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--- |
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model_creators: |
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- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan |
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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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- plo_dfiltered_config |
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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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model-index: |
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- name: roberta-base-finetuned-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: plo_dfiltered_config |
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type: plo_dfiltered_config |
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args: PLODfiltered |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9644756447594547 |
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- name: Recall |
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type: recall |
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value: 0.9583209148378798 |
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- name: F1 |
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type: f1 |
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value: 0.9613884293804785 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9575894768204436 |
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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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# roberta-base-finetuned-ner |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the plo_dfiltered_config dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1148 |
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- Precision: 0.9645 |
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- Recall: 0.9583 |
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- F1: 0.9614 |
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- Accuracy: 0.9576 |
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## Model description |
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RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means |
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it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of |
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publicly available data) with an automatic process to generate inputs and labels from those texts. |
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More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model |
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randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict |
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the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one |
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after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to |
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learn a bidirectional representation of the sentence. |
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This way, the model learns an inner representation of the English language that can then be used to extract features |
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard |
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classifier using the features produced by the BERT model as inputs. |
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## Intended uses & limitations |
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More information needed |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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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: 6 |
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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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| 0.1179 | 1.99 | 7000 | 0.1130 | 0.9602 | 0.9517 | 0.9559 | 0.9522 | |
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| 0.0878 | 3.98 | 14000 | 0.1106 | 0.9647 | 0.9564 | 0.9606 | 0.9567 | |
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| 0.0724 | 5.96 | 21000 | 0.1149 | 0.9646 | 0.9582 | 0.9614 | 0.9576 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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