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AutoML - NLP

Requirements

This example requires GPU. Install the [nlp] option:

pip install "flaml[nlp]"

A simple sequence classification example

from flaml import AutoML
from datasets import load_dataset

train_dataset = load_dataset("glue", "mrpc", split="train").to_pandas()
dev_dataset = load_dataset("glue", "mrpc", split="validation").to_pandas()
test_dataset = load_dataset("glue", "mrpc", split="test").to_pandas()
custom_sent_keys = ["sentence1", "sentence2"]
label_key = "label"
X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]
X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]
X_test = test_dataset[custom_sent_keys]

automl = AutoML()
automl_settings = {
"time_budget": 100,
"task": "seq-classification",
"fit_kwargs_by_estimator": {
"transformer":
{
"output_dir": "data/output/" # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base
}
}, # setting the huggingface arguments: output directory
"gpu_per_trial": 1, # set to 0 if no GPU is available
}
automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)
automl.predict(X_test)

Notice that after you run automl.fit, the intermediate checkpoints are saved under the specified output_dir data/output. You can use the following code to clean these outputs if they consume a large storage space:

if os.path.exists("data/output/"):
shutil.rmtree("data/output/")

Sample output

[flaml.automl: 12-06 08:21:39] {1943} INFO - task = seq-classification
[flaml.automl: 12-06 08:21:39] {1945} INFO - Data split method: stratified
[flaml.automl: 12-06 08:21:39] {1949} INFO - Evaluation method: holdout
[flaml.automl: 12-06 08:21:39] {2019} INFO - Minimizing error metric: 1-accuracy
[flaml.automl: 12-06 08:21:39] {2071} INFO - List of ML learners in AutoML Run: ['transformer']
[flaml.automl: 12-06 08:21:39] {2311} INFO - iteration 0, current learner transformer
{'data/output/train_2021-12-06_08-21-53/train_8947b1b2_1_n=1e-06,s=9223372036854775807,e=1e-05,s=-1,s=0.45765,e=32,d=42,o=0.0,y=0.0_2021-12-06_08-21-53/checkpoint-53': 53}
[flaml.automl: 12-06 08:22:56] {2424} INFO - Estimated sufficient time budget=766860s. Estimated necessary time budget=767s.
[flaml.automl: 12-06 08:22:56] {2499} INFO - at 76.7s, estimator transformer's best error=0.1740, best estimator transformer's best error=0.1740
[flaml.automl: 12-06 08:22:56] {2606} INFO - selected model: <flaml.nlp.huggingface.trainer.TrainerForAuto object at 0x7f49ea8414f0>
[flaml.automl: 12-06 08:22:56] {2100} INFO - fit succeeded
[flaml.automl: 12-06 08:22:56] {2101} INFO - Time taken to find the best model: 76.69802761077881
[flaml.automl: 12-06 08:22:56] {2112} WARNING - Time taken to find the best model is 77% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.

A simple sequence regression example

from flaml import AutoML
from datasets import load_dataset

train_dataset = (
load_dataset("glue", "stsb", split="train").to_pandas()
)
dev_dataset = (
load_dataset("glue", "stsb", split="train").to_pandas()
)
custom_sent_keys = ["sentence1", "sentence2"]
label_key = "label"
X_train = train_dataset[custom_sent_keys]
y_train = train_dataset[label_key]
X_val = dev_dataset[custom_sent_keys]
y_val = dev_dataset[label_key]

automl = AutoML()
automl_settings = {
"gpu_per_trial": 0,
"time_budget": 20,
"task": "seq-regression",
"metric": "rmse",
}
automl_settings["fit_kwargs_by_estimator"] = { # setting the huggingface arguments
"transformer": {
"model_path": "google/electra-small-discriminator", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base
"output_dir": "data/output/", # setting the output directory
"fp16": False,
} # setting whether to use FP16
}
automl.fit(
X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
)

Sample output

[flaml.automl: 12-20 11:47:28] {1965} INFO - task = seq-regression
[flaml.automl: 12-20 11:47:28] {1967} INFO - Data split method: uniform
[flaml.automl: 12-20 11:47:28] {1971} INFO - Evaluation method: holdout
[flaml.automl: 12-20 11:47:28] {2063} INFO - Minimizing error metric: rmse
[flaml.automl: 12-20 11:47:28] {2115} INFO - List of ML learners in AutoML Run: ['transformer']
[flaml.automl: 12-20 11:47:28] {2355} INFO - iteration 0, current learner transformer

A simple summarization example

from flaml import AutoML
from datasets import load_dataset

train_dataset = (
load_dataset("xsum", split="train").to_pandas()
)
dev_dataset = (
load_dataset("xsum", split="validation").to_pandas()
)
custom_sent_keys = ["document"]
label_key = "summary"

X_train = train_dataset[custom_sent_keys]
y_train = train_dataset[label_key]

X_val = dev_dataset[custom_sent_keys]
y_val = dev_dataset[label_key]

automl = AutoML()
automl_settings = {
"gpu_per_trial": 1,
"time_budget": 20,
"task": "summarization",
"metric": "rouge1",
}
automl_settings["fit_kwargs_by_estimator"] = { # setting the huggingface arguments
"transformer": {
"model_path": "t5-small", # if model_path is not set, the default model is t5-small: https://huggingface.co/t5-small
"output_dir": "data/output/", # setting the output directory
"fp16": False,
} # setting whether to use FP16
}
automl.fit(
X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
)

