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| { | |
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| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", | |
| "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", | |
| "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", | |
| "You will be able to reuse this secret in all of your notebooks.\n", | |
| "Please note that authentication is recommended but still optional to access public models or datasets.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "config.json: 0%| | 0.00/570 [00:00<?, ?B/s]" | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| "data": { | |
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| "model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]" | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n", | |
| "\n", | |
| "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n", | |
| "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import TFBertForSequenceClassification\n", | |
| "model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf" | |
| ], | |
| "metadata": { | |
| "id": "etl2kX0nKU3z" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tf.__version__" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 37 | |
| }, | |
| "id": "oVhfUstZKaU4", | |
| "outputId": "563d714b-dbc3-45bb-d305-11e823e02a0f" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'2.15.0'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import keras" | |
| ], | |
| "metadata": { | |
| "id": "OcSvcatSKzxU" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "keras.__version__" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 37 | |
| }, | |
| "id": "7wDgj10XK3WV", | |
| "outputId": "1b5d6dc1-d7c9-41d7-9139-b4d702b49879" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'2.15.0'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os\n", | |
| "os.environ[\"TF_USE_LEGACY_KERAS\"] =\"1\"" | |
| ], | |
| "metadata": { | |
| "id": "EaPWJA_VLaPK" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5)\n", | |
| "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", | |
| "metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n", | |
| "model.compile(optimizer=optimizer, loss=loss, metrics=[metric])" | |
| ], | |
| "metadata": { | |
| "id": "wZTysYXuK42N" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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