(3-shot@1) under the category "Math & Code". Due to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score is derived by averaging the scores on the remaining tasks. Since the scores for these two tasks are generally lower than the average, we believe that ...
The following function produces elegantly the set of n-grams for a sequence of tokens:9 def ngrams(tokens, n=2, sep=' '): return [sep.join(ngram) for ngram in zip(*[tokens[i:] for i in range(n)])] text = "the visible manifestation of the global climate change" tokens = toke...
importnumpy as npimportpandas as pdimportmatplotlib.pyplot as pltfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.linear_model.logisticimportLogisticRegressionfromsklearn.cross_validationimporttrain_test_split, cross_val_scorefromsklearn.metricsimportroc_curve, auc df= pd.read_csv('mlsl...
If an n-gram has a stronger tendency to occur in a sentence with a particular emotion label, than in a sentence that does not have that label, then that ngram–emotion pair will have an SoA score that is greater than zero. 5.2. Emotion lexicons created from the 1000-headlines dataset ...
72 + "from pathlib import Path\n", 73 + "\n", 74 + "finetuned_model_path = Path(\"gpt2-medium355M-sft.pth\")\n", 75 + "if not finetuned_model_path.exists():\n", 76 + " print(\n", 77 + " f\"Could not find '{finetuned_model_path}'.\\n\"\n", 78 + "...
tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english') features = tfidf.fit_transform(ACLED.notes).toarray() labels = ACLED.category_id print(features.shape) ...
eos_token_id, do_sample=True, repetition_penalty=1.3, no_repeat_ngram_size=5, temperature=0.7, top_k=40, top_p=0.8, ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Output Prompt: There's a place where time stands still. A place of breath taking wonder, but also ...
("There's a place where time stands still. A place of breath taking wonder, but also",return_tensors="pt")max_length=256outputs=model.generate(inputs.input_ids.cuda(),max_length=max_length,eos_token_id=tokenizer.eos_token_id,do_sample=True,repetition_penalty=1.3,no_repeat_ngram_size=...