metadata
language:
- en
license: mit
tags:
- text-classification
datasets:
- trec
model-index:
- name: aychang/bert-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: 0.974
name: Accuracy
verified: true
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- type: precision
value: 0.9793164100816639
name: Precision Macro
verified: true
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- type: precision
value: 0.974
name: Precision Micro
verified: true
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- type: precision
value: 0.9746805065928548
name: Precision Weighted
verified: true
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- type: recall
value: 0.9783617516169679
name: Recall Macro
verified: true
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- type: recall
value: 0.974
name: Recall Micro
verified: true
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- type: recall
value: 0.974
name: Recall Weighted
verified: true
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- type: f1
value: 0.9783635353409951
name: F1 Macro
verified: true
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- type: f1
value: 0.974
name: F1 Micro
verified: true
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- type: f1
value: 0.97377371266232
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verified: true
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- type: loss
value: 0.13812002539634705
name: loss
verified: true
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bert-base-cased trained on TREC 6-class task
Model description
A simple base BERT 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/bert-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/bert-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',
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',
save_steps=3000
)
Eval results
{'epoch': 2.0,
'eval_accuracy': 0.974,
'eval_f1': array([0.98181818, 0.94444444, 1. , 0.99236641, 0.96995708,
0.98159509]),
'eval_loss': 0.138086199760437,
'eval_precision': array([0.98540146, 0.98837209, 1. , 0.98484848, 0.94166667,
0.97560976]),
'eval_recall': array([0.97826087, 0.90425532, 1. , 1. , 1. ,
0.98765432]),
'eval_runtime': 1.6132,
'eval_samples_per_second': 309.943}