!pip install simpletransformers
Collecting simpletransformers
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Collecting transformers>=3.0.2
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Collecting tokenizers
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[?25hCollecting streamlit
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Collecting validators
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Collecting blinker
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Collecting gitdb<5,>=4.0.1
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Building wheels for collected packages: seqeval, watchdog, subprocess32, gql, sacremoses, blinker, pathtools, graphql-core
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Installing collected packages: smmap, gitdb, GitPython, pathtools, watchdog, subprocess32, shortuuid, configparser, graphql-core, gql, docker-pycreds, sentry-sdk, wandb, tqdm, seqeval, tokenizers, sentencepiece, sacremoses, transformers, tensorboardx, validators, enum-compat, blinker, ipykernel, pydeck, base58, streamlit, simpletransformers
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from simpletransformers.classification import ClassificationModel, ClassificationArgs
import pandas as pd
import logging
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
# Preparing train data
train_data = [
["Aragorn was the heir of Isildur", "true"],
["Frodo was the heir of Isildur", "false"],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", "true"],
["Merry was the king of Rohan", "false"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
# Optional model configuration
model_args = ClassificationArgs()
model_args.num_train_epochs=1
model_args.labels_list = ["true", "false"]
# Create a ClassificationModel
model = ClassificationModel(
"roberta", "roberta-base", args=model_args,use_cuda=False
)
# Train the model
model.train_model(train_df)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
# Make predictions with the model
predictions, raw_outputs = model.predict(["Sam was a Wizard"])
INFO:filelock:Lock 140155715063760 acquired on /root/.cache/torch/transformers/80b4a484eddeb259bec2f06a6f2f05d90934111628e0e1c09a33bd4a121358e1.49b88ba7ec2c26a7558dda98ca3884c3b80fa31cf43a1b1f23aef3ff81ba344e.lock
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WARNING:transformers.modeling_utils:Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']
- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
WARNING:transformers.modeling_utils:Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.
warnings.warn(SAVE_STATE_WARNING, UserWarning)
INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.
INFO:simpletransformers.classification.classification_model: Converting to features started. Cache is not used.
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/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:900: RuntimeWarning: invalid value encountered in double_scalars
mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
INFO:simpletransformers.classification.classification_model:{'mcc': 0.0, 'tp': 1, 'tn': 0, 'fp': 1, 'fn': 0, 'eval_loss': 0.696165144443512}
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predictions
['false']
ls outputs/
[0m[01;34mcheckpoint-1-epoch-1[0m/ merges.txt special_tokens_map.json vocab.json
config.json model_args.json tokenizer_config.json
eval_results.txt pytorch_model.bin training_args.bin
from simpletransformers.classification import ClassificationModel, ClassificationArgs
import pandas as pd
import logging
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
# Preparing train data
train_data = [
["Aragorn was the heir of Isildur", "true"],
["Frodo was the heir of Isildur", "false"],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", "true"],
["Merry was the king of Rohan", "false"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
# Optional model configuration
model_args = ClassificationArgs()
model_args.num_train_epochs=1
model_args.labels_list = ["true", "false"]
model_args.overwrite_output_dir = True
# Create a ClassificationModel
model = ClassificationModel(
"roberta", "outputs/", args=model_args,use_cuda=False
)
# Train the model
model.train_model(train_df)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
# Make predictions with the model
predictions, raw_outputs = model.predict(["Sam was a Wizard"])
INFO:simpletransformers.classification.classification_model: Converting to features started. Cache is not used.
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INFO:simpletransformers.classification.classification_model: Starting fine-tuning.
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/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.
warnings.warn(SAVE_STATE_WARNING, UserWarning)
INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.
INFO:simpletransformers.classification.classification_model: Converting to features started. Cache is not used.
HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))
HBox(children=(FloatProgress(value=0.0, description='Running Evaluation', max=1.0, style=ProgressStyle(descrip…
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:900: RuntimeWarning: invalid value encountered in double_scalars
mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
INFO:simpletransformers.classification.classification_model:{'mcc': 0.0, 'tp': 1, 'tn': 0, 'fp': 1, 'fn': 0, 'eval_loss': 0.696165144443512}
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import logging
import pandas as pd
import sklearn
import wandb
from simpletransformers.classification import (
ClassificationArgs,
ClassificationModel,
)
sweep_config = {
"method": "bayes", # grid, random
"metric": {"name": "train_loss", "goal": "minimize"},
"parameters": {
"num_train_epochs": {"values": [2, 3, 5]},
"learning_rate": {"min": 5e-5, "max": 4e-4},
},
}
sweep_id = wandb.sweep(sweep_config, project="Simple Sweep")
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
# Preparing train data
train_data = [
["Aragorn was the heir of Isildur", "true"],
["Frodo was the heir of Isildur", "false"],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", "true"],
["Merry was the king of Rohan", "false"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
model_args = ClassificationArgs()
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.evaluate_during_training = True
model_args.manual_seed = 4
model_args.train_batch_size = 16
model_args.eval_batch_size = 8
model_args.labels_list = ["true", "false"]
model_args.wandb_project = "Simple Sweep"
def train():
# Initialize a new wandb run
wandb.init()
# Create a TransformerModel
model = ClassificationModel(
"roberta",
"roberta-base",
use_cuda=False,
args=model_args,
sweep_config=wandb.config,
