"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [6250/6250 1:22:11, Epoch 10/10]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Accuracy | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 0.190000 | \n",
+ " 0.181103 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.197300 | \n",
+ " 0.180947 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.175800 | \n",
+ " 0.181908 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.189000 | \n",
+ " 0.190711 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.186400 | \n",
+ " 0.188646 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.190000 | \n",
+ " 0.183267 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 0.180000 | \n",
+ " 0.184369 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 0.186300 | \n",
+ " 0.184290 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 0.180600 | \n",
+ " 0.180573 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 0.176500 | \n",
+ " 0.181132 | \n",
+ " {'accuracy': 0.9558} | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(\n",
+ "Trainer is attempting to log a value of \"{'accuracy': 0.9558}\" of type for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=6250, training_loss=0.18548879638671875, metrics={'train_runtime': 4952.1965, 'train_samples_per_second': 20.193, 'train_steps_per_second': 1.262, 'total_flos': 1.34512914432e+16, 'train_loss': 0.18548879638671875, 'epoch': 10.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.to(\"cpu\")\n",
+ "\n",
+ "# Define a list of example texts for testing\n",
+ "text_list = [\n",
+ " \"It was good.\",\n",
+ " \"Not a fan, don't recommend.\",\n",
+ " \"Better than the first one.\",\n",
+ " \"This is not worth watching even once.\",\n",
+ " \"This one is a pass.\"\n",
+ "]\n",
+ "\n",
+ "print(\"Trained model predictions:\")\n",
+ "print(\"--------------------------\")\n",
+ "\n",
+ "for text in text_list:\n",
+ " inputs = tokenizer(text, return_tensors=\"pt\").to(\"cpu\") # Ensure correct input format\n",
+ "\n",
+ " with torch.no_grad(): # Disable gradient tracking for inference\n",
+ " logits = model(**inputs).logits\n",
+ " predictions = logits.argmax(dim=1) # Get the predicted label index\n",
+ "\n",
+ " print(f\"{text} - {id2label[predictions.item()]}\") # Convert tensor to int and map to label\n",
+ "\n",
+ "from huggingface_hub import notebook_login\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187,
+ "referenced_widgets": [
+ "5a3d0533f43a41629ac7f516f6825e4f",
+ "de31ace8df05407ea685b25751e28e34",
+ "875c330036cf4cb7907f024d1fef483d",
+ "263d93ece7fa45ec9f476cefc04d201f",
+ "57b6bd3c3b9f4bf9a7fd77ffc48d1cde",
+ "178c2719f1ab472299642a7d666cbb88",
+ "225ecdd9259a4833a37c0afeb59a730f",
+ "d498a65ce5c64ee892a52c2b9c9ba4c1",
+ "3318fcb8754d435bb563843b9d5152b6",
+ "6a52806c23e64d63bc0d25c43006854e",
+ "cebdbcc607e84ff5acf2787ccc917405",
+ "4da9e924fee74264825cd517fe3984fe",
+ "6695ffe2a70b4ee39f2388b9a0e68ae5",
+ "1513588f658b4ba3b4b5b1e93eda9c7b",
+ "3a6329fdb5bb458f8683426a72530ffe",
+ "152aee1ee2504fe596fdacb8d645ef88",
+ "f8e71a10fab845acab1860be7eba17b6",
+ "e2793240fd6c4ec2be8bcafeda9952f2",
+ "6247c2d68b144784a35623c1db5d9df3",
+ "9ee6fbc327ff45988f11b8356f6c588f",
+ "940632c65a28437eb098724f8bd16316",
+ "e53bd59b1e614b91bd5b461f1878e0fc",
+ "f86e79716c7c4b5497a4298e84f03a98"
+ ]
+ },
+ "id": "fCwsuQvov-Ta",
+ "outputId": "3c1b0cfc-74cb-4963-9dc3-175c5f8ec09d"
+ },
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Trained model predictions:\n",
+ "--------------------------\n",
+ "It was good. - High Revenue\n",
+ "Not a fan, don't recommend. - High Revenue\n",
+ "Better than the first one. - High Revenue\n",
+ "This is not worth watching even once. - High Revenue\n",
+ "This one is a pass. - High Revenue\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='