Update MRPC example notebook with new code cells

notebooks/mrpc_example.ipynb

@@ -11,12 +11,16 @@
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "!pip install -q transformers datasets evaluate scikit-learn accelerate"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "import torch\n",
     "from datasets import load_dataset\n",
@@ -33,10 +37,13 @@
     "\n",
     "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
     "print(device)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "dataset = load_dataset('glue','mrpc')\n",
     "tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')\n",
@@ -54,10 +61,13 @@
     "val_loader   = DataLoader(val, batch_size=16)\n",
     "\n",
     "metric = evaluate.load('glue','mrpc')"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "def eval_model(model):\n",
     "    model.eval()\n",
@@ -70,10 +80,13 @@
     "            p=model(input_ids=x,attention_mask=m).logits.argmax(-1)\n",
     "            preds.extend(p.cpu().numpy()); labels.extend(y.cpu().numpy())\n",
     "    return metric.compute(predictions=preds,references=labels)['f1']"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "# BASELINE\n",
     "model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
@@ -90,10 +103,13 @@
     "\n",
     "f1_base = eval_model(model)\n",
     "print('Baseline F1:', round(f1_base,3))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "# ORBITAL\n",
     "model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
@@ -118,18 +134,32 @@
     "\n",
     "f1_orb = eval_model(model)\n",
     "print('Orbital F1:', round(f1_orb,3))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
+   "metadata": {},
    "source": [
     "print('\\nBaseline:', round(f1_base,3))\n",
     "print('Orbital:', round(f1_orb,3))\n",
     "print('Delta:', round(f1_orb-f1_base,3))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }