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README.md

@@ -1,3 +1,56 @@
 ---
+language:
+- 'no'
+- nb
+- nn
 license: cc-by-4.0
+pipeline_tag: token-classification
 ---
+# Targeted Sentiment Analysis model for Norwegian text
+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).  
+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". 
+
+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).
+
+
+## Quick start
+You can use this model in your scripts as follows:
+```>>> from transformers import pipeline
+>>> origin = "ltg/norbert3-large_TSA"
+>>> trust_remote = "norbert3" in origin.lower()
+>>> text = "Hans hese , litt såre stemme kler bluesen , men denne platen kommer neppe til å bli blant hans største kommersielle suksesser ."
+>>> if trust_remote: # Downloads configurations for norbert3
+...     pipe = transformers.pipeline( "token-classification",
+...                                 aggregation_strategy='first',
+...                                 model = origin,
+...                                 trust_remote_code=trust_remote,
+...                                 tokenizer = AutoTokenizer.from_pretrained(origin)
+...                 )
+...     preds = pipe(text)
+...     for p in preds:
+...         print(p)
+
+{'entity_group': 'targ-Positive', 'score': 0.6990814, 'word': ' Hans hese , litt såre stemme', 'start': 0, 'end': 28}
+{'entity_group': 'targ-Negative', 'score': 0.5721016, 'word': ' platen', 'start': 53, 'end': 60}
+```  
+
+
+
+## Training hyperparameters
+- per_device_train_batch_size: 64
+- per_device_eval_batch_size: 8
+- learning_rate: 1e-05
+- gradient_accumulation_steps: 1
+- num_train_epochs: 24 (best epoch 18)
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+
+## Evaluation
+```               precision    recall  f1-score   support
+
+targ-Negative     0.4648    0.3143    0.3750       210
+targ-Positive     0.5097    0.6019    0.5520       525
+
+    micro avg     0.5013    0.5197    0.5104       735
+    macro avg     0.4872    0.4581    0.4635       735
+ weighted avg     0.4969    0.5197    0.5014       735
+ ```