Commit
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804d1a3
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Parent(s):
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first release
Browse files- README.md +31 -0
- config.json +40 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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language: da
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tags:
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- danish
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- bert
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- sentiment
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- polarity
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license: cc-by-4.0
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widget:
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- text: "Sikke en dejlig dag det er i dag"
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---
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# Danish BERT fine-tuned for Sentiment Analysis <img src="https://raw.githubusercontent.com/ebanalyse/NERDA/main/logo.png" align="right" height=150/>
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This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
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It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/senda) package.
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Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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tokenizer = AutoTokenizer.from_pretrained("pin/senda")
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model = AutoModelForSequenceClassification.from_pretrained("pin/senda")
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# create 'senda' sentiment analysis pipeline
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senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
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text = "Sikke en dejlig dag det er i dag"
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# 'what a lovely day'
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senda_pipeline("Sikke en dejlig dag det er i dag")
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```
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config.json
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{
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"_name_or_path": "./results/checkpoint-900",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "negativ",
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"1": "neutral",
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"2": "positiv"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"negativ": 0,
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"neutral": 1,
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"positiv": 2
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"transformers_version": "4.5.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:6bf99d3a3c56e417ed3d4fb10d51a4fba9fbc008545b296336dbfe911c981cfc
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size 442566130
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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]"}
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "Maltehb/danish-bert-botxo", "do_basic_tokenize": true, "never_split": null}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:fde9e18bd59ab66453c2401ac48131b3281dc7cfd5aadb705a53803fc702abef
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size 2351
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vocab.txt
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