Sample Output

[flaml.automl: 12-20 11:44:03] {1965} INFO - task = summarization
[flaml.automl: 12-20 11:44:03] {1967} INFO - Data split method: uniform
[flaml.automl: 12-20 11:44:03] {1971} INFO - Evaluation method: holdout
[flaml.automl: 12-20 11:44:03] {2063} INFO - Minimizing error metric: -rouge
[flaml.automl: 12-20 11:44:03] {2115} INFO - List of ML learners in AutoML Run: ['transformer']
[flaml.automl: 12-20 11:44:03] {2355} INFO - iteration 0, current learner transformer
loading configuration file https://huggingface.co/t5-small/resolve/main/config.json from cache at /home/xliu127/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985
Model config T5Config {
"_name_or_path": "t5-small",
"architectures": [
"T5WithLMHeadModel"
],
"d_ff": 2048,
"d_kv": 64,
"d_model": 512,
"decoder_start_token_id": 0,
"dropout_rate": 0.1,
"eos_token_id": 1,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 6,
"num_heads": 8,
"num_layers": 6,
"output_past": true,
"pad_token_id": 0,
"relative_attention_num_buckets": 32,
"task_specific_params": {
"summarization": {
"early_stopping": true,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
},
"transformers_version": "4.14.1",
"use_cache": true,
"vocab_size": 32128
}

A simple token classification example

There are two ways to define the label for a token classification task. The first is to define the token labels:

from flaml import AutoML
import pandas as pd

train_dataset = {
"id": ["0", "1"],
"ner_tags": [
["B-ORG", "O", "B-MISC", "O", "O", "O", "B-MISC", "O", "O"],
["B-PER", "I-PER"],
],
"tokens": [
[
"EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", ".",
],
["Peter", "Blackburn"],
],
}
dev_dataset = {
"id": ["0"],
"ner_tags": [
["O"],
],
"tokens": [
["1996-08-22"]
],
}
test_dataset = {
"id": ["0"],
"ner_tags": [
["O"],
],
"tokens": [
['.']
],
}
custom_sent_keys = ["tokens"]
label_key = "ner_tags"

train_dataset = pd.DataFrame(train_dataset)
dev_dataset = pd.DataFrame(dev_dataset)
test_dataset = pd.DataFrame(test_dataset)

X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]
X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]
X_test = test_dataset[custom_sent_keys]

automl = AutoML()
automl_settings = {
"time_budget": 10,
"task": "token-classification",
"fit_kwargs_by_estimator": {
"transformer":
{
"output_dir": "data/output/"
# if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base
}
}, # setting the huggingface arguments: output directory
"gpu_per_trial": 1, # set to 0 if no GPU is available
"metric": "seqeval:overall_f1"
}

automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)
automl.predict(X_test)

The second is to define the id labels + a token label list:

from flaml import AutoML
import pandas as pd

train_dataset = {
"id": ["0", "1"],
"ner_tags": [
[3, 0, 7, 0, 0, 0, 7, 0, 0],
[1, 2],
],
"tokens": [
[
"EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", ".",
],
["Peter", "Blackburn"],
],
}
dev_dataset = {
"id": ["0"],
"ner_tags": [
[0],
],
"tokens": [
["1996-08-22"]
],
}
test_dataset = {
"id": ["0"],
"ner_tags": [
[0],
],
"tokens": [
['.']
],
}
custom_sent_keys = ["tokens"]
label_key = "ner_tags"

train_dataset = pd.DataFrame(train_dataset)
dev_dataset = pd.DataFrame(dev_dataset)
test_dataset = pd.DataFrame(test_dataset)

X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]
X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]
X_test = test_dataset[custom_sent_keys]

automl = AutoML()
automl_settings = {
"time_budget": 10,
"task": "token-classification",
"fit_kwargs_by_estimator": {
"transformer":
{
"output_dir": "data/output/",
# if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base
"label_list": [ "O","B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC" ]
}
}, # setting the huggingface arguments: output directory
"gpu_per_trial": 1, # set to 0 if no GPU is available
"metric": "seqeval:overall_f1"
}

automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)
automl.predict(X_test)

Sample Output

[flaml.automl: 06-30 03:10:02] {2423} INFO - task = token-classification
[flaml.automl: 06-30 03:10:02] {2425} INFO - Data split method: stratified
[flaml.automl: 06-30 03:10:02] {2428} INFO - Evaluation method: holdout
[flaml.automl: 06-30 03:10:02] {2497} INFO - Minimizing error metric: seqeval:overall_f1
[flaml.automl: 06-30 03:10:02] {2637} INFO - List of ML learners in AutoML Run: ['transformer']
[flaml.automl: 06-30 03:10:02] {2929} INFO - iteration 0, current learner transformer

For tasks that are not currently supported, use flaml.tune for customized tuning.

To run more examples, especially examples using Ray Tune, please go to:

Link to notebook | Open in colab