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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ju-bezdek/conll2003-SK-NER |
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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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base_model: gerulata/slovakbert |
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model-index: |
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- name: outputs |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: ju-bezdek/conll2003-SK-NER |
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type: ju-bezdek/conll2003-SK-NER |
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args: conll2003-SK-NER |
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metrics: |
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- type: precision |
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value: 0.8189727994593682 |
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name: Precision |
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- type: recall |
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value: 0.8389581169955002 |
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name: Recall |
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- type: f1 |
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value: 0.8288450029922203 |
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name: F1 |
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- type: accuracy |
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value: 0.9526157920337243 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# outputs |
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This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset. |
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It achieves the following results on the evaluation (validation) set: |
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- Loss: 0.1752 |
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- Precision: 0.8190 |
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- Recall: 0.8390 |
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- F1: 0.8288 |
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- Accuracy: 0.9526 |
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## Model description |
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More information needed |
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## Code example |
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```python: |
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from transformers import pipeline, AutoModel, AutoTokenizer |
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from spacy import displacy |
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import os |
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model_path="ju-bezdek/slovakbert-conll2003-sk-ner" |
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aggregation_strategy="max" |
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ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy) |
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input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax" |
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ner_ents = ner_pipeline(input_sentence) |
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print(ner_ents) |
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ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0] |
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options = {"ents":ent_group_labels} |
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dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents] |
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displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True) |
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``` |
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### Result: |
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<div> |
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<span class="tex2jax_ignore"><div class="entities" style="line-height: 2.5; direction: ltr"> |
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<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Ruský |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">MISC</span> |
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</mark> |
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premiér |
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<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Viktor Černomyrdin |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> |
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</mark> |
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v piatok povedal, že prezident |
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<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Boris Jeľcin, |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> |
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</mark> |
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, ktorý je na dovolenke mimo |
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<mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Moskvy |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> |
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</mark> |
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, podporil mierový plán šéfa bezpečnosti |
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<mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Alexandra Lebedu |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> |
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</mark> |
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pre |
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<mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Čečensko, |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> |
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</mark> |
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uviedla tlačová agentúra |
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<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
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Interfax |
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<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORG</span> |
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</mark> |
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</div></span> |
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</div> |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 | |
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| 0.1663 | 2.0 | 1756 | 0.2370 | 0.7775 | 0.8090 | 0.7929 | 0.9394 | |
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| 0.1251 | 3.0 | 2634 | 0.2289 | 0.7732 | 0.8029 | 0.7878 | 0.9385 | |
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| 0.0984 | 4.0 | 3512 | 0.2818 | 0.7294 | 0.8189 | 0.7715 | 0.9294 | |
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| 0.0808 | 5.0 | 4390 | 0.3138 | 0.7615 | 0.7900 | 0.7755 | 0.9326 | |
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| 0.0578 | 6.0 | 5268 | 0.3072 | 0.7548 | 0.8222 | 0.7871 | 0.9370 | |
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| 0.0481 | 7.0 | 6146 | 0.2778 | 0.7897 | 0.8156 | 0.8025 | 0.9408 | |
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| 0.0414 | 8.0 | 7024 | 0.3336 | 0.7695 | 0.8201 | 0.7940 | 0.9389 | |
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| 0.0268 | 9.0 | 7902 | 0.3294 | 0.7868 | 0.8140 | 0.8002 | 0.9409 | |
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| 0.0204 | 10.0 | 8780 | 0.3693 | 0.7657 | 0.8239 | 0.7938 | 0.9376 | |
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| 0.016 | 11.0 | 9658 | 0.3816 | 0.7932 | 0.8242 | 0.8084 | 0.9425 | |
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| 0.0108 | 12.0 | 10536 | 0.3607 | 0.7929 | 0.8256 | 0.8089 | 0.9431 | |
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| 0.0078 | 13.0 | 11414 | 0.3980 | 0.7915 | 0.8240 | 0.8074 | 0.9423 | |
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| 0.0062 | 14.0 | 12292 | 0.4096 | 0.7995 | 0.8247 | 0.8119 | 0.9436 | |
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| 0.0035 | 15.0 | 13170 | 0.4177 | 0.8006 | 0.8251 | 0.8127 | 0.9438 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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