|
178 | 178 | { |
179 | 179 | "cell_type": "code", |
180 | 180 | "execution_count": null, |
181 | | - "outputs": [], |
182 | | - "source": [ |
183 | | - "results = giskard.scan(model, dataset)#%% md\n", |
184 | | - "# Newspaper comments generation [LangChain, OpenAI]" |
185 | | - ], |
186 | | - "metadata": { |
187 | | - "collapsed": false |
188 | | - } |
189 | | - }, |
190 | | - { |
191 | | - "cell_type": "markdown", |
192 | | - "source": [ |
193 | | - "## Install Giskard" |
194 | | - ], |
195 | | - "metadata": { |
196 | | - "collapsed": false |
197 | | - } |
198 | | - }, |
199 | | - { |
200 | | - "cell_type": "markdown", |
201 | | - "source": [ |
202 | | - "**⚠️ LLM support is in Alpha version** - It may be unstable, and detection capabilities are still limited.\n", |
203 | | - "\n", |
204 | | - "We're interested in your feedback to improve it 🙏" |
205 | | - ], |
206 | | - "metadata": { |
207 | | - "collapsed": false |
208 | | - } |
209 | | - }, |
210 | | - { |
211 | | - "cell_type": "code", |
212 | | - "execution_count": null, |
213 | | - "outputs": [], |
214 | | - "source": [ |
215 | | - "pip install \"giskard[llm]>=2.0.0b\" -U" |
216 | | - ], |
217 | | - "metadata": { |
218 | | - "collapsed": false |
219 | | - } |
220 | | - }, |
221 | | - { |
222 | | - "cell_type": "markdown", |
223 | | - "source": [ |
224 | | - "## Install additional libraries" |
225 | | - ], |
226 | | - "metadata": { |
227 | | - "collapsed": false |
228 | | - } |
229 | | - }, |
230 | | - { |
231 | | - "cell_type": "code", |
232 | | - "execution_count": null, |
233 | | - "outputs": [], |
234 | | - "source": [ |
235 | | - "!pip install openai" |
236 | | - ], |
237 | | - "metadata": { |
238 | | - "collapsed": false |
239 | | - } |
240 | | - }, |
241 | | - { |
242 | | - "cell_type": "markdown", |
243 | | - "source": [ |
244 | | - "## Import libraries" |
245 | | - ], |
246 | | - "metadata": { |
247 | | - "collapsed": false |
248 | | - } |
249 | | - }, |
250 | | - { |
251 | | - "cell_type": "code", |
252 | | - "execution_count": null, |
253 | | - "outputs": [], |
254 | | - "source": [ |
255 | | - "import pandas as pd\n", |
256 | | - "from langchain.llms import OpenAI\n", |
257 | | - "from langchain import PromptTemplate, LLMChain\n", |
258 | | - "\n", |
259 | | - "import giskard" |
260 | | - ], |
261 | | - "metadata": { |
262 | | - "collapsed": false |
263 | | - } |
264 | | - }, |
265 | | - { |
266 | | - "cell_type": "markdown", |
267 | | - "source": [ |
268 | | - "## Load and filter dataframe from Github" |
269 | | - ], |
270 | | - "metadata": { |
271 | | - "collapsed": false |
272 | | - } |
273 | | - }, |
274 | | - { |
275 | | - "cell_type": "code", |
276 | | - "execution_count": null, |
277 | | - "outputs": [], |
278 | | - "source": [ |
279 | | - "df = pd.read_csv('https://github.com/__raw/sunnysai12345/News_Summary/master/news_summary_more.csv')" |
280 | | - ], |
281 | | - "metadata": { |
282 | | - "collapsed": false |
283 | | - } |
284 | | - }, |
285 | | - { |
286 | | - "cell_type": "code", |
287 | | - "execution_count": null, |
288 | | - "outputs": [], |
289 | | - "source": [ |
290 | | - "df_filtered = pd.DataFrame(df[\"text\"].sample(10, random_state=11))" |
291 | | - ], |
292 | | - "metadata": { |
293 | | - "collapsed": false |