)
# Train the model
model.train_model(train_df, eval_df=eval_df)
# Evaluate the model
model.eval_model(eval_df)
# Sync wandb
wandb.join()
wandb.agent(sweep_id, train)
Create sweep with ID: 03byb6mp
Sweep URL: https://app.wandb.ai/ankur310794/Simple%20Sweep/sweeps/03byb6mp
INFO:wandb.wandb_agent:Running runs: []
INFO:wandb.wandb_agent:Agent received command: run
INFO:wandb.wandb_agent:Agent starting run with config:
learning_rate: 0.00015971839686415725
num_train_epochs: 3
wandb: Agent Starting Run: zt8m9f5b with config:
learning_rate: 0.00015971839686415725
num_train_epochs: 3
wandb: Agent Started Run: zt8m9f5b
Logging results to <a href="https://wandb.com" target="_blank">Weights & Biases</a> <a href="https://docs.wandb.com/integrations/jupyter.html" target="_blank">(Documentation)</a>.<br/>
Project page: <a href="https://app.wandb.ai/ankur310794/Simple%20Sweep" target="_blank">https://app.wandb.ai/ankur310794/Simple%20Sweep</a><br/>
Sweep page: <a href="https://app.wandb.ai/ankur310794/Simple%20Sweep/sweeps/03byb6mp" target="_blank">https://app.wandb.ai/ankur310794/Simple%20Sweep/sweeps/03byb6mp</a><br/>
Run page: https://app.wandb.ai/ankur310794/Simple%20Sweep/runs/zt8m9f5b
INFO:wandb.run_manager:system metrics and metadata threads started
INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds
INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnp0OG05ZjViOlNpbXBsZSBTd2VlcDphbmt1cjMxMDc5NA==
INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds
INFO:wandb.run_manager:saving pip packages
INFO:wandb.run_manager:initializing streaming files api
INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers
INFO:wandb.run_manager:file/dir modified: /content/wandb/run-20200827_001734-zt8m9f5b/config.yaml
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001734-zt8m9f5b/wandb-history.jsonl
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001734-zt8m9f5b/requirements.txt
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001734-zt8m9f5b/wandb-summary.json
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001734-zt8m9f5b/wandb-events.jsonl
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001734-zt8m9f5b/wandb-metadata.json
INFO:wandb.wandb_agent:Running runs: ['zt8m9f5b']
WARNING:transformers.modeling_utils:Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']
- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
WARNING:transformers.modeling_utils:Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
INFO:simpletransformers.classification.classification_model: Converting to features started. Cache is not used.
HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))
HBox(children=(FloatProgress(value=0.0, description='Epoch', max=3.0, style=ProgressStyle(description_width='i…
Logging results to Weights & Biases (Documentation).
Project page: https://app.wandb.ai/ankur310794/Simple%20Sweep
Run page: https://app.wandb.ai/ankur310794/Simple%20Sweep/runs/s4yq3tsr
INFO:wandb.run_manager:system metrics and metadata threads started
INFO:wandb.run_manager:checking resume status, waiting at most 10 seconds
INFO:wandb.run_manager:resuming run from id: UnVuOnYxOnM0eXEzdHNyOlNpbXBsZSBTd2VlcDphbmt1cjMxMDc5NA==
INFO:wandb.run_manager:upserting run before process can begin, waiting at most 10 seconds
INFO:wandb.run_manager:saving pip packages
INFO:wandb.run_manager:initializing streaming files api
INFO:wandb.run_manager:unblocking file change observer, beginning sync with W&B servers
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INFO:wandb.run_manager:file/dir modified: /content/wandb/run-20200827_001741-s4yq3tsr/config.yaml
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001741-s4yq3tsr/wandb-history.jsonl
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001741-s4yq3tsr/wandb-metadata.json
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001741-s4yq3tsr/wandb-events.jsonl
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001741-s4yq3tsr/wandb-summary.json
INFO:wandb.run_manager:file/dir created: /content/wandb/run-20200827_001741-s4yq3tsr/requirements.txt
Process Process-11:
Traceback (most recent call last):
File "/usr/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/usr/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.6/dist-packages/wandb/wandb_agent.py", line 64, in _start
function()
File "<ipython-input-11-75bdbb0e00ff>", line 67, in train
model.train_model(train_df, eval_df=eval_df)
File "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py", line 306, in train_model
**kwargs,
File "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py", line 494, in train
loss.backward()
File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 185, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py", line 127, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: Unable to handle autograd's threading in combination with fork-based multiprocessing. See https://github.com/pytorch/pytorch/wiki/Autograd-and-Fork
[34m[1mwandb[0m: Ctrl-c pressed. Waiting for runs to end. Press ctrl-c again to terminate them.
from simpletransformers.classification import ClassificationModel, ClassificationArgs
import pandas as pd
import logging
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
# Preparing train data
train_data = [
["Aragorn was the heir of Isildur", 1],
["Frodo was the heir of Isildur", 0],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", 1],
["Merry was the king of Rohan", 0],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
# Train only the classifier layers
model_args = ClassificationArgs()
model_args.train_custom_parameters_only = True
model_args.custom_parameter_groups = [
{
"params": ["classifier.weight"],
"lr": 1e-3,
},
{
"params": ["classifier.bias"],
"lr": 1e-3,
"weight_decay": 0.0,
},
]
model_args.overwrite_output_dir=True
# Create a ClassificationModel
model = ClassificationModel(
"bert", "bert-base-cased", args=model_args,use_cuda=False
)
# Train the model
model.train_model(train_df)
WARNING:transformers.modeling_utils:Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
WARNING:transformers.modeling_utils:Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
INFO:simpletransformers.classification.classification_model: Converting to features started. Cache is not used.
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HBox(children=(FloatProgress(value=0.0, description='Epoch', max=1.0, style=ProgressStyle(description_width='i…
HBox(children=(FloatProgress(value=0.0, description='Running Epoch 0 of 1', max=1.0, style=ProgressStyle(descr…
/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.
warnings.warn(SAVE_STATE_WARNING, UserWarning)
INFO:simpletransformers.classification.classification_model: Training of bert model complete. Saved to outputs/.