cronoik
GitHub
parent
e977ed2142
commit
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "YKdSeUmVSXah",
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"pycharm": {
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"is_executing": false,
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"name": "#%% md\n"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "TFHTP6CFSXai",
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"pycharm": {
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"name": "#%% md\n"
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}
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@ -78,6 +80,7 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "KnT3Jn6fSXai",
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"pycharm": {
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"is_executing": false,
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"name": "#%% code\n"
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@ -92,8 +95,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "UIQGDTIDSXai",
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"outputId": "9851454a-c898-4fba-a389-9b16462a27c1",
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"pycharm": {
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"is_executing": false,
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"name": "#%% code\n"
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@ -103,11 +111,13 @@
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{
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"data": {
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"text/plain": [
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"<torch.autograd.grad_mode.set_grad_enabled at 0x7f10b441e890>"
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"<torch.autograd.grad_mode.set_grad_enabled at 0x7f9c03e5b3c8>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"output_type": "execute_result"
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}
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],
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@ -120,8 +130,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {
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"id": "1xMDTHQXSXai",
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"pycharm": {
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"is_executing": false,
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"name": "#%% code\n"
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@ -140,6 +151,7 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "l6EcynhYSXai",
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"pycharm": {
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"name": "#%% md\n"
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}
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@ -152,8 +164,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "XgkFg52fSXai",
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"outputId": "94b569d4-5415-4327-f39e-c9541b0a53e0",
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"pycharm": {
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"is_executing": false,
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"name": "#%% code\n"
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@ -189,13 +206,17 @@
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"print(\"Tokens PyTorch: {}\".format(tokens_pt))\n",
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"\n",
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"# Now we're ready to go through BERT with out input\n",
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"outputs, pooled = model(tokens_pt)\n",
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"print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
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"outputs = model(tokens_pt)\n",
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"last_hidden_state = outputs.last_hidden_state\n",
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"pooler_output = outputs.pooler_output\n",
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"\n",
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"print(\"Token wise output: {}, Pooled output: {}\".format(last_hidden_state.shape, pooler_output.shape))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "lBbvwNKXSXaj",
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"pycharm": {
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"name": "#%% md\n"
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}
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@ -215,6 +236,7 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "DCxuDWH2SXaj",
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"pycharm": {
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"name": "#%% md\n"
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}
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@ -227,8 +249,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "sgcNCdXUSXaj",
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"outputId": "af2fb928-7c17-475b-cf81-89cfc4b1d9e5",
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"pycharm": {
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"is_executing": false,
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"name": "#%% code\n"
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@ -260,13 +287,18 @@
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"for key, value in tokens_pt2.items():\n",
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" print(\"{}:\\n\\t{}\".format(key, value))\n",
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"\n",
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"outputs2, pooled2 = model(**tokens_pt2)\n",
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"print(\"Difference with previous code: ({}, {})\".format((outputs2 - outputs).sum(), (pooled2 - pooled).sum()))"
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"outputs2 = model(**tokens_pt2)\n",
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"last_hidden_state2 = outputs2.last_hidden_state\n",
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"pooler_output2 = outputs2.pooler_output\n",
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"\n",
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"print(\"Difference with previous code: ({}, {})\".format((last_hidden_state2 - last_hidden_state).sum(), (pooler_output2 - pooler_output).sum()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"id": "gC-7xGYPSXal"
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},
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"source": [
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"As you can see above, calling the tokenizer provides a convenient way to generate all the required parameters\n",
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"that will go through the model. \n",
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@ -279,8 +311,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Pl2HIcwDSXal",
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"outputId": "22e5d010-47a9-4a12-a67d-208e5016157e",
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"pycharm": {
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"is_executing": false
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}
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@ -320,8 +357,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "1NtvWOgzSXam",
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"outputId": "e66c47d0-e106-408d-d01c-9ac194ca3ec6",
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"pycharm": {
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"is_executing": false
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}
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@ -358,7 +400,9 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"id": "vkRYm2HESXan"
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},
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"source": [
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"## Frameworks interoperability\n",
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"\n",
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@ -370,8 +414,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {
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"id": "Kubwm-wJSXan",
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"pycharm": {
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"is_executing": false
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}
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@ -387,8 +432,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 12,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lJ13tlzOSXan",
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"outputId": "1e4ac151-a8fc-4b34-946a-da0bc44ed0e6",
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"pycharm": {
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"is_executing": false
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}
