---
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
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGNmZTQ1Mjk3YTQ0NTdiZmY2NGM2NDM2Yzc2OTI4NGNiZDg4MmViN2I0ZGZiYWJlMTg1ZDU0MTc2ZTg1NjcwZiIsInZlcnNpb24iOjF9.4x_Ze9S5MbAeIHZ4p1EFmWev8RLkAIYWKqouAzYOxTNqdfFN0HnqULiM19EMP42v658vl_fR3-Ig0xG45DioCA
    - type: precision
      value: 0.9742915631870833
      name: Precision Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjA2MWVjMDc3MDYyY2M3NzY4NGNhY2JlNzJjMGQzZDUzZjE3ZWI1MjVmMzc4ODM2ZTQ4YmRhOTVkZDU0MzJiNiIsInZlcnNpb24iOjF9.EfmXJ6w5_7dK6ys03hpADP9h_sWuPAHgxpltUtCkJP4Ys_Gh8Ak4pGS149zt5AdP_zkvsWlXwAvx5BDMEoB2AA
    - type: precision
      value: 0.97
      name: Precision Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVjOGFjM2RkMDMxZTFiMzE1ZDM4OTRjMzkwOWE2NTJmMmUwMDdiZDg5ZjExYmFmZjg2Y2Y5NzcxZWVkODkwZSIsInZlcnNpb24iOjF9.BtO7DqJsUhSXE-_tJZJOPPd421VmZ3KR9-KkrhJkLNenoV2Xd6Pu6i5y6HZQhFB-9WfEhU9cCsIPQ1ioZ7dyDA
    - type: precision
      value: 0.9699546283251607
      name: Precision Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGQ0Mzc2MTE2YjkwNGY1MDEzNWQwYmNlZDMzZjBmNWM0ODExYjM1OTQyZGJkNjI2OTA5MDczZjFmOGM5MmMzMyIsInZlcnNpb24iOjF9.fGi2qNpOjWd1ci3p_E1p80nOqabiKiQqpQIxtk5aWxe_Nzqh3XiOCBF8vswCRvX8qTKdCc2ZEJ4s8dZMeltfCA
    - type: recall
      value: 0.972626762268805
      name: Recall Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjQwMWZiYjIyMGVhN2M1ZDE5M2EzZmQ1ODRlYzE0MzJhZmU3ZTM1MmIyNTg5ZjBlMDcyMmQ0NmYzZjFmMmM4NSIsInZlcnNpb24iOjF9.SYDxsRw0xoQuQhei0YBdUbBxG891gqLafVFLdPMCJtQIktqCTrPW0sMKtis7GA-FEbNQVu8lp92znvlryNiFCw
    - type: recall
      value: 0.97
      name: Recall Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjQ0MjczYjFhZDdiMjdkMWVlZTAzYWU0ODVhNjkxN2I1N2Y1Y2IyOTNlYWQxM2UxODIyNDZhZDM3MWIwMTgzZCIsInZlcnNpb24iOjF9.C5cfDTz_H4Y7nEO4Eq_XFy92CSbo3IBuL5n8wBKkTuB6hSgctTHOdOJzV8gWyMJ9gRcNqxp_yVU4BEB_I_0KAA
    - type: recall
      value: 0.97
      name: Recall Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDZmYWM3OWExZWI1ZjRiZjczYWQwOWI5NWQzNDNkODcyMjBhMmVkYjY0MGZjYzlhNWQ0Y2MyMjc3OWEyZjY4NCIsInZlcnNpb24iOjF9.65WM5ihNfbKOCNZ6apX7iVAC2Ge_cwz9Xwa5oJHFq3Ci97eBFqK-qtADdB_SFRcSQUoNodaBeIhNfe0hVddxCA
    - type: f1
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      name: F1 Macro
      verified: true
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    - type: f1
      value: 0.97
      name: F1 Micro
      verified: true
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    - type: f1
      value: 0.9694196751375908
      name: F1 Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTQ5ZjdiM2NiNDNkZTY5ZjNjNWUzZmI1MzgwMjhhNDEzMTEzZjFiNDhmZDllYmI0NjIwYjY0ZjcxM2M0ODE3NSIsInZlcnNpb24iOjF9.x4oR_PL0ALHYl-s4S7cPNPm4asSX3s3h30m-TKe7wpyZs0x6jwOqF-Tb1kgd4IMLl23pzsezmh72e_PmBFpRCg
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      verified: true
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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

```python
# 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

```python
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
```python
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}
```