metadata
language:
- en
license: mit
tags:
- text-classification
datasets:
- trec
model-index:
- name: aychang/distilbert-base-cased-trec-coarse
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: trec
type: trec
config: default
split: test
metrics:
- type: accuracy
value: 0.97
name: Accuracy
verified: true
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- type: precision
value: 0.9742915631870833
name: Precision Macro
verified: true
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value: 0.97
name: Precision Micro
verified: true
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- type: precision
value: 0.9699546283251607
name: Precision Weighted
verified: true
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- type: recall
value: 0.972626762268805
name: Recall Macro
verified: true
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- type: recall
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- type: recall
value: 0.97
name: Recall Weighted
verified: true
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- type: f1
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TREC 6-class Task: distilbert-base-cased
Model description
A simple base distilBERT model trained on the "trec" dataset.
Intended uses & limitations
How to use
Transformers
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Use pipeline
from transformers import pipeline
model_name = "aychang/distilbert-base-cased-trec-coarse"
nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"])
AdaptNLP
from adaptnlp import EasySequenceClassifier
model_name = "aychang/distilbert-base-cased-trec-coarse"
texts = ["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]
classifer = EasySequenceClassifier
results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2)
Limitations and bias
This is minimal language model trained on a benchmark dataset.
Training data
TREC https://huggingface.co/datasets/trec
Training procedure
Preprocessing, hardware used, hyperparameters...
Hardware
One V100
Hyperparameters and Training Args
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir='./models',
overwrite_output_dir=False,
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
warmup_steps=500,
weight_decay=0.01,
evaluation_strategy="steps",
logging_dir='./logs',
fp16=False,
eval_steps=500,
save_steps=300000
)
Eval results
{'epoch': 2.0,
'eval_accuracy': 0.97,
'eval_f1': array([0.98220641, 0.91620112, 1. , 0.97709924, 0.98678414,
0.97560976]),
'eval_loss': 0.14275787770748138,
'eval_precision': array([0.96503497, 0.96470588, 1. , 0.96969697, 0.98245614,
0.96385542]),
'eval_recall': array([1. , 0.87234043, 1. , 0.98461538, 0.99115044,
0.98765432]),
'eval_runtime': 0.9731,
'eval_samples_per_second': 513.798}