Update README.md

README.md

@@ -8,3 +8,54 @@ datasets:
 - ltg/norec_sentence
 pipeline_tag: text-classification
 ---
+
+# Sentence-level Sentiment Analysis model for Norwegian text
+This model is a fine-tuned version of [ltg/norbert3-base](https://huggingface.co/ltg/norbert3-base) for text classification.
+
+## Training data
+The dataset used for fine-tuning is [ltg/norec_sentence](https://huggingface.co/datasets/ltg/norec_sentence), the `mixed` subset with four sentement categories:  
+```
+[0]:  Negative,  
+[1]:  Positive,  
+[2]:  Neutral
+[0,1]: Mixed
+```
+
+## Quick start
+You can use this model for inference as follows:
+```
+>>> from transformers import pipeline
+>>> origin = "ltg/norbert3-large_sentence-sentiment"
+>>> pipe = transformers.pipeline( "text-classification",
+...                             model = origin,
+...                             trust_remote_code=origin.startswith("ltg/norbert3"),
+...                             config= origin,
+...                             tokenizer = AutoTokenizer.from_pretrained(origin)
+...             )
+>>> preds = pipe(["Hans hese, litt såre stemme kler bluesen, men denne platen kommer neppe til å bli blant hans største kommersielle suksesser.",
+...              "Borten-regjeringen gjorde ikke jobben sin." ])
+>>> for p in preds:
+...     print(p)
+```
+Output:
+```
+The model 'NorbertForSequenceClassification' is not supported for text-classification. Supported models are ['AlbertForSequenceClassification', ...
+{'label': 'Mixed', 'score': 0.7435498237609863}
+{'label': 'Negative', 'score': 0.765734851360321}
+```
+
+## Training hyperparameters
+- per_device_train_batch_size: 32
+- learning_rate: 1e-05
+- gradient_accumulation_steps: 1
+- num_train_epochs: 10 (best epoch 2)
+
+## Evaluation
+| Metric          |       F1 | |
+|:----------------|---------:|----:|
+| Negative_F1     | 0.670241 |<img width=400/> |
+| Positive_F1     | 0.832918 | |
+| Neutral_F1      | 0.850082 | |
+| Mixed_F1        | 0.580645 | |
+| Weighted_avg_F1 | 0.799663 | |
+