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},
"cells": [
{
"cell_type": "markdown",
"source": [
"## Load Checkpoint/the model"
],
"metadata": {
"id": "RgqfXPJtEz3l"
}
},
{
"cell_type": "code",
"source": [
"import transformers"
],
"metadata": {
"id": "45X8XZM1Fn23"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"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,
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},
"id": "9jl7N7DrXgiE",
"outputId": "654f86e8-befe-42b6-f61f-b7391d2a9c52"
},
"execution_count": 30,
"outputs": [
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"output_type": "display_data",
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