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fernet sk ner

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README.md ADDED
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - wikiann
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ language:
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+ - sk
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+ inference: false
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+ model-index:
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+ - name: fernet-sk-ner
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: wikiann sk
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+ type: wikiann
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+ args: sk
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.9359821760118826
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+ - name: Recall
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+ type: recall
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+ value: 0.9472378804960541
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+ - name: F1
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+ type: f1
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+ value: 0.9415763914830033
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9789063466534702
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+ ---
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+
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+ # Named Entity Recognition based on FERNET-CC_sk
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+
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+ This model is a fine-tuned version of [fav-kky/FERNET-CC_sk](https://huggingface.co/fav-kky/FERNET-CC_sk) on the Slovak wikiann dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1763
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+ - Precision: 0.9360
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+ - Recall: 0.9472
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+ - F1: 0.9416
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+ - Accuracy: 0.9789
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+
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+ ## Intended uses & limitation
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+ Supported classes: LOCATION, PERSON, ORGANIZATION
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+
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+ ```
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+ from transformers import pipeline
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+
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+ ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
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+ input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
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+ classifications = ner_pipeline(input_sentence)
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+ ```
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 24
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+ - eval_batch_size: 24
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.1259 | 1.0 | 834 | 0.1095 | 0.8963 | 0.9182 | 0.9071 | 0.9697 |
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+ | 0.071 | 2.0 | 1668 | 0.0974 | 0.9270 | 0.9357 | 0.9313 | 0.9762 |
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+ | 0.0323 | 3.0 | 2502 | 0.1259 | 0.9257 | 0.9330 | 0.9293 | 0.9745 |
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+ | 0.0175 | 4.0 | 3336 | 0.1347 | 0.9241 | 0.9360 | 0.9300 | 0.9756 |
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+ | 0.0156 | 5.0 | 4170 | 0.1407 | 0.9337 | 0.9404 | 0.9370 | 0.9780 |
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+ | 0.0062 | 6.0 | 5004 | 0.1522 | 0.9267 | 0.9410 | 0.9338 | 0.9774 |
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+ | 0.0055 | 7.0 | 5838 | 0.1559 | 0.9322 | 0.9429 | 0.9375 | 0.9780 |
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+ | 0.0024 | 8.0 | 6672 | 0.1733 | 0.9321 | 0.9438 | 0.9379 | 0.9779 |
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+ | 0.0009 | 9.0 | 7506 | 0.1765 | 0.9347 | 0.9468 | 0.9407 | 0.9784 |
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+ | 0.0002 | 10.0 | 8340 | 0.1763 | 0.9360 | 0.9472 | 0.9416 | 0.9789 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.14.0.dev0
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+ - Pytorch 1.10.0
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+ - Datasets 1.16.1
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+ - Tokenizers 0.10.3
config.json ADDED
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+ {
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+ "_name_or_path": "fav-kky/FERNET-CC_sk",
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+ "architectures": [
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+ "BertForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "finetuning_task": "ner",
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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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+ },
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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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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.14.0.dev0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 100000
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+ }
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special_tokens_map.json ADDED
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "special_tokens_map_file": "cache/ee3ccb4bc8ed4c3c3cb32c5f954c6703aec7926e320ececc0fc9dcec6dd538ba.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "fav-kky/FERNET-CC_sk", "tokenizer_class": "BertTokenizer"}
vocab.txt ADDED
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