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Error code: ExternalFilesSizeRequestTimeoutError Exception: ReadTimeout Message: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=10.0) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 466, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 461, in _make_request httplib_response = conn.getresponse() File "/usr/local/lib/python3.9/http/client.py", line 1377, in getresponse response.begin() File "/usr/local/lib/python3.9/http/client.py", line 320, in begin version, status, reason = self._read_status() File "/usr/local/lib/python3.9/http/client.py", line 281, in _read_status line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1") File "/usr/local/lib/python3.9/socket.py", line 704, in readinto return self._sock.recv_into(b) File "/usr/local/lib/python3.9/ssl.py", line 1242, in recv_into return self.read(nbytes, buffer) File "/usr/local/lib/python3.9/ssl.py", line 1100, in read return self._sslobj.read(len, buffer) socket.timeout: The read operation timed out During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 486, in send resp = conn.urlopen( File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 798, in urlopen retries = retries.increment( File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/util/retry.py", line 550, in increment raise six.reraise(type(error), error, _stacktrace) File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/packages/six.py", line 770, in reraise raise value File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 714, in urlopen httplib_response = self._make_request( File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 468, in _make_request self._raise_timeout(err=e, url=url, timeout_value=read_timeout) File "/src/services/worker/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 357, in _raise_timeout raise ReadTimeoutError( urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=10.0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 488, in _is_too_big_from_external_data_files for i, size in enumerate(pool.imap_unordered(get_size, ext_data_files)): File "/usr/local/lib/python3.9/multiprocessing/pool.py", line 870, in next raise value File "/usr/local/lib/python3.9/multiprocessing/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 386, in _request_size response = http_head(url, headers=headers, max_retries=3) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 429, in http_head response = _request_with_retry( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 328, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, **send_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 725, in send history = [resp for resp in gen] File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 725, in <listcomp> history = [resp for resp in gen] File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 266, in resolve_redirects resp = self.send( File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 532, in send raise ReadTimeout(e, request=request) requests.exceptions.ReadTimeout: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=10.0)
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id
string | hit_ids
sequence | sentence
string | indices_into_review_text
sequence | model_0_label
string | model_0_probs
dict | text_id
string | review_id
string | review_rating
int32 | label_distribution
dict | gold_label
string | metadata
dict |
---|---|---|---|---|---|---|---|---|---|---|---|
r1-0000001 | [
"y5238"
] | Roto-Rooter is always good when you need someone right away. | [
0,
60
] | positive | {
"negative": 0.01173639390617609,
"positive": 0.7473671436309814,
"neutral": 0.24089649319648743
} | r1-0000001 | IDHkeGo-nxhqX4Exkdr08A | 1 | {
"positive": [
"w130",
"w186",
"w207",
"w264",
"w54"
],
"negative": [],
"neutral": [],
"mixed": []
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000002 | [
"y11155"
] | It's so worth the price of cox service over headaches of not being able to publish anything in a short amount of time on the Internet or frankly just stream in different rooms without buffering constantly. | [
155,
360
] | positive | {
"negative": 0.004590339958667755,
"positive": 0.950273871421814,
"neutral": 0.04513582959771156
} | r1-0000002 | HERx4qHWIVDwH3AME9OYgQ | 1 | {
"positive": [
"w129",
"w375",
"w39"
],
"negative": [
"w70"
],
"neutral": [],
"mixed": [
"w477"
]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000003 | [
"y14984",
"y17992"
] | I placed my order of "sticky ribs" as an appetizer and the "angry chicken" as my entree. | [
530,
618
] | negative | {
"negative": 0.9451407194137573,
"positive": 0.004023305606096983,
"neutral": 0.05083595588803291
} | r1-0000003 | gCshZ3zPCaAwu8sAejmTcA | 5 | {
"positive": [
"w133"
],
"negative": [],
"neutral": [
"w135",
"w184",
"w38",
"w439"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000004 | [
"y1167",
"y297"
] | There is mandatory valet parking, so make sure you get everything you need from the car! | [
798,
886
] | positive | {
"negative": 0.0416930615901947,
"positive": 0.5428488254547119,
"neutral": 0.4154581129550934
} | r1-0000004 | JeLaSy9T3-pdVj_fQ6JX9A | 5 | {
"positive": [],
"negative": [
"w135"
],
"neutral": [
"w148",
"w207",
"w294",
"w772"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000006 | [
"y11412",
"y19170",
"y4073"
] | My wife and I couldn't finish it. | [
294,
327
] | negative | {
"negative": 0.977493166923523,
"positive": 0.0006850706995464861,
"neutral": 0.02182171680033207
} | r1-0000006 | o-5hLEypDxR4jXjbmzkNBA | 5 | {
"positive": [],
"negative": [
"w254"
],
"neutral": [
"w176",
"w328",
"w39",
"w598"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000007 | [
"y2002"
] | I went with a revised quote and they set an appointment for the next week. | [
732,
806
] | negative | {
"negative": 0.6572147011756897,
"positive": 0.09853064268827438,
"neutral": 0.2442547082901001
} | r1-0000007 | jttEKPQZ6UImUQVubG1GDg | 5 | {
"positive": [
"w280"
],
"negative": [],
"neutral": [
"w265",
"w46",
"w490",
"w936"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000008 | [
"y14885",
"y16948"
] | I found out that the restaurant has only been open for about a week. | [
138,
206
] | negative | {
"negative": 0.45139288902282715,
"positive": 0.12103912234306335,
"neutral": 0.4275679886341095
} | r1-0000008 | W8GvEfQ2qShtF7iRmehWVw | 5 | {
"positive": [],
"negative": [],
"neutral": [
"w137",
"w310",
"w415",
"w460",
"w627"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000010 | [
"y18243",
"y4069",
"y4712"
] | I suffer with high blood pressure, headaches, type 2 diabetes and TMJ. | [
449,
519
] | neutral | {
"negative": 0.3392733931541443,
"positive": 0.30037474632263184,
"neutral": 0.36035192012786865
} | r1-0000010 | uKrOYLnAVUsB1GE2huJt4A | 5 | {
"positive": [],
"negative": [
"w155",
"w193",
"w204",
"w620"
],
"neutral": [
"w133"
],
"mixed": []
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000011 | [
"y19430",
"y2698"
] | Seriously, come here now before the hype sets in and you won't be able to get a table! | [
1060,
1146
] | positive | {
"negative": 0.026026399806141853,
"positive": 0.8168579936027527,
"neutral": 0.15711559355258942
} | r1-0000011 | 80r8He9zm4G1aR5N_7EP3Q | 5 | {
"positive": [
"w132",
"w173",
"w227"
],
"negative": [],
"neutral": [
"w447"
],
"mixed": [
"w23"
]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000012 | [
"y3416"
] | It was difficult to find the place. | [
86,
121
] | neutral | {
"negative": 0.2690688669681549,
"positive": 0.2074701339006424,
"neutral": 0.5234610438346863
} | r1-0000012 | B6mQSD5qLmd0kaaTzRh8jw | 5 | {
"positive": [
"w385",
"w591"
],
"negative": [
"w290",
"w539",
"w552"
],
"neutral": [],
"mixed": []
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000013 | [
"y5784",
"y801"
] | And their shuffleboard game. | [
1282,
1310
] | neutral | {
"negative": 0.041217926889657974,
"positive": 0.04401402175426483,
"neutral": 0.9147680997848511
} | r1-0000013 | 0DGlwRVW-m7RHGJOTWuR1A | 5 | {
"positive": [],
"negative": [],
"neutral": [
"w165",
"w199",
"w285",
"w368",
"w62"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000014 | [
"y14474",
"y19184"
] | Need a cheap spatula? | [
542,
563
] | neutral | {
"negative": 0.3583439886569977,
"positive": 0.03304021805524826,
"neutral": 0.6086158156394958
} | r1-0000014 | aXrReHdGItNRnkijnVRH4g | 5 | {
"positive": [],
"negative": [],
"neutral": [
"w129",
"w134",
"w305",
"w424",
"w8"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000015 | [
"y15969"
] | Mexican. | [
7,
15
] | neutral | {
"negative": 0.01613759435713291,
"positive": 0.05673476681113243,
"neutral": 0.9271275997161865
} | r1-0000015 | FLqOVx0X5I4srqLMilNapQ | 1 | {
"positive": [],
"negative": [],
"neutral": [
"w143",
"w188",
"w625",
"w707",
"w716"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000016 | [
"y11207",
"y5687",
"y7110"
] | Coronado Cafe and Kate's corner are still awesome but I think we will be venturing out to other casinos and see if we find out new spot. | [
1520,
1656
] | positive | {
"negative": 0.05092405527830124,
"positive": 0.48395779728889465,
"neutral": 0.4651181399822235
} | r1-0000016 | pF1rD1WnVxbXddq3aRsAww | 1 | {
"positive": [
"w143",
"w265",
"w755"
],
"negative": [],
"neutral": [
"w577"
],
"mixed": [
"w44"
]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000017 | [
"y3946",
"y8055"
] | Not sure why, just my observation. | [
768,
802
] | neutral | {
"negative": 0.0760495588183403,
"positive": 0.0820464938879013,
"neutral": 0.8419039249420166
} | r1-0000017 | mh-RYGiocnxElg2BUltuyw | 5 | {
"positive": [],
"negative": [],
"neutral": [
"w198",
"w210",
"w22",
"w35",
"w671"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000019 | [
"y1429"
] | You win! | [
54,
62
] | positive | {
"negative": 0.00790376216173172,
"positive": 0.9436400532722473,
"neutral": 0.048456232994794846
} | r1-0000019 | nDW0K0d9rclQPLy5ZIgFAg | 1 | {
"positive": [
"w155",
"w199",
"w234",
"w289"
],
"negative": [],
"neutral": [
"w326"
],
"mixed": []
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000020 | [
"y11167",
"y18763",
"y580"
] | I'm not affiliated with Presto I was just super impressed by the food! | [
1098,
1168
] | positive | {
"negative": 0.0017010181909427047,
"positive": 0.9760681390762329,
"neutral": 0.02223076857626438
} | r1-0000020 | kyzuLHLnr421QgJaNuGOxA | 1 | {
"positive": [
"w122",
"w135",
"w314",
"w572"
],
"negative": [],
"neutral": [],
"mixed": [
"w102"
]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000021 | [
"y6427"
] | We told him again. | [
3527,
3545
] | neutral | {
"negative": 0.007084425538778305,
"positive": 0.08201110363006592,
"neutral": 0.9109045267105103
} | r1-0000021 | rLA1DJqBZaigZsmC4FnlAw | 1 | {
"positive": [
"w187"
],
"negative": [
"w321"
],
"neutral": [
"w121",
"w436",
"w598"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000022 | [
"y1837",
"y9030"
] | Being that the place serves tapas-style plates, be prepared to spend a little more $$$. | [
2288,
2375
] | neutral | {
"negative": 0.17849618196487427,
"positive": 0.12436950206756592,
"neutral": 0.6971343159675598
} | r1-0000022 | UbEhejiR-HxMMnHn6v9cEg | 5 | {
"positive": [],
"negative": [
"w144",
"w22",
"w4"
],
"neutral": [
"w296",
"w57"
],
"mixed": []
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000023 | [
"y12204",
"y12390"
] | Don't kiss the desserts, not your typical. | [
152,
194
] | positive | {
"negative": 0.041412170976400375,
"positive": 0.5366756916046143,
"neutral": 0.42191219329833984
} | r1-0000023 | BjN3IQJ43dojMdv9wl_Sdg | 5 | {
"positive": [],
"negative": [
"w393"
],
"neutral": [
"w129",
"w153",
"w222",
"w60"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000024 | [
"y17573",
"y18578"
] | I went for dinner on a Sunday night. | [
0,
36
] | neutral | {
"negative": 0.31058475375175476,
"positive": 0.19066913425922394,
"neutral": 0.4987460970878601
} | r1-0000024 | Gn99xywF2rb0cMhKM9q1YA | 1 | {
"positive": [],
"negative": [],
"neutral": [
"w135",
"w212",
"w439",
"w832"
],
"mixed": [
"w245"
]
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000025 | [
"y9164"
] | This is the place I always think of when a Strip hotel bar or swanky "lounge" charges me $12 for a precisely-measured (read: "stingy") something-and-soda. | [
0,
154
] | negative | {
"negative": 0.9054734110832214,
"positive": 0.006846081931143999,
"neutral": 0.08768061548471451
} | r1-0000025 | 4PUNdhRaBOkJM5GfJmADmA | 5 | {
"positive": [],
"negative": [
"w16",
"w193",
"w444",
"w566"
],
"neutral": [],
"mixed": [
"w299"
]
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000026 | [
"y11583"
] | Going to Arizona Tile! | [
429,
451
] | positive | {
"negative": 0.0066894968040287495,
"positive": 0.9693523049354553,
"neutral": 0.023958249017596245
} | r1-0000026 | z3vCmcosGT_36M2hzdtnBQ | 1 | {
"positive": [
"w109",
"w235"
],
"negative": [],
"neutral": [
"w114",
"w188",
"w50"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000028 | [
"y15462",
"y15471",
"y15472"
] | How can the price go up in a recession!?!?! | [
145,
188
] | negative | {
"negative": 0.7989655137062073,
"positive": 0.06025974452495575,
"neutral": 0.14077478647232056
} | r1-0000028 | hpTU0Ja222KsJx-CuvjIUA | 5 | {
"positive": [],
"negative": [
"w239",
"w279",
"w32"
],
"neutral": [
"w439"
],
"mixed": [
"w123"
]
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000029 | [
"y14529",
"y14827"
] | He deserves to be performing at a classier place. | [
1354,
1403
] | negative | {
"negative": 0.8333871960639954,
"positive": 0.006685946602374315,
"neutral": 0.15992677211761475
} | r1-0000029 | bw3vosBf_rHAoS0eGdA3CA | 5 | {
"positive": [
"w160",
"w205",
"w58"
],
"negative": [],
"neutral": [
"w297",
"w467"
],
"mixed": []
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000030 | [
"y20182",
"y7954"
] | We brought a bottle that we got from Napa. | [
369,
411
] | neutral | {
"negative": 0.3160594403743744,
"positive": 0.06637421250343323,
"neutral": 0.6175663471221924
} | r1-0000030 | O-yk04uEf8eIFRmKHDZvvg | 5 | {
"positive": [],
"negative": [],
"neutral": [
"w247",
"w33",
"w415",
"w544",
"w85"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000031 | [
"y13773",
"y14834",
"y20171"
] | We all ordered different things ranging from Ahi Tuna salad (I ordered it), harissa chicken, and tacos to pastas. | [
500,
613
] | neutral | {
"negative": 0.05926955118775368,
"positive": 0.3318365514278412,
"neutral": 0.608893871307373
} | r1-0000031 | znhAL0FGtHI1QvfOWVaKcA | 1 | {
"positive": [],
"negative": [],
"neutral": [
"w135",
"w195",
"w32",
"w395",
"w40"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
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r1-0000032 | [
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] | At the desk helping another customer. | [
2702,
2739
] | neutral | {
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} | neutral | {
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r1-0000033 | [
"y20226",
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] | We got there for the 2eme service. | [
272,
306
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} | neutral | {
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r1-0000034 | [
"y16154",
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] | These are not coney dogs like Detroit Coney Grill down the street or James Coney Island hotdogs. | [
244,
340
] | negative | {
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"split": "train",
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r1-0000035 | [
"y3780"
] | It starts with the letter "T". | [
1242,
1272
] | neutral | {
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r1-0000037 | [
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0,
18
] | neutral | {
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r1-0000038 | [
"y3622"
] | But thankfully 3 breads and rice. | [
477,
510
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"split": "train",
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r1-0000039 | [
"y2991"
] | We found some fabulous restaurants to explore. | [
1357,
1403
] | positive | {
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} | positive | {
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r1-0000041 | [
"y14583",
"y15728",
"y903"
] | She said that would take too long but reluctantly went and got the stuff to do that. | [
834,
918
] | positive | {
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"mixed": []
} | negative | {
"split": "train",
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r1-0000042 | [
"y14012"
] | If I had known this, I would have never switched. | [
739,
788
] | negative | {
"negative": 0.8083490133285522,
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} | negative | {
"split": "train",
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r1-0000043 | [
"y10860",
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] | I was very nervous to go to the DMV. | [
0,
36
] | negative | {
"negative": 0.5692477822303772,
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} | negative | {
"split": "train",
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} |
r1-0000044 | [
"y10458",
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] | But now I'm a confirmed kool aid drinker. | [
198,
239
] | neutral | {
"negative": 0.37934306263923645,
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r1-0000045 | [
"y10536"
] | I am a bit of a Barro's freak, and it's tops on my list for diet cheats... | [
0,
74
] | positive | {
"negative": 0.009173555299639702,
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} | positive | {
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r1-0000046 | [
"y18639",
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] | Make sure you ask for Mike. | [
330,
357
] | positive | {
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r1-0000047 | [
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] | We figured we would have a few drinks and a burger... we ended up trying all the appetizers on their happy hour menu and we tried their surf and turf burger. | [
241,
398
] | neutral | {
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r1-0000048 | [
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] | Always in a hurry in the morning. | [
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} | neutral | {
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r1-0000049 | [
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98
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r1-0000050 | [
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] | nor will you like 42% of restaurants on yelp. | [
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} | neutral | {
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r1-0000051 | [
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] | I never actually met this promoter. | [
292,
327
] | neutral | {
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} | neutral | {
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r1-0000052 | [
"y1577"
] | How can he miss us??? | [
665,
686
] | negative | {
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r1-0000053 | [
"y11835",
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] | Nothing comes even close to being half as good on quality, service, selection, pricing, knowledge, financing, etc. | [
703,
817
] | negative | {
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} | positive | {
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r1-0000054 | [
"y16231"
] | STEER CLEAR! | [
0,
12
] | positive | {
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} | negative | {
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r1-0000055 | [
"y2339"
] | Loud bang. | [
802,
812
] | neutral | {
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} | r1-0000055 | rXUL2N9WuLlnvGdPeCsecw | 1 | {
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} | neutral | {
"split": "train",
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} |
r1-0000056 | [
"y16412"
] | Its a safeway company so if you are from Cali use your Safeway Club Card. | [
57,
130
] | neutral | {
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} | r1-0000056 | XEetuhHYDhMUUuZetX8uGQ | 5 | {
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} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000057 | [
"y17219",
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"y4371"
] | The spinach was pretty flavorless, though. | [
1034,
1076
] | negative | {
"negative": 0.6507993936538696,
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} | r1-0000057 | kDKKtaUrfMbNbA9CuDhwpg | 5 | {
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} | negative | {
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} |
r1-0000058 | [
"y16136",
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] | I booked this hotel for a room on Jan 2 2015. | [
66,
111
] | negative | {
"negative": 0.9394829869270325,
"positive": 0.015047682449221611,
"neutral": 0.04546929523348808
} | r1-0000058 | FZryCvEUrtPZBFJxym1XFg | 1 | {
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} | neutral | {
"split": "train",
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} |
r1-0000059 | [
"y10361",
"y12272",
"y9827"
] | We ended up here Saturday afternoon as we waited for the ASU tailgating scene to get going (which it never really did). | [
0,
119
] | negative | {
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"positive": 0.06769336014986038,
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} | r1-0000059 | QUMr-uD10PptKRi-TOZUsQ | 5 | {
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],
"mixed": []
} | negative | {
"split": "train",
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} |
r1-0000060 | [
"y1892",
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] | Dog friendly? | [
0,
13
] | neutral | {
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"positive": 0.22970275580883026,
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} | r1-0000060 | zfwmtqh44cMvTq0D-dxXhg | 1 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000061 | [
"y4513",
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] | After measuring it we figured it would fit in our trunk but it didn't. | [
115,
185
] | negative | {
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} | r1-0000061 | 58xPo_OHDFLDQF5h5o5JRg | 5 | {
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} | negative | {
"split": "train",
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} |
r1-0000062 | [
"y4735"
] | I have eaten at this place many times over the years and always been happy with the food and the service. | [
0,
105
] | positive | {
"negative": 0.005712695419788361,
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} | r1-0000062 | PN03CRT5CUmsS1frIP998A | 1 | {
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} | positive | {
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} |
r1-0000063 | [
"y2228",
"y2750",
"y8975"
] | is unbarable. | [
246,
259
] | positive | {
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} | r1-0000063 | LmjXv4ooD3qSvodSs8F2Aw | 1 | {
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],
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} | negative | {
"split": "train",
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} |
r1-0000064 | [
"y15403",
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] | I call again. | [
1139,
1152
] | neutral | {
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} | r1-0000064 | eL7ZinuyMmW27uq_Lraz2Q | 1 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000065 | [
"y20186",
"y7804",
"y9147"
] | We asked for sriracha sauce three times to three different people before finally giving up. | [
389,
480
] | negative | {
"negative": 0.9765224456787109,
"positive": 0.0004989303415641189,
"neutral": 0.022978641092777252
} | r1-0000065 | DAH-sFwceKg5vRjc3iGNPw | 5 | {
"positive": [
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],
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} | negative | {
"split": "train",
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} |
r1-0000066 | [
"y17829",
"y8360"
] | I was seated and they explained how everything worked... | [
72,
129
] | positive | {
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"neutral": 0.2588832974433899
} | r1-0000066 | cwBBMP-TUyfodX23LKXOEA | 1 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000068 | [
"y4233"
] | Not the case with Irrigation Repair Service of Gilbert (IRS - don't be afraid of the acronym), which supplied the utmost competence on all three selling points. | [
174,
334
] | positive | {
"negative": 0.1365562230348587,
"positive": 0.8118839263916016,
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} | r1-0000068 | TUsHOJo4B3nKJ3TWH-gqsw | 5 | {
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"w57"
],
"negative": [],
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} | positive | {
"split": "train",
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} |
r1-0000070 | [
"y12192",
"y1297"
] | Do not buy from them until you compare!!!! | [
238,
280
] | negative | {
"negative": 0.9605700373649597,
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} | r1-0000070 | sMm3UGAmpF6W64MfBTU_JA | 5 | {
"positive": [],
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],
"mixed": []
} | negative | {
"split": "train",
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} |
r1-0000071 | [
"y10423"
] | It's served alongside our garlic bread and made fresh daily, so get yours before we run out!" | [
386,
479
] | positive | {
"negative": 0.0009642356890253723,
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} | r1-0000071 | kUZCmocgt0UrsbznGYY33Q | 1 | {
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} | positive | {
"split": "train",
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} |
r1-0000073 | [
"y1534",
"y2821"
] | really old guy missing teeth. | [
100,
129
] | neutral | {
"negative": 0.12784337997436523,
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} | r1-0000073 | i8jZfuGAeUF81aUKYkr93g | 5 | {
"positive": [],
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"w341"
]
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
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} |
r1-0000074 | [
"y11199",
"y9286"
] | I wasn't even issue a new key card when the gym transitioned from Gold's to EOS. | [
467,
547
] | negative | {
"negative": 0.822490930557251,
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} | r1-0000074 | QQi-9N2-aK3iyjAK3vmUNw | 1 | {
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],
"mixed": []
} | negative | {
"split": "train",
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} |
r1-0000075 | [
"y5908",
"y6407",
"y6423"
] | An update on Peoria Ford. | [
0,
25
] | neutral | {
"negative": 0.07865799218416214,
"positive": 0.41396957635879517,
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} | r1-0000075 | L4qc79vlYBaOBjaSjUFLVw | 1 | {
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} | neutral | {
"split": "train",
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} |
r1-0000076 | [
"y995"
] | He did a decent job on my fixes. | [
414,
446
] | positive | {
"negative": 0.0271324273198843,
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} | r1-0000076 | od2_4WZlcXM3PlZ7ElYS9w | 1 | {
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} | positive | {
"split": "train",
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} |
r1-0000077 | [
"y11045",
"y420"
] | Can't wait til the next time. | [
537,
566
] | positive | {
"negative": 0.013300797902047634,
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} | r1-0000077 | 5gYJQekxyOe-1-H7R27KMQ | 5 | {
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"mixed": []
} | positive | {
"split": "train",
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} |
r1-0000078 | [
"y11747",
"y19348"
] | Our server, Jesse, took our orders. | [
218,
253
] | positive | {
"negative": 0.010599385015666485,
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} | r1-0000078 | F_FoyRf7srm2dlNgw2PFGg | 1 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000080 | [
"y16498",
"y5279"
] | I didn't just remember those things and put them in my own run on sentence, that is how she said it. | [
1767,
1867
] | neutral | {
"negative": 0.37396419048309326,
"positive": 0.19372722506523132,
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} | r1-0000080 | mPOdSV56QdsoZ57vgAdvDg | 1 | {
"positive": [],
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"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000081 | [
"y18371",
"y18554",
"y7903"
] | Mine was an older shuffle iPod and hubby's was a newly purchased iPod touch, wasn't holding a charge and kept dying, as the battery kept draining ever so quickly (still under warranty, until next June). | [
583,
785
] | positive | {
"negative": 0.017564402893185616,
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} | r1-0000081 | ZcBUz52uSyE0Pa6wyy89kQ | 5 | {
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]
} | negative | {
"split": "train",
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"subset": "all",
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} |
r1-0000082 | [
"y19323",
"y3738",
"y3739"
] | Only been here once. | [
0,
20
] | neutral | {
"negative": 0.23316945135593414,
"positive": 0.04900443181395531,
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} | r1-0000082 | Z0i9PR2eFxVFumuFNt_xNg | 5 | {
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"mixed": []
} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000084 | [
"y3230",
"y3231",
"y507"
] | Our bill was over a grand after everything was said and done. | [
1540,
1601
] | negative | {
"negative": 0.7608275413513184,
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} | r1-0000084 | pfo5P8u7kG9v0Zt1HYnifQ | 5 | {
"positive": [
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],
"mixed": []
} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000085 | [
"y5772",
"y5920"
] | Also, I am not Korean and I am usually one of a few Caucasians in the restaurant when I am here. | [
482,
578
] | neutral | {
"negative": 0.46218833327293396,
"positive": 0.0746566653251648,
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} | r1-0000085 | HHtYoctYsBqDUcVgMjscOg | 5 | {
"positive": [
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],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
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} |
r1-0000086 | [
"y16270",
"y17874"
] | I was asked the obligatory question that every person that ever worked retail is to ask a customer. | [
469,
568
] | neutral | {
"negative": 0.04620305076241493,
"positive": 0.15265889465808868,
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} | r1-0000086 | Qp4X7baY_7StqgPHRWbttw | 5 | {
"positive": [],
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"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
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} |
r1-0000087 | [
"y15957",
"y8020"
] | They have unseated our defacto Thai restaurant and will now become of our our regular spots. | [
295,
387
] | negative | {
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} | r1-0000087 | v-oI-M2tujtkGClMn5GStA | 5 | {
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],
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} | neutral | {
"split": "train",
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} |
r1-0000088 | [
"y1712",
"y19198"
] | No liquor license and not sure if you can byob (cold Pacifico would have been nice). | [
421,
505
] | negative | {
"negative": 0.4608093798160553,
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} | r1-0000088 | Tw30QZWN9Tw1yenOCqxNAQ | 5 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000089 | [
"y16624",
"y1955"
] | I'm glad they could get rid of those bees. | [
911,
953
] | positive | {
"negative": 0.05263874679803848,
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} | r1-0000089 | pMTjEpeZafQZnZp2Z8XKnw | 5 | {
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],
"mixed": []
} | positive | {
"split": "train",
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} |
r1-0000090 | [
"y20418",
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] | With Nellis AFB in the area, you would think there would be more surplus stores, but there are not. | [
1077,
1176
] | neutral | {
"negative": 0.32454076409339905,
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} | r1-0000090 | EI25c_8M1KhhGZEOnM62MA | 5 | {
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"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000091 | [
"y15557",
"y9170"
] | If someone isn't ready when they say they are, don't wait for them because folks who are ready to go when they say they are expect you to be! | [
543,
684
] | positive | {
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} | r1-0000091 | spnjcNHgDnT2f6siNnkAuQ | 1 | {
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],
"mixed": []
} | positive | {
"split": "train",
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"subset": "all",
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} |
r1-0000093 | [
"y11599",
"y3104",
"y7086"
] | Its a take-out kind of place. | [
34,
63
] | neutral | {
"negative": 0.2126961648464203,
"positive": 0.03711751848459244,
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} | r1-0000093 | Q1tQEcs8VuBLMTYubbOQ9Q | 5 | {
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],
"mixed": []
} | neutral | {
"split": "train",
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} |
r1-0000094 | [
"y11765"
] | The third transaction went smooth. | [
626,
660
] | positive | {
"negative": 0.002713320776820183,
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} | r1-0000094 | jqGT4X-0bOgK4pcBfejA3Q | 1 | {
"positive": [
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],
"mixed": []
} | positive | {
"split": "train",
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"subset": "all",
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} |
r1-0000095 | [
"y12208",
"y236",
"y4826"
] | Do note that it IS supposed to be more bitter than sweet. | [
31,
88
] | neutral | {
"negative": 0.3217654824256897,
"positive": 0.13603706657886505,
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} | r1-0000095 | s3XO_CwWleezivnDDyX8ow | 5 | {
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],
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]
} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000096 | [
"y450"
] | The white and red sangrias were lovely. | [
928,
967
] | positive | {
"negative": 0.006733091082423925,
"positive": 0.7922220826148987,
"neutral": 0.20104487240314484
} | r1-0000096 | viJEF0iTv6NshyEptiuHRg | 1 | {
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],
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} | positive | {
"split": "train",
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} |
r1-0000097 | [
"y7193"
] | My car way sooo bad. | [
54,
74
] | negative | {
"negative": 0.9249998331069946,
"positive": 0.024158049374818802,
"neutral": 0.05084206908941269
} | r1-0000097 | hlitKEmQ29DI1DDjbDCY1g | 5 | {
"positive": [],
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],
"neutral": [],
"mixed": []
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000098 | [
"y10015",
"y18269"
] | The bathroom was clean. | [
437,
460
] | neutral | {
"negative": 0.03929327800869942,
"positive": 0.354475200176239,
"neutral": 0.6062314510345459
} | r1-0000098 | lhA6eGPE2H3N-Xa5FdUtyQ | 5 | {
"positive": [
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"mixed": []
} | positive | {
"split": "train",
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} |
r1-0000099 | [
"y12409",
"y12450"
] | Don't waste your time- give them a call! | [
395,
435
] | negative | {
"negative": 0.9967790246009827,
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"neutral": 0.0023292251862585545
} | r1-0000099 | 8-HYEnxljk3mUHuoHUtU6Q | 5 | {
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],
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],
"mixed": []
} | positive | {
"split": "train",
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"subset": "all",
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} |
r1-0000100 | [
"y13713",
"y16904",
"y2426"
] | The waitress even setup her own mobile hotspot for me to use as the restaurant's wi-fi was not working. | [
731,
834
] | positive | {
"negative": 0.03375811502337456,
"positive": 0.6903913617134094,
"neutral": 0.2758505046367645
} | r1-0000100 | boie3B2gnvVXSxoWQfrM9Q | 5 | {
"positive": [
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"w55"
],
"negative": [],
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"mixed": []
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000101 | [
"y2367"
] | I figured I had to go to this specific department because the sweaters were found here. | [
567,
654
] | neutral | {
"negative": 0.08521131426095963,
"positive": 0.3711188733577728,
"neutral": 0.5436697602272034
} | r1-0000101 | DdrD1aThrjHGtTl20JsMEw | 1 | {
"positive": [
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"w448",
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"w81"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
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} |
r1-0000102 | [
"y18158",
"y2140",
"y7196"
] | The crostini looked like a cheesy bread but ended up tasting more like a cheesy bread on steroids. | [
1810,
1908
] | negative | {
"negative": 0.9607546329498291,
"positive": 0.0007049888372421265,
"neutral": 0.03854048252105713
} | r1-0000102 | macUlFgt9KW81mIdMrO1Vg | 5 | {
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"w386"
],
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"mixed": [
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]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000103 | [
"y3621"
] | I went to the pool to party and had the best 4 hour nap. | [
23,
79
] | positive | {
"negative": 0.028758781030774117,
"positive": 0.938450276851654,
"neutral": 0.032790981233119965
} | r1-0000103 | _4EYIujSzAQsz5HCQTTSUg | 5 | {
"positive": [
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],
"negative": [],
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"mixed": [
"w805"
]
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000104 | [
"y2398"
] | I don't know what is going on at Stephanie street in Henderson, but it is turning into restaurant and junk food rowe of the southeast! | [
1528,
1662
] | negative | {
"negative": 0.5198332071304321,
"positive": 0.2143346071243286,
"neutral": 0.26583218574523926
} | r1-0000104 | sHRFZIkl8TNE1Zva8u_36Q | 5 | {
"positive": [],
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],
"mixed": [
"w666",
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]
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000105 | [
"y1285"
] | Books trickle out of crevices of walls and shelf's alike. | [
267,
324
] | neutral | {
"negative": 0.11211304366588593,
"positive": 0.17811284959316254,
"neutral": 0.7097740769386292
} | r1-0000105 | HHpRh50h8OkPo6XroWDH4Q | 5 | {
"positive": [],
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"neutral": [
"w130",
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"w241",
"w461",
"w84"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000106 | [
"y9203"
] | When you place your order, they will give you a number to place on your table for the food to be delivered. | [
164,
271
] | positive | {
"negative": 0.23111717402935028,
"positive": 0.4442256987094879,
"neutral": 0.324657142162323
} | r1-0000106 | bGXehDzmhMNEsi1UeFJGjQ | 5 | {
"positive": [
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"negative": [],
"neutral": [
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"w155",
"w252"
],
"mixed": []
} | neutral | {
"split": "train",
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"subset": "all",
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} |
r1-0000107 | [
"y4074"
] | I was curious about the Seafood Risotto for a kosher place, are they going to use Pollock? | [
1324,
1414
] | neutral | {
"negative": 0.11829402297735214,
"positive": 0.3616541624069214,
"neutral": 0.5200517773628235
} | r1-0000107 | FwtqHxxqrvIRTK4l_g_m1g | 1 | {
"positive": [],
"negative": [
"w938"
],
"neutral": [
"w132",
"w148",
"w26",
"w59"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000108 | [
"y13573"
] | I ordered to go so I could make it to my appointment on time. | [
189,
250
] | positive | {
"negative": 0.07309255003929138,
"positive": 0.5847066640853882,
"neutral": 0.34220078587532043
} | r1-0000108 | M0G5HEtRGjP3GwqJzH4xXw | 1 | {
"positive": [
"w27"
],
"negative": [],
"neutral": [
"w16",
"w263",
"w305",
"w8"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000109 | [
"y11974",
"y1660"
] | Definitely too much for one person. | [
281,
316
] | neutral | {
"negative": 0.4212559163570404,
"positive": 0.08036341518163681,
"neutral": 0.4983806312084198
} | r1-0000109 | Z664_SVwOxTusiLqkKKDuw | 5 | {
"positive": [],
"negative": [
"w258",
"w57",
"w879"
],
"neutral": [
"w195",
"w490"
],
"mixed": []
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000110 | [
"y13636",
"y6360"
] | Since I started seeing Dr. Mayer, the wait time post check in has always been less than 5 minutes for the pre-screening workout and then anither 5-10 minutes (at most) to see the doctor after being placed in an exam room. | [
252,
473
] | positive | {
"negative": 0.21935796737670898,
"positive": 0.5343391299247742,
"neutral": 0.24630293250083923
} | r1-0000110 | kMlRVNP2kyksPr4uQ2LlUg | 5 | {
"positive": [
"w18",
"w211",
"w53",
"w54"
],
"negative": [],
"neutral": [
"w26"
],
"mixed": []
} | positive | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000111 | [
"y13490"
] | First saw a Hakkasan in San Francisco but they were booked for the evening so we never got in. | [
0,
94
] | neutral | {
"negative": 0.1411745548248291,
"positive": 0.363070011138916,
"neutral": 0.4957554042339325
} | r1-0000111 | IUeVcPo-qp0jfth0cG50Eg | 5 | {
"positive": [
"w521"
],
"negative": [
"w115",
"w143",
"w22"
],
"neutral": [],
"mixed": [
"w525"
]
} | negative | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
r1-0000112 | [
"y4237",
"y8266"
] | My dogs never barked, so even if I missed the knocking, they wouldn't have. | [
500,
575
] | positive | {
"negative": 0.014877786859869957,
"positive": 0.8939515352249146,
"neutral": 0.09117067605257034
} | r1-0000112 | qZmm5Kxp4e3qyPBjPLoD1A | 1 | {
"positive": [],
"negative": [
"w43"
],
"neutral": [
"w288",
"w326",
"w48",
"w594"
],
"mixed": []
} | neutral | {
"split": "train",
"round": 1,
"subset": "all",
"model_in_the_loop": "RoBERTa"
} |
DynaSent: Dynamic Sentiment Analysis Dataset
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original DynaSent Repository.
Contents
Citation
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2020. DynaSent: A dynamic benchmark for sentiment analysis. Ms., Stanford University and Facebook AI Research.
@article{potts-etal-2020-dynasent,
title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe},
journal={arXiv preprint arXiv:2012.15349},
url={https://arxiv.org/abs/2012.15349},
year={2020}}
Dataset files
The dataset is dynasent-v1.1.zip, which is included in this repository. v1.1
differs from v1
only in that v1.1
has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata.
The dataset consists of two rounds, each with a train/dev/test split:
Round 1: Naturally occurring sentences
dynasent-v1.1-round01-yelp-train.jsonl
dynasent-v1.1-round01-yelp-dev.jsonl
dynasent-v1.1-round01-yelp-test.jsonl
Round 1: Sentences crowdsourced using Dynabench
dynasent-v1.1-round02-dynabench-train.jsonl
dynasent-v1.1-round02-dynabench-dev.jsonl
dynasent-v1.1-round02-dynabench-test.jsonl
SST-dev revalidation
The dataset also contains a version of the Stanford Sentiment Treebank dev set in our format with labels from our validation task:
sst-dev-validated.jsonl
Quick start
This function can be used to load any subset of the files:
import json
def load_dataset(*src_filenames, labels=None):
data = []
for filename in src_filenames:
with open(filename) as f:
for line in f:
d = json.loads(line)
if labels is None or d['gold_label'] in labels:
data.append(d)
return data
For example, to create a Round 1 train set restricting to examples with ternary gold labels:
import os
r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl')
ternary_labels = ('positive', 'negative', 'neutral')
r1_train = load_dataset(r1_train_filename, labels=ternary_labels)
X_train, y_train = zip(*[(d['sentence'], d['gold_label']) for d in r1_train])
Data format
Round 1 format
{'hit_ids': ['y5238'],
'sentence': 'Roto-Rooter is always good when you need someone right away.',
'indices_into_review_text': [0, 60],
'model_0_label': 'positive',
'model_0_probs': {'negative': 0.01173639390617609,
'positive': 0.7473671436309814,
'neutral': 0.24089649319648743},
'text_id': 'r1-0000001',
'review_id': 'IDHkeGo-nxhqX4Exkdr08A',
'review_rating': 1,
'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
Details:
'hit_ids'
: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.'sentence'
: The example text.'indices_into_review_text':
indices of'sentence'
into the original review in the Yelp Academic Dataset.'model_0_label'
: prediction of Model 0 as described in the paper. The possible values are'positive'
,'negative'
, and'neutral'
.'model_0_probs'
: probability distribution predicted by Model 0. The keys are('positive', 'negative', 'neutral')
and the values are floats.'text_id'
: unique identifier for this entry.'review_id'
: review-level identifier for the review from the Yelp Academic Dataset containing'sentence'
.'review_rating'
: review-level star-rating for the review containing'sentence'
in the Yelp Academic Dataset. The possible values are1
,2
,3
,4
, and5
.'label_distribution':
response distribution from the MTurk validation task. The keys are('positive', 'negative', 'neutral')
and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.'gold_label'
: the label chosen by at least three of the five workers if there is one (possible values:'positive'
,'negative'
, 'neutral'
, and'mixed'
), elseNone
.
Here is some code one could use to augment a dataset, as loaded by load_dataset
, with a field giving the full review text from the Yelp Academic Dataset:
import json
def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'):
index = {}
with open(yelp_src_filename) as f:
for line in f:
d = json.loads(line)
index[d['review_id']] = d['text']
return index
yelp_index = index_yelp_reviews()
def add_review_text_round1(dataset, yelp_index):
for d in dataset:
review_text = yelp_index[d['text_id']]
# Check that we can find the sentence as expected:
start, end = d['indices_into_review_text']
assert review_text[start: end] == d['sentence']
d['review_text'] = review_text
return dataset
Round 2 format
{'hit_ids': ['y22661'],
'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.",
'sentence_author': 'w250',
'has_prompt': True,
'prompt_data': {'indices_into_review_text': [2093, 2213],
'review_rating': 5,
'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.",
'review_id': 'Krm4kSIb06BDHternF4_pA'},
'model_1_label': 'positive',
'model_1_probs': {'negative': 0.29140257835388184,
'positive': 0.6788994669914246,
'neutral': 0.029697999358177185},
'text_id': 'r2-0000001',
'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'],
'negative': [],
'neutral': [],
'mixed': ['w174']},
'gold_label': 'positive'}
Details:
'hit_ids'
: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.'sentence'
: The example text.'sentence_author'
: Anonymized MTurk id of the worker who wrote'sentence'
. These are from the same family of ids as used in'label_distribution'
, but this id is never one of the ids in'label_distribution'
for this example.'has_prompt'
:True
