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README.md

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+---
+language: 
+- en
+thumbnail: 
+tags:
+- text-classification
+license: mit
+datasets:
+- trec
+metrics:
+---
+
+# 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
+
+```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/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
+
+```python
+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
+```python
+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}
+```