Initial commit

README.md

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+# Targeted Sentiment Analysis in News Headlines
+
+BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
+
+(details to be published)
+
+
+## Examples
+
+Input is as follows
+
+`Headline [SEP] Target`
+
+where headline is the news title and target an
+
+Try
+
+`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri` (should be NEG)
+
+and
+
+`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri` (POS or NEU)

config.json

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+{
+  "_name_or_path": "dccuchile/bert-base-spanish-wwm-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NEG",
+    "1": "NEU",
+    "2": "POS"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NEG": 0,
+    "NEU": 1,
+    "POS": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.4.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31002
+}

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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