Push distilbert base cased trec model

README.md

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

config.json

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+{
+  "_name_or_path": "distilbert-base-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "DESC",
+    "1": "ENTY",
+    "2": "ABBR",
+    "3": "HUM",
+    "4": "NUM",
+    "5": "LOC"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "ABBR": 2,
+    "DESC": 0,
+    "ENTY": 1,
+    "HUM": 3,
+    "LOC": 5,
+    "NUM": 4
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.2.2",
+  "vocab_size": 28996
+}

pytorch_model.bin

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "distilbert-base-cased"}

training_args.bin

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