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"execution_count": 5, + "metadata": { + "id": "oTnrQPb6Enmz" + }, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, AutoModelForSequenceClassification" + ] + }, + { + "cell_type": "code", + "source": [ + "checkpoint_dir =\"/content/drive/MyDrive/Amazon_shoe_review/checkpoint-4221\"" + ], + "metadata": { + "id": "vdNu-QDwE_Ny" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#load the model\n", + "model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir)" + ], + "metadata": { + "id": "5JjwLdTtGidW" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#load the tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir)" + ], + "metadata": { + "id": "fAVv8ynCGnzV" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Make Prediction" + ], + "metadata": { + "id": "XWEAs5vvMBTJ" + } + }, + { + "cell_type": "code", + "source": [ + "raw_inputs = [\n", + " \"The shoes fell to pieces after a few days, why did I buy them??\",\n", + " \"This is a very comfortable pair of shoes, I'm super happy with it.\",\n", + "]\n", + "\n", + "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")" + ], + "metadata": { + "id": "dMwgoenzG7Mm" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "inputs" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_nNiyKUnM110", + "outputId": "7d84b141-ad58-4e3f-d904-0b55bf80d4aa" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'input_ids': tensor([[ 101, 1996, 6007, 3062, 2000, 4109, 2044, 1037, 2261, 2420, 1010, 2339,\n", + " 2106, 1045, 4965, 2068, 1029, 1029, 102],\n", + " [ 101, 2023, 2003, 1037, 2200, 6625, 3940, 1997, 6007, 1010, 1045, 1005,\n", + " 1049, 3565, 3407, 2007, 2009, 1012, 102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "outputs = model(inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"])" + ], + "metadata": { + "id": "2dDzuRm0NaU1" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "outputs" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B0kV8LaTN2Y1", + "outputId": "ec611a29-e4ff-4b86-8b65-898dd3e9ecf0" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SequenceClassifierOutput(loss=None, logits=tensor([[ 3.5638, 1.9136, 0.1254, -2.6943, -3.5762],\n", + " [-2.6418, -2.9041, -1.0317, 2.3485, 3.4736]],\n", + " grad_fn=), hidden_states=None, attentions=None)" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "These values are not probabilities. These are actual activations of the five neurons on the last layer of the model. We apply the softmax function to the output logits, and see that we get the same top probability as with the pipeline above." + ], + "metadata": { + "id": "iJCu6w4MR3c-" + } + }, + { + "cell_type": "code", + "source": [ + "import torch" + ], + "metadata": { + "id": "KwqOAPHVOUPM" + }, + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "probs = torch.softmax(outputs.logits, axis=1)" + ], + "metadata": { + "id": "pukqXay2Sclp" + }, + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "probs" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e1VjlkknSpCJ", + "outputId": "c91e0ea0-558e-4001-b6ee-c9743994e582" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor([[8.1511e-01, 1.5651e-01, 2.6178e-02, 1.5609e-03, 6.4616e-04],\n", + " [1.6486e-03, 1.2683e-03, 8.2492e-03, 2.4233e-01, 7.4650e-01]],\n", + " grad_fn=)" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Push the model to the Hub" + ], + "metadata": { + "id": "eVV9sB4fTJr-" + } + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login" + ], + "metadata": { + "id": "bchXwDFdSqHl" + }, + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 163, + "referenced_widgets": [ + "9a860b222cc545948c8536bea500b2ef", + "3724a5a0391943e49fcae7293ec25240", + "f55c67737cd64e53b9048b96481f8c23", + "849e859900864870bfaeb4c4bcaa82fa", + "877ec821f936437cbc3d975a0578d37a", + "d6bc60247b744f5c9c8ed5de8ef282b6", + "97f5977873814d83a8b91b4728825811", + "8240e7b585a247968564e65b8b2ae495", + "7095b85d2c28418c9eca2aba4ce18f93", + "61c3503796fc4889972a9a34c6efaf44", + "4b3c66de2b7047ac9eae074732963f81", + "44909bad7efa4c05a03d4b3e6d00754a", + "ac08e2d31e0b40ad8463571660b3646d", + "e9df0fc84fc84f2ab7b62d5449cadb21", + "b7931abd7c754dd6b8faad7cd6dba98d", + "5442dffe86164117856da091173181ef", + "1b0f19fef2844e65b399cc50ed33267a", + "f87885f3e85f40cf91cb12add91be12b", + "33ee66bd4d5c40c68f5ab9c24900fc65", + "04082409cdeb449db932f1f72715054a", + "8ddf2ff906d148fab87b8910cb125f31", + "0583c792481c47da8353cf6c8719b397", + "a6e3233f666440f684c2226631368b3b", + "a0ac1fe640614aafb0ebadfb4834b558", + "afda2514e10d4fe2b190b5ef98068abd", + "0b928db2bc4244baab2d819c31d5e2a8", + "ff48f8edac4143b1b9e30fe1add88854", + "a882c72aaafe450896e0aff9f93e95fe", + "f688c60ae2b14839bcc0c014a1d4ed2c", + "315090c104bd4ad28f7908a45903c090", + "599ae65a4c6746d791f54c86daa22224", + "958055b4b77c4efe87b64a30063e1b6c" + ] + }, + "id": "9jl7N7DrXgiE", + "outputId": "654f86e8-befe-42b6-f61f-b7391d2a9c52" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='