diff --git "a/Lime Explorations.ipynb" "b/Lime Explorations.ipynb"
deleted file mode 100644--- "a/Lime Explorations.ipynb"
+++ /dev/null
@@ -1,185821 +0,0 @@
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- "outputs": [],
- "source": [
- "import numpy as np\n",
- "#https://github.com/marcotcr/lime for reference\n",
- "import lime\n",
- "import torch\n",
- "import torch.nn.functional as F\n",
- "from lime.lime_text import LimeTextExplainer\n",
- "\n",
- "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "73778913-2ad6-4e3d-b1ee-9eadbc17e823",
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- "source": [
- "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
- "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "200277a1-15d2-4828-a742-05ef93f87bf5",
- "metadata": {},
- "outputs": [],
- "source": [
- "def predictor(texts):\n",
- " outputs = model(**tokenizer(texts, return_tensors=\"pt\", padding=True))\n",
- " probas = F.softmax(outputs.logits, dim=1).detach().numpy()\n",
- " return probas"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
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- "source": [
- "class_names = ['negative', 'positive']\n",
- "explainer = LimeTextExplainer(class_names=class_names)\n",
- "\n",
- "str_to_predict = \"Native Americans deserve to have their lands back.\"\n",
- "exp = explainer.explain_instance(str_to_predict, predictor, num_features=20, num_samples=2000)\n",
- "exp.show_in_notebook(text=str_to_predict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
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- "source": [
- "text1 = \"FC Barcelona is not a German football team.\"\n",
- "exp = explainer.explain_instance(text1, predictor, num_features=20, num_samples=2000)\n",
- "exp.show_in_notebook(text=text1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "0ea62f5e-b83d-4ce9-b92b-e09aeacd4741",
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- "source": [
- "text2 = \"He had a German car that he drove.\"\n",
- "exp = explainer.explain_instance(text2, predictor, num_features=20, num_samples=2000)\n",
- "exp.show_in_notebook(text=text2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "813682cc-1271-4021-a932-dfe6347e13af",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[('German', -0.6919233098787057),\n",
- " ('he', -0.2385283355148371),\n",
- " ('car', 0.17295381923211997),\n",
- " ('had', -0.14217629753917047),\n",
- " ('a', 0.12866437095896172),\n",
- " ('drove', 0.08097486924056058),\n",
- " ('He', 0.049226218092743),\n",
- " ('that', 0.00988639644487302)]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "exp.as_list()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "02d0c97e-098c-4932-9d67-285af7bbb1ec",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[('Iraq', -0.9081354370318596),\n",
- " ('was', -0.03403640727982729),\n",
- " ('in', -0.030957227884937304),\n",
- " ('This', -0.03011625239348594),\n",
- " ('movie', -0.029048209127173097),\n",
- " ('filmed', -0.015553741829700034)]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "upt1 = \"I like movies starring black actors.\"\n",
- "upt2 = \"I am a black trans-woman.\"\n",
- "upt3 = \"Native Americans deserve to have their land back.\"\n",
- "upt4 = \"This movie was filmed in Iraq.\"\n",
- "exp = explainer.explain_instance(upt4, predictor, num_features=20, num_samples=2000)\n",
- "exp.as_list()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "ede2760d-0e58-4779-83bb-6f94d053a7a0",
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- "source": [
- "iraq = \"North Korea eats halal meat.\"\n",
- "exp = explainer.explain_instance(iraq, predictor, num_features=20, num_samples=2000)\n",
- "exp.show_in_notebook(text=iraq)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "7dd86b0b-497e-4f0b-bd28-aec1e6548663",
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- "source": [
- "iraq = \"North Korea serves halal meat.\"\n",
- "exp = explainer.explain_instance(iraq, predictor, num_features=20, num_samples=2000)\n",
- "exp.show_in_notebook(text=iraq)"
- ]
- },
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- "language": "python",
- "name": "python3"
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