNModel (line 130) | class NNModel:
method __init__ (line 131) | def __init__(self, n_neighbors, weights="uniform"):
method __call__ (line 137) | def __call__(self, xs, ys, inds=None):
class LeastSquaresModel (line 174) | class LeastSquaresModel:
method __init__ (line 175) | def __init__(self, driver=None):
method __call__ (line 179) | def __call__(self, xs, ys, inds=None):
class AveragingModel (line 206) | class AveragingModel:
method __init__ (line 207) | def __init__(self):
method __call__ (line 210) | def __call__(self, xs, ys, inds=None):
class LassoModel (line 236) | class LassoModel:
method __init__ (line 237) | def __init__(self, alpha, max_iter=100000):
method __call__ (line 245) | def __call__(self, xs, ys, inds=None):
class GDModel (line 294) | class GDModel:
method __init__ (line 295) | def __init__(
method __call__ (line 320) | def __call__(self, xs, ys, inds=None, verbose=False, print_step=100):
class DecisionTreeModel (line 403) | class DecisionTreeModel:
method __init__ (line 404) | def __init__(self, max_depth=None):
method __call__ (line 410) | def __call__(self, xs, ys, inds=None):
class XGBoostModel (line 442) | class XGBoostModel:
method __init__ (line 443) | def __init__(self):
method __call__ (line 448) | def __call__(self, xs, ys, inds=None):
FILE: src/plot_utils.py
function basic_plot (line 43) | def basic_plot(metrics, models=None, trivial=1.0):
function collect_results (line 70) | def collect_results(run_dir, df, valid_row=None, rename_eval=None, renam...
FILE: src/samplers.py
class DataSampler (line 6) | class DataSampler:
method __init__ (line 7) | def __init__(self, n_dims):
method sample_xs (line 10) | def sample_xs(self):
function get_data_sampler (line 14) | def get_data_sampler(data_name, n_dims, **kwargs):
function sample_transformation (line 26) | def sample_transformation(eigenvalues, normalize=False):
class GaussianSampler (line 36) | class GaussianSampler(DataSampler):
method __init__ (line 37) | def __init__(self, n_dims, bias=None, scale=None):
method sample_xs (line 42) | def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None):
FILE: src/tasks.py
function squared_error (line 6) | def squared_error(ys_pred, ys):
function mean_squared_error (line 10) | def mean_squared_error(ys_pred, ys):
function accuracy (line 14) | def accuracy(ys_pred, ys):
function cross_entropy (line 22) | def cross_entropy(ys_pred, ys):
class Task (line 28) | class Task:
method __init__ (line 29) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None):
method evaluate (line 36) | def evaluate(self, xs):
method generate_pool_dict (line 40) | def generate_pool_dict(n_dims, num_tasks):
method get_metric (line 44) | def get_metric():
method get_training_metric (line 48) | def get_training_metric():
function get_task_sampler (line 52) | def get_task_sampler(
class LinearRegression (line 76) | class LinearRegression(Task):
method __init__ (line 77) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, sca...
method evaluate (line 96) | def evaluate(self, xs_b):
method generate_pool_dict (line 102) | def generate_pool_dict(n_dims, num_tasks, **kwargs): # ignore extra args
method get_metric (line 106) | def get_metric():
method get_training_metric (line 110) | def get_training_metric():
class SparseLinearRegression (line 114) | class SparseLinearRegression(LinearRegression):
method __init__ (line 115) | def __init__(
method evaluate (line 145) | def evaluate(self, xs_b):
method get_metric (line 151) | def get_metric():
method get_training_metric (line 155) | def get_training_metric():
class LinearClassification (line 159) | class LinearClassification(LinearRegression):
method evaluate (line 160) | def evaluate(self, xs_b):
method get_metric (line 165) | def get_metric():
method get_training_metric (line 169) | def get_training_metric():
class NoisyLinearRegression (line 173) | class NoisyLinearRegression(LinearRegression):
method __init__ (line 174) | def __init__(
method evaluate (line 191) | def evaluate(self, xs_b):
class QuadraticRegression (line 200) | class QuadraticRegression(LinearRegression):
method evaluate (line 201) | def evaluate(self, xs_b):
class Relu2nnRegression (line 211) | class Relu2nnRegression(Task):
method __init__ (line 212) | def __init__(
method evaluate (line 247) | def evaluate(self, xs_b):
method generate_pool_dict (line 258) | def generate_pool_dict(n_dims, num_tasks, hidden_layer_size=4, **kwargs):
method get_metric (line 265) | def get_metric():
method get_training_metric (line 269) | def get_training_metric():
class DecisionTree (line 273) | class DecisionTree(Task):
method __init__ (line 274) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, dep...
method evaluate (line 310) | def evaluate(self, xs_b):
method generate_pool_dict (line 335) | def generate_pool_dict(n_dims, num_tasks, hidden_layer_size=4, **kwargs):
method get_metric (line 339) | def get_metric():
method get_training_metric (line 343) | def get_training_metric():
FILE: src/train.py
function train_step (line 22) | def train_step(model, xs, ys, optimizer, loss_func):
function sample_seeds (line 31) | def sample_seeds(total_seeds, count):
function train (line 38) | def train(model, args):
function main (line 137) | def main(args):
Condensed preview — 21 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (771K chars).
