Duplicate from aychang/roberta-base-imdb

Co-authored-by: Andrew Chang <aychang@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+thumbnail: null
+tags:
+- text-classification
+license: mit
+datasets:
+- imdb
+metrics: null
+duplicated_from: aychang/roberta-base-imdb
+---
+
+# IMDB Sentiment Task: roberta-base 
+
+## Model description
+
+A simple base roBERTa model trained on the "imdb" 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/roberta-base-imdb"
+
+nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
+
+results = nlp(["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."])
+```
+
+##### AdaptNLP
+
+```python
+from adaptnlp import EasySequenceClassifier
+
+model_name = "aychang/roberta-base-imdb"
+texts = ["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."]
+
+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
+
+IMDB https://huggingface.co/datasets/imdb
+
+## Training procedure
+
+#### 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=8,
+    per_device_eval_batch_size=8,
+    warmup_steps=500,
+    weight_decay=0.01,
+    evaluation_strategy="steps",
+    logging_dir='./logs',
+    fp16=False,
+    eval_steps=800,
+    save_steps=300000
+)
+```
+
+## Eval results
+
+```
+{'epoch': 2.0,
+ 'eval_accuracy': 0.94668,
+ 'eval_f1': array([0.94603457, 0.94731017]),
+ 'eval_loss': 0.2578844428062439,
+ 'eval_precision': array([0.95762642, 0.93624502]),
+ 'eval_recall': array([0.93472, 0.95864]),
+ 'eval_runtime': 244.7522,
+ 'eval_samples_per_second': 102.144}
+```

config.json

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+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "neg",
+    "1": "pos"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "neg": 0,
+    "pos": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.2.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

tokenizer_config.json

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+{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 512, "name_or_path": "roberta-base"}

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