This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (RoBERTa: A Robustly Optimized BERT Pretraining Approach) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the **AUC-ROC** of 0.98 and **F1-score** of 0.76. This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve the public good.
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Check out a demo of the model here. This model is a fine-tuned version of NbAiLab/nb-bert-base for Named Entity Recognition for Danish, Norwegian (both Bokmål and Nynorsk), Swedish, Icelandic and Faroese. It has been fine-tuned on the concatenation of DaNE, NorNE, SUC 3.0 and the Icelandic and Faroese parts of the WikiANN dataset. It also works reasonably well on English sentences, given the fact that the pretrained model is also trained on English data along with Scandinavian languages. The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here:
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This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1197 - Mse: 1.1197 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0
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See more details in the SamLowe/roberta-base-go_emotions model card for the increases possible through selecting label-specific thresholds to maximise F1 scores, or another metric. See more details in the SamLowe/roberta-base-go_emotions model card for the increases possible through selecting label-specific thresholds to maximise F1 scores, or another metric. Optimum library has equivalents (starting `ORT`) for the main Transformers classes, so these models can be used with the familiar constructs. The only extra property needed is `file_name` on the model creation, which in the below example specifies the quantized (INT8) model.
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This model is the result of a project entitled Towards Controllable Biases in Language Generation. It consists of a BERT classifier (no ensemble) trained on 1.7K samples of biased language. *Regard* measures language polarity towards and social perceptions of a demographic (compared to sentiment, which only measures overall language polarity).
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for file in train.txt dev.txt test.txt labels.txt do wget https://schweter.eu/storage/turkish-bert-wikiann/$file done cd .. It will download the pre-processed datasets with training, dev and test splits and put them in a tr-data folder. Run pre-training After downloading the dataset, pre-training can be started. Just set the following environment variables:
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This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased > 05/10/2020 17:00:43 - INFO - transformers.trainer - \*\*\*\*\* Running Evaluation \*\*\*\*\* > 05/10/2020 17:00:43 - INFO - transformers.trainer - Num examples = 7999 > 05/10/2020 17:00:43 - INFO - transformers.trainer - Batch size = 8 > Evaluation: 100% 1000/1000 [00:34 05/10/2020 17:01:17 - INFO - \_\_main__ - \*\*\*\*\* Eval results sst-2 \*\*\*\*\* > 05/10/2020 17:01:17 - INFO - \_\_main__ - acc = 0.9539942492811602 > 05/10/2020 17:01:17 - INFO - \_\_main__ - loss = 0.16348013816401363
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
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German FinBERT is a BERT language model focusing on the financial domain within the German language. In my paper, I describe in more detail the steps taken to train the model and show that it outperforms its generic benchmarks for finance specific downstream tasks. This model is the pre-trained from scratch version of German FinBERT, after fine-tuning on a translated version of the financial news phrase bank of Malo et al. (2013). The data is available here. **Author** Moritz Scherrmann **Paper:** here **Architecture:** BERT base **Language:** German **Specialization:** Financial sentiment **Base model:** German_FinBert_SC
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