[
{
"path": ".gitignore",
"chars": 49,
"preview": ".ipynb_checkpoints\n__pycache__\nmodels.zip\nmodels\n"
},
{
"path": "LICENSE",
"chars": 1086,
"preview": "MIT License\n\nCopyright (c) 2022 Dimitris Tsipras, Shivam Garg\n\nPermission is hereby granted, free of charge, to any pers"
},
{
"path": "README.md",
"chars": 1832,
"preview": "This repository contains the code and models for our paper:\n\n**What Can Transformers Learn In-Context? A Case Study of S"
},
{
"path": "environment.yml",
"chars": 401,
"preview": "name: in-context-learning\nchannels:\n - pytorch\n - defaults\ndependencies:\n - pip=21.2.4\n - python=3.8.12\n - pytorch="
},
{
"path": "src/base_models.py",
"chars": 1043,
"preview": "import torch\nfrom torch import nn\n\n\nclass NeuralNetwork(nn.Module):\n def __init__(self, in_size=50, hidden_size=1000,"
},
{
"path": "src/conf/base.yaml",
"chars": 390,
"preview": "inherit: \n - models/standard.yaml\n - wandb.yaml\n\nmodel:\n n_dims: 20\n n_positions: 101\n\ntraining:\n data: g"
},
{
"path": "src/conf/decision_tree.yaml",
"chars": 289,
"preview": "inherit: \n - base.yaml\n\ntraining:\n task: decision_tree\n task_kwargs: {\"depth\": 4}\n curriculum:\n point"
},
{
"path": "src/conf/linear_regression.yaml",
"chars": 270,
"preview": "inherit: \n - base.yaml\n\ntraining:\n task: linear_regression\n curriculum:\n points:\n start: 11\n "
},
{
"path": "src/conf/relu_2nn_regression.yaml",
"chars": 321,
"preview": "inherit: \n - base.yaml\n\ntraining:\n task: relu_2nn_regression\n task_kwargs: {\"hidden_layer_size\": 100}\n curri"
},
{
"path": "src/conf/sparse_linear_regression.yaml",
"chars": 317,
"preview": "inherit: \n - base.yaml\n\ntraining:\n task: sparse_linear_regression\n task_kwargs: {\"sparsity\": 3}\n curriculum:"
},
{
"path": "src/conf/toy.yaml",
"chars": 597,
"preview": "inherit: \n - models/standard.yaml\n - wandb.yaml\n\nmodel:\n n_dims: 5\n n_positions: 11\n\ntraining:\n task: lin"
},
{
"path": "src/conf/wandb.yaml",
"chars": 100,
"preview": "wandb:\n project: in-context-training\n entity: your-entity\n notes:\n log_every_steps: 100\n"
},
{
"path": "src/curriculum.py",
"chars": 1134,
"preview": "import math\n\n\nclass Curriculum:\n def __init__(self, args):\n # args.dims and args.points each contain start, en"
},
{
"path": "src/eval.ipynb",
"chars": 703761,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"ed6cfeb1\",\n \"metadata\": {},\n \"outputs\":"
},
{
"path": "src/eval.py",
"chars": 12926,
"preview": "import json\nimport os\nimport sys\n\nfrom munch import Munch\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\ni"
},
{
"path": "src/models.py",
"chars": 16357,
"preview": "import torch\nimport torch.nn as nn\nfrom transformers import GPT2Model, GPT2Config\nfrom tqdm import tqdm\nfrom sklearn.svm"
},
{
"path": "src/plot_utils.py",
"chars": 3536,
"preview": "import os\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom eval import get_run_metrics, baseline_names, get_"
},
{
"path": "src/samplers.py",
"chars": 1756,
"preview": "import math\n\nimport torch\n\n\nclass DataSampler:\n def __init__(self, n_dims):\n self.n_dims = n_dims\n\n def sam"
},
{
"path": "src/schema.py",
"chars": 2305,
"preview": "from quinine import (\n tstring,\n tinteger,\n tfloat,\n tboolean,\n stdict,\n tdict,\n default,\n requi"
},
{
"path": "src/tasks.py",
"chars": 11351,
"preview": "import math\n\nimport torch\n\n\ndef squared_error(ys_pred, ys):\n return (ys - ys_pred).square()\n\n\ndef mean_squared_error("
},
{
"path": "src/train.py",
"chars": 5624,
"preview": "import os\nfrom random import randint\nimport uuid\n\nfrom quinine import QuinineArgumentParser\nfrom tqdm import tqdm\nimport"
}
]
About this extraction
This page contains the full source code of the dtsip/in-context-learning GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 21 files (747.5 KB), approximately 481.1k tokens, and a symbol index with 110 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.