First version of the da-bert-tone-subjective-objective model and tokenizer.

README.md

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+---
+language:
+- da
+tags:
+- bert
+- pytorch
+- subjectivity
+- objectivity
+license: CC-BY_4.0
+datasets:
+- Twitter Sentiment
+- Europarl Sentiment
+widget:
+- text: "Jeg tror alligvel, det bliver godt"
+metrics:
+- f1
+---
+
+# Danish BERT Tone for the detection of subjectivity/objectivity
+
+The BERT Tone model detects whether a text (in Danish) is subjective or objective. 
+The model is based on the finetuning of the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. 
+
+See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details. 
+
+
+Here is how to use the model:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+
+model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-tone-subjective-objective")
+tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-tone-subjective-objective")
+```
+
+## Training data
+
+The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
+

added_tokens.json

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+{}

config.json

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+{
+  "_name_or_path": ".",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "finetuning_task": "emo",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "objective",
+    "1": "subjective"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "objective": 0,
+    "subjective": 1
+  },
+  "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": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.9.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

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]"}

tf_model.h5

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tokenizer_config.json

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+{"do_lower_case": true, "init_inputs": []}

training_args.bin

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