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@ -398,8 +448,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"output differences: 1.6236e-05\n",
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"pooled differences: -1.3039e-08\n"
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"last_hidden_state differences: 1.0094e-05\n",
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"pooler_output differences: 7.2969e-07\n"
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]
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}
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],
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@ -413,13 +463,14 @@
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"\n",
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"# Models outputs 2 values (The value for each tokens, the pooled representation of the input sentence)\n",
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"# Here we compare the output differences between PyTorch and TensorFlow.\n",
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"for name, o_tf, o_pt in zip([\"output\", \"pooled\"], output_tf, output_pt):\n",
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" print(\"{} differences: {:.5}\".format(name, (o_tf.numpy() - o_pt.numpy()).sum()))"
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"for name in [\"last_hidden_state\", \"pooler_output\"]:\n",
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" print(\"{} differences: {:.5}\".format(name, (output_tf[name].numpy() - output_pt[name].numpy()).sum()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CQf_fpApSXao",
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"pycharm": {
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"name": "#%% md\n"
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}
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@ -442,21 +493,85 @@
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 185,
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"referenced_widgets": [
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"fcffccb427714665bec7d621d00d4ce3",
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"7aa02ef05fe64489ad6c969dd92d1b07",
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"817f53e3fa5c43e29ad1b4410c3df7db",
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"50a441d7a43c4a809a09505b36e83375",
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"497ba6a585a147459f1346c0661d5c94",
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"cf319210c8134cdba487525c49e4813b",
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"id": "wfxMOXb-SXao",
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"outputId": "fa667556-fbf2-4c86-fc7e-9e3d3ec9da88",
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [
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{
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"data": {
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"model_id": "fcffccb427714665bec7d621d00d4ce3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=411.0, style=ProgressStyle(description_…"
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]
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},
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"metadata": {
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"tags": []
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 232 ms, sys: 0 ns, total: 232 ms\n",
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"Wall time: 21.1 ms\n",
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"CPU times: user 511 ms, sys: 0 ns, total: 511 ms\n",
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"Wall time: 43.9 ms\n"
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"\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=263273408.0, style=ProgressStyle(descri…"
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]
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},
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"metadata": {
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"tags": []
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"CPU times: user 64.4 ms, sys: 0 ns, total: 64.4 ms\n",
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"Wall time: 72.9 ms\n",
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"CPU times: user 130 ms, sys: 124 µs, total: 130 ms\n",
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"Wall time: 131 ms\n"
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]
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}
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],
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@ -476,7 +591,9 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"id": "7lSIc7FbSXao"
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},
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"source": [
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"## Community provided models\n",
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"\n",
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@ -490,8 +607,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 16,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "cxLYnadGSXao",
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"outputId": "70ab584a-e795-490a-8c6a-06e034b3df3d",
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"pycharm": {
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"is_executing": false
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}
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"Tokens (str) : ['[CLS]', 'Hug', '##ging', 'Fac', '##e', 'ist', 'eine', 'französische', 'Firma', 'mit', 'Sitz', 'in', 'New', '-', 'York', '.', '[SEP]']\n",
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"Tokens (attn_mask): [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
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"\n",
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"Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
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"Token wise output: torch.Size([1, 17, 768]), Pooled output: torch.Size([1, 768])\n"
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]
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}
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],
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"print(\"Tokens (attn_mask): {}\".format(de_input['attention_mask'].tolist()[0]))\n",
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"print()\n",
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"\n",
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"output_de, pooled_de = de_bert(**de_input)\n",
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"outputs_de = de_bert(**de_input)\n",
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"last_hidden_state_de = outputs_de.last_hidden_state\n",
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"pooler_output_de = outputs_de.pooler_output\n",
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"\n",
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"print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
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"print(\"Token wise output: {}, Pooled output: {}\".format(last_hidden_state_de.shape, pooler_output_de.shape))"
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]
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}
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],
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"metadata": {
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"colab": {
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"name": "02-transformers.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.7.3"
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},
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"pycharm": {
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"stem_cell": {
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@ -555,8 +683,502 @@
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},
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"source": []
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}
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},
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"widgets": {
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"description_width": "initial"
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"_model_module_version": "1.5.0",
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"_model_name": "HTMLModel",
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"_view_count": null,
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"_view_module": "@jupyter-widgets/controls",
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"_view_module_version": "1.5.0",
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"_view_name": "HTMLView",
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|
||||
"top": null,
|
||||
"visibility": null,
|
||||
"width": null
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue