testing new changes

app.py

@@ -0,0 +1,24 @@
+from nubia_score import Nubia
+import gradio
+
+nubia = Nubia()
+
+
+def predict(inp_1, inp_2):
+    features = nubia.score(inp_1, inp_2, get_features=True)
+    labels = {k: v for k, v in features["features"].items()}
+    return {"nubia_score": features["nubia_score"]}, labels
+
+title = "NUBIA"
+description = "NeUral Based Interchangeability Assessor. A SoTA evaluation metric for text generation."
+inputs = [gradio.inputs.Textbox(label="First Text"), gradio.inputs.Textbox(label="Second Text")]
+outputs = [gradio.outputs.Label(label="Interchangeability Score"), gradio.outputs.JSON(label="All Features")]
+examples = [
+    ["This car is expensive! I can't buy it.", "That automobile costs a fortune! Purchasing it? Impossible!"],
+    ["This car is expensive! I can't buy it.", "That automobile costs a good amount. Purchasing it? Totally feasible!"],
+    ["The dinner was delicious.", "The dinner did not taste good."]
+]
+iface = gradio.Interface(fn=predict, inputs=inputs, outputs=outputs, capture_session=True, examples=examples,
+                         title=title, description=description, allow_flagging=False)
+
+iface.launch()

nubia-demo.ipynb

@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NUBIA: A SoTA evaluation metric for text generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import and initialize the Nubia class from nubia.py (wherever you cloned the repo)\n",
+    "See our [colab notebook](https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg) as an example\n",
+    "\n",
+    "Note: The first time you initialize the class it will download the pretrained models from the S3 bucket, this could take a while depending on your internet connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nubia import Nubia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading archive file pretrained/roBERTa_STS\n",
+      "| [input] dictionary: 50265 types\n",
+      "loading archive file pretrained/roBERTa_MNLI\n",
+      "| dictionary: 50264 types\n"
+     ]
+    }
+   ],
+   "source": [
+    "nubia = Nubia()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You're now ready to start evaluating! \n",
+    "\n",
+    "`nubia.score` takes 7 parameters: `(ref, hyp, verbose=False, get_features=False, six_dim=False, aggregator=\"agg_one\")`\n",
+    "\n",
+    "`ref` and `hyp` are the strings nubia will compare. \n",
+    "\n",
+    "Setting `get_features` to `True` will return a dictionary with additional features (semantic relation, contradiction, irrelevancy, logical agreement, and grammaticality) aside from the nubia score. `Verbose=True` prints all the features.\n",
+    "\n",
+    "`six_dim = True` will use a six dimensional \n",
+    "\n",
+    "`aggregator` is set to `agg_one` by default, but you may choose to try `agg_two` which is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9905961979783401"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nubia.score(\"The dinner was delicious.\", \"It was a tasty dinner.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.601254306957781"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Semantic relation: 1.4594818353652954/5.0\n",
+      "Percent chance of contradiction: 99.90345239639282%\n",
+      "Percent chance of irrelevancy or new information: 0.06429857457987964%\n",
+      "Percent chance of logical agreement: 0.03225349937565625%\n",
+      "\n",
+      "NUBIA score: 0.601254306957781/1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'nubia_score': 0.601254306957781,\n",
+       " 'features': {'semantic_relation': 1.4594818353652954,\n",
+       "  'contradiction': 99.90345239639282,\n",
+       "  'irrelevancy': 0.06429857457987964,\n",
+       "  'logical_agreement': 0.03225349937565625,\n",
+       "  'grammar_ref': 5.1724853515625,\n",
+       "  'grammar_hyp': 4.905452728271484}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\", verbose=True, get_features=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

nubia_score/__init__.py

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+from nubia_score.nubia import *
+
+

nubia_score/nubia.py

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+from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+import os
+import wget
+import numpy as np
+from fairseq.models.roberta import RobertaModel
+from joblib import load
+
+pretrained = os.path.join(os.path.dirname(__file__), "pretrained")
+
+ROBERTA_STS_PATH = pretrained + '/roBERTa_STS'
+ROBERTA_MNLI_PATH = pretrained + '/roBERTa_MNLI'
+AGGREGATOR_DIR = pretrained + '/aggregators/'
+AGGREGATOR_2015_2016 = \
+    pretrained + '/aggregators/nn_2015_2016_6_dim' \
+    '.joblib'
+AGGREGATOR_2015_2017 = \
+    pretrained + '/aggregators/nn_2015_2017_6_dim' \
+    '.joblib'
+AGGREGATOR_2015_2016_8_dim = \
+    pretrained + '/aggregators/nn_2015_2016_8_dim' \
+    '.joblib'
+AGGREGATOR_2015_2017_8_dim = \
+    pretrained + '/aggregators/nn_2015_2017_8_dim' \
+    '.joblib'
+
+ROBERTA_STS_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                  "neural-feature-extractors/checkpoint_best.pt"
+ROBERTA_MNLI_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                   "neural-feature-extractors/model_mnli.pt"
+AGGREGATOR_2015_2016_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                           "aggregators/nn_2015_2016_6_dim.joblib"
+AGGREGATOR_2015_2017_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                           "aggregators/nn_2015_2017_6_dim.joblib"
+AGGREGATOR_2015_2016_8_dim_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                           "aggregators/nn_2015_2016_8_dim.joblib"
+
+AGGREGATOR_2015_2017_8_dim_URL = "https://nubia-nn.s3.amazonaws.com/" \
+                           "aggregators/nn_2015_2017_8_dim.joblib"
+
+
+class Nubia:
+    def __init__(self):
+        if not os.path.exists(AGGREGATOR_DIR):
+            os.makedirs(AGGREGATOR_DIR)
+        if not os.path.isfile(AGGREGATOR_2015_2016):
+            print("Downloading aggregators from s3...")
+            wget.download(AGGREGATOR_2015_2016_URL,
+                                AGGREGATOR_2015_2016)
+        if not os.path.isfile(AGGREGATOR_2015_2017):
+            print("\nDownloading aggregators from s3...")
+            wget.download(AGGREGATOR_2015_2017_URL,
+                                AGGREGATOR_2015_2017)
+        if not os.path.isfile(AGGREGATOR_2015_2016_8_dim):
+            print("\nDownloading aggregators from s3...")
+            wget.download(AGGREGATOR_2015_2016_8_dim_URL,
+                                AGGREGATOR_2015_2016_8_dim)
+        if not os.path.isfile(AGGREGATOR_2015_2017_8_dim):
+            print("\nDownloading aggregators from s3...")
+            wget.download(AGGREGATOR_2015_2017_8_dim_URL,
+                                AGGREGATOR_2015_2017_8_dim)
+        if not os.path.isfile(ROBERTA_STS_PATH + '/checkpoint_best.pt'):
+            print("\nDownloading ROBERTA STS model from s3...")
+            wget.download(ROBERTA_STS_URL, ROBERTA_STS_PATH +
+                          '/checkpoint_best.pt')
+        if not os.path.isfile(ROBERTA_MNLI_PATH + '/model_mnli.pt'):
+            print("\nDownloading ROBERTA MNLI model from s3...")
+            wget.download(ROBERTA_MNLI_URL, ROBERTA_MNLI_PATH +
+                          '/model_mnli.pt')
+        print('\n')
+        self.roberta_STS = RobertaModel.from_pretrained(
+            checkpoint_file='checkpoint_best.pt',
+            model_name_or_path=ROBERTA_STS_PATH)
+        self.roberta_STS.eval()
+
+        self.roberta_MNLI = RobertaModel.from_pretrained(
+            checkpoint_file='model_mnli.pt',
+            model_name_or_path=ROBERTA_MNLI_PATH)
+        self.roberta_MNLI.eval()
+        self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+        self.gpt_model = GPT2LMHeadModel.from_pretrained('gpt2')
+        self.agg_one = load(AGGREGATOR_2015_2016)
+        self.agg_two = load(AGGREGATOR_2015_2017)
+        self.agg_one_8_dim = load(AGGREGATOR_2015_2016_8_dim)
+        self.agg_two_8_dim = load(AGGREGATOR_2015_2017_8_dim)
+
+    @staticmethod
+    def _download_progress_bar(current, total, width=80):
+        print("Downloading: %d%% [%d / %d] bytes" % (
+            current / total * 100, current, total))
+
+    def _roberta_tokenizer(self, ref, hyp):
+        tokens = self.roberta_STS.encode(ref, hyp)
+        return tokens
+
+    def _roberta_similarity(self, tokens):
+        if len(tokens) > 512:
+          tokens = tokens[:512]
+        features = self.roberta_STS.extract_features(tokens)
+        predicted_semantic_distance = 5.0 * \
+            self.roberta_STS.model.classification_heads['sentence_classification_head'](features)
+        return predicted_semantic_distance
+
+    def _roberta_mnli_all_values(self, tokens):
+        if len(tokens) > 512:
+          tokens = tokens[:512]
+        prediction = self.roberta_MNLI.predict('mnli', tokens)[0].\
+            cpu().detach().numpy()
+        return prediction
+
+    def _gpt_score(self, text):
+        tokenize_input = self.tokenizer.tokenize(text)
+        tensor_input = torch.tensor([[self.tokenizer. eos_token_id] +
+                                     self.tokenizer.convert_tokens_to_ids(
+                                         tokenize_input)])
+        with torch.no_grad():
+            outputs = self.gpt_model(tensor_input, labels=tensor_input)
+            loss, logits = outputs[:2]
+        return loss
+
+    def nubia(self, ref, hyp, get_features=False, six_dim=False,
+              aggregator="agg_two", gpt_ref=None):
+        tokens = self._roberta_tokenizer(ref, hyp)
+        sim = float(self._roberta_similarity(tokens)[0])
+
+        mnli_zero, mnli_one, mnli_two = self._roberta_mnli_all_values(tokens)
+        if not gpt_ref:
+            gpt_ref = self._gpt_score(ref)
+            gpt_hyp = self._gpt_score(hyp)
+        else:
+            gpt_hyp = gpt_ref
+        len_ref = len(ref.split(" "))
+        len_hyp = len(hyp.split(" "))
+
+        mnli_friendly = torch.nn.functional.softmax(
+            torch.tensor([mnli_zero, mnli_one, mnli_two]), dim=0).tolist()
+
+        neural_features_6_dim = np.array(
+            [float(sim), float(mnli_zero), float(mnli_one), float(mnli_two),
+             float(gpt_ref), float(gpt_hyp)])  # 6 Neural Features
+
+        neural_features_8_dim = np.array(
+            [float(sim), float(mnli_zero), float(mnli_one), float(mnli_two),
+             float(gpt_ref), float(gpt_hyp), float(len_ref),
+             float(len_hyp)])  # 8 Neural Features
+
+        if aggregator == "agg_one":
+            if six_dim:
+                nubia_metric = float(self.agg_one.predict(
+                    neural_features_6_dim.reshape(1, -1))[0])
+            else:
+                nubia_metric = float(self.agg_one_8_dim.predict(
+                    neural_features_8_dim.reshape(1, -1))[0])
+        else:
+            if six_dim:
+                nubia_metric = float(self.agg_two.predict(
+                    neural_features_6_dim.reshape(1, -1))[0])
+            else:
+                nubia_metric = float(self.agg_two_8_dim.predict(
+                    neural_features_8_dim.reshape(1, -1))[0])
+
+        if get_features:
+            return {"nubia_score": nubia_metric, "features": {
+                "semantic_relation": min(5.0, sim),
+                "contradiction": mnli_friendly[0]*100,
+                "irrelevancy": mnli_friendly[1]*100,
+                "logical_agreement": mnli_friendly[2]*100,
+                "grammar_ref": gpt_ref.item(),
+                "grammar_hyp": gpt_hyp.item(),
+            }
+             }, gpt_ref
+        return nubia_metric, gpt_ref
+
+    def score(self, ref, hyp, verbose=False, get_features=False,
+              six_dim=False, aggregator="agg_two"):
+
+        if not ref or not hyp:
+            if get_features:
+                return {"nubia_score": 0, "features": {
+                    "semantic_relation": 0,
+                    "contradiction": 0,
+                    "irrelevancy": 0,
+                    "logical_agreement": 0,
+                    "grammar_ref": 0,
+                    "grammar_hyp": 0,
+                }
+                        }
+            return 0
+
+        nubia, gpt_ref = self.nubia(ref, hyp, get_features=True, six_dim=six_dim,
+                           aggregator=aggregator)
+
+        self_similarity, _ = self.nubia(ref, ref,
+                                     get_features=False, six_dim=six_dim,
+                                     aggregator=aggregator, gpt_ref=gpt_ref)
+
+        amplitude = abs(self_similarity) + 1
+        difference = self_similarity - nubia["nubia_score"]
+
+        calibrated = 1.0 - (float(difference) / float(amplitude))
+
+        if calibrated > 0:
+            calibrated = min(1.0, calibrated)
+        else:
+            calibrated = max(0.0, calibrated)
+
+        if verbose:
+            print("Semantic relation: " +
+                  str(min(5.0, nubia["features"]["semantic_relation"])) +
+                  '/5.0')
+            print("Percent chance of contradiction: " +
+                  str(nubia["features"]["contradiction"]) + "%")
+            print("Percent chance of irrelevancy or new information: " +
+                  str(nubia["features"]["irrelevancy"]) + "%")
+            print("Percent chance of logical agreement: " +
+                  str(nubia["features"]["logical_agreement"]) + "%\n")
+            print("NUBIA score: " + str(calibrated) + "/1.0")
+
+        nubia["nubia_score"] = calibrated
+
+        if get_features:
+            return nubia
+
+        return calibrated

nubia_score/pretrained/roBERTa_MNLI/NOTE

@@ -0,0 +1 @@
+Models from the paper ``RoBERTa: A Robustly Optimized BERT Pretraining Approach"

nubia_score/pretrained/roBERTa_MNLI/mnli_label_dict.txt

@@ -0,0 +1,3 @@
+contradiction 0
+neutral 1
+entailment 2

nubia_score/pretrained/roBERTa_STS/input0/test.bin

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nubia_score/pretrained/roBERTa_STS/input1/train.bin

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requirements.txt

@@ -0,0 +1,9 @@
+torch >= 1.4.0
+fairseq
+pytorch-transformers
+numpy >= 1.18.2
+joblib
+scikit-learn >= 0.22.2
+wget
+gradio
+setuptools