294 | | - } |
295 | | - }, |
296 | | - { |
297 | | - "cell_type": "markdown", |
298 | | - "source": [ |
299 | | - "## Initialize Prompt, LLM and Chain from LangChain" |
300 | | - ], |
301 | | - "metadata": { |
302 | | - "collapsed": false |
303 | | - } |
304 | | - }, |
305 | | - { |
306 | | - "cell_type": "code", |
307 | | - "execution_count": null, |
308 | | - "outputs": [], |
309 | | - "source": [ |
310 | | - "prompt = PromptTemplate(template=\"Create a reader comment according to the following article summary: '{text}''\",\n", |
311 | | - " input_variables=[\"text\"])" |
312 | | - ], |
313 | | - "metadata": { |
314 | | - "collapsed": false |
315 | | - } |
316 | | - }, |
317 | | - { |
318 | | - "cell_type": "code", |
319 | | - "execution_count": null, |
320 | | - "outputs": [], |
321 | | - "source": [ |
322 | | - "llm = OpenAI(openai_api_key=\"${OPENAI_KEY}\", request_timeout=15, max_retries=50, temperature=0)" |
323 | | - ], |
324 | | - "metadata": { |
325 | | - "collapsed": false |
326 | | - } |
327 | | - }, |
328 | | - { |
329 | | - "cell_type": "code", |
330 | | - "execution_count": null, |
331 | | - "outputs": [], |
332 | | - "source": [ |
333 | | - "chain = LLMChain(prompt=prompt, llm=llm)" |
334 | | - ], |
335 | | - "metadata": { |
336 | | - "collapsed": false |
337 | | - } |
338 | | - }, |
339 | | - { |
340 | | - "cell_type": "markdown", |
341 | | - "source": [ |
342 | | - "## Dataset and Model preparation" |
343 | | - ], |
344 | 181 | "metadata": { |
345 | 182 | "collapsed": false |
346 | | - } |
347 | | - }, |
348 | | - { |
349 | | - "cell_type": "code", |
350 | | - "execution_count": null, |
351 | | - "outputs": [], |
352 | | - "source": [ |
353 | | - "dataset = giskard.Dataset(df=df_filtered, column_types={\"text\": \"text\"})" |
354 | | - ], |
355 | | - "metadata": { |
356 | | - "collapsed": false |
357 | | - } |
358 | | - }, |
359 | | - { |
360 | | - "cell_type": "code", |
361 | | - "execution_count": null, |
362 | | - "outputs": [], |
363 | | - "source": [ |
364 | | - "model = giskard.Model(chain, model_type='text_generation')" |
365 | | - ], |
366 | | - "metadata": { |
367 | | - "collapsed": false |
368 | | - } |
369 | | - }, |
370 | | - { |
371 | | - "cell_type": "markdown", |
372 | | - "source": [ |
373 | | - "## Scan your model to find vulnerabilities" |
374 | | - ], |
375 | | - "metadata": { |
376 | | - "collapsed": false |
377 | | - } |
378 | | - }, |
379 | | - { |
380 | | - "cell_type": "code", |
381 | | - "execution_count": null, |
382 | | - "outputs": [], |
383 | | - "source": [ |
384 | | - "results = giskard.scan(model, dataset)" |
385 | | - ], |
386 | | - "metadata": { |
387 | | - "collapsed": false |
388 | | - } |
389 | | - }, |
390 | | - { |
391 | | - "cell_type": "code", |
392 | | - "execution_count": null, |
| 183 | + }, |
393 | 184 | "outputs": [], |
394 | 185 | "source": [ |
395 | | - "display(results)#%%\n", |
396 | | - "display(results)" |
397 | | - ], |
398 | | - "metadata": { |
399 | | - "collapsed": false |
400 | | - } |
| 186 | + "results = giskard.scan(model, dataset)#%% md\n", |
| 187 | + "# Newspaper comments generation [LangChain, OpenAI]" |
| 188 | + ] |
401 | 189 | } |
402 | 190 | ], |
403 | 191 | "metadata": { |
|
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