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--- |
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language: |
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- 'no' |
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- nb |
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- nn |
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license: cc-by-4.0 |
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pipeline_tag: token-classification |
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--- |
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# Targeted Sentiment Analysis model for Norwegian text |
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This model is a fine-tuned version of [ltg/norbert3-large](https://huggingface.co/ltg/norbert3-large) For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable [on github](https://github.com/egilron/seq-label.git). |
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In TSA, we identify sentiment targets, "That what is spoken positively or negatively about" in each sentence. Our models performs the task through sequence labeling, AKA "token classification". |
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The dataset used for fine-tuning is [ltg/norec_tsa](https://huggingface.co/datasets/ltg/norec_tsa), at its defaul settings, were sentiment targets are labeled as either "targ-Positive" or "targ-Negative". The norec_tsa dataset is derived from the [NoReC_fine dataset](https://github.com/ltgoslo/norec_fine). |
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## Quick start |
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You can use this model in your scripts as follows: |
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```>>> from transformers import pipeline |
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>>> origin = "ltg/norbert3-large_TSA" |
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>>> trust_remote = "norbert3" in origin.lower() |
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>>> text = "Hans hese , litt såre stemme kler bluesen , men denne platen kommer neppe til å bli blant hans største kommersielle suksesser ." |
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>>> if trust_remote: # Downloads configurations for norbert3 |
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... pipe = transformers.pipeline( "token-classification", |
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... aggregation_strategy='first', |
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... model = origin, |
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... trust_remote_code=trust_remote, |
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... tokenizer = AutoTokenizer.from_pretrained(origin) |
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... ) |
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... preds = pipe(text) |
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... for p in preds: |
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... print(p) |
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{'entity_group': 'targ-Positive', 'score': 0.6990814, 'word': ' Hans hese , litt såre stemme', 'start': 0, 'end': 28} |
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{'entity_group': 'targ-Negative', 'score': 0.5721016, 'word': ' platen', 'start': 53, 'end': 60} |
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``` |
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## Training hyperparameters |
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- per_device_train_batch_size: 64 |
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- per_device_eval_batch_size: 8 |
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- learning_rate: 1e-05 |
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- gradient_accumulation_steps: 1 |
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- num_train_epochs: 24 (best epoch 18) |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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## Evaluation |
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``` precision recall f1-score support |
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targ-Negative 0.4648 0.3143 0.3750 210 |
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targ-Positive 0.5097 0.6019 0.5520 525 |
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micro avg 0.5013 0.5197 0.5104 735 |
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macro avg 0.4872 0.4581 0.4635 735 |
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weighted avg 0.4969 0.5197 0.5014 735 |
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``` |
