dummy sequence classification model for testing

README.md

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+This is a dummy model that can be used for testing.
+
+It was created as follows in a python shell
+
+```python
+import transformers
+config = transformers.DistilBertConfig(vocab_size=1, n_layers=1, n_heads=1, dim=1, hidden_dim=4 * 1, num_labels=2, id2label={0: "negative", 1: "positive"}, label2id={"negative": 0, "positive": 1})
+model = transformers.DistilBertForSequenceClassification(config)
+tokenizer = transformers.DistilBertTokenizer("/tmp/empty_vocab.txt", model_max_length=512)
+config.save_pretrained(".")
+model.save_pretrained(".")
+tokenizer.save_pretrained(".")
+```

config.json

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+{
+  "activation": "gelu",
+  "attention_dropout": 0.1,
+  "dim": 1,
+  "dropout": 0.1,
+  "hidden_dim": 4,
+  "id2label": {
+    "0": "negative",
+    "1": "positive"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "negative": 0,
+    "positive": 1
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 1,
+  "n_layers": 1,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "transformers_version": "4.4.2",
+  "vocab_size": 1
+}

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": true, "do_basic_tokenize": true, "never_split": null, "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}