Improve the presentation of outputs in frontend

README.md

@@ -113,7 +113,8 @@ In our approach, for each comma from the prediction text obtained from the model
  * If a comma from ground truth is not predicted, it counts as a false negative.
 
 ## Training
-The fine-tuned model can be found [here](https://huggingface.co/klasocki/roberta-large-lora-ner-comma-fixer).
+The fine-tuned model is the [klasocki/roberta-large-lora-ner-comma-fixer](https://huggingface.co/klasocki/roberta-large-lora-ner-comma-fixer).
+Further description can be found in the model card.
 
 To compare with the baseline, we fine-tune the same model, RoBERTa large, on the wikitext English dataset.
 We use a similar approach, where we treat comma-fixing as a NER problem, and for each token predict whether a comma 

commafixer/src/baseline.py

@@ -1,7 +1,7 @@
 from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline, NerPipeline
 import re
 
-from commafixer.src.comma_fixer_interface import CommaFixerInterface
+from comma_fixer_interface import CommaFixerInterface
 
 
 class BaselineCommaFixer(CommaFixerInterface):

commafixer/src/fixer.py

@@ -1,9 +1,9 @@
 from peft import PeftConfig, PeftModel
-from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline, NerPipeline, RobertaTokenizerFast
+from transformers import AutoTokenizer, AutoModelForTokenClassification, RobertaTokenizerFast
 import nltk
 import re
 
-from commafixer.src.comma_fixer_interface import CommaFixerInterface
+from comma_fixer_interface import CommaFixerInterface
 
 
 class CommaFixer(CommaFixerInterface):
@@ -11,7 +11,7 @@ class CommaFixer(CommaFixerInterface):
     A wrapper class for the fine-tuned comma fixer model.
     """
 
-    def __init__(self, device=-1):
+    def __init__(self):
         self.id2label = {0: 'O', 1: 'B-COMMA'}
         self.label2id = {'O': 0, 'B-COMMA': 1}
         self.model, self.tokenizer = self._load_peft_model()

notebooks/evaluation.ipynb

@@ -3281,7 +3281,8 @@
   {
    "cell_type": "code",
    "source": [
-    "# comma_fixer.model = comma_fixer.model.cuda()"
+    "# comma_fixer.model = comma_fixer.model.cuda() # TODO make this work and evaluate on test in the notebook as well. In\n",
+    "#  training eval on test was ~ same F1"
    ],
    "metadata": {
     "id": "ePP_WzS7XeYC"

static/index.html

@@ -37,7 +37,8 @@
                     value="This is however a very bad, and terrible sentence grammatically that is."
             />
             <button id="comma-fixing-submit">Submit</button>
-            <p class="comma-fixing-output"></p>
+            <p class="comma-fixing-main-output"></p>
+            <p class="comma-fixing-baseline-output"></p>
         </form>
     </section>
 </main>

static/script.js

@@ -22,9 +22,11 @@ commaFixingForm.addEventListener("submit", async (event) => {
     event.preventDefault();
 
     const commaFixingInput = document.getElementById("comma-fixing-input");
-    const commaFixingParagraph = document.querySelector(".comma-fixing-output");
+    const commaFixingParagraph = document.querySelector(".comma-fixing-main-output");
+    const commaFixingBaselineParagraph = document.querySelector(".comma-fixing-baseline-output");
 
     const fixed = await fixCommas(commaFixingInput.value);
 
-    commaFixingParagraph.textContent = `Our model: ${fixed.main}\n\nBaseline model: ${fixed.baseline}`
+    commaFixingParagraph.textContent = `Our model: ${fixed.main}`
+    commaFixingBaselineParagraph.textContent = `Baseline model: ${fixed.baseline}`
 });