diff --git "a/Lime Explorations.ipynb" "b/Lime Explorations.ipynb"
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+ "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"
+ ]
+ },
+ {
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+ "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"
+ ]
+ },
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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)"
+ ]
+ },
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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",
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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)"
+ ]
+ },
+ {
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+ "[('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",
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+ "[('Iraq', -0.9081354370318596),\n",
+ " ('was', -0.03403640727982729),\n",
+ " ('in', -0.030957227884937304),\n",
+ " ('This', -0.03011625239348594),\n",
+ " ('movie', -0.029048209127173097),\n",
+ " ('filmed', -0.015553741829700034)]"
+ ]
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+ "metadata": {},
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+ ],
+ "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,
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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)"
+ ]
+ },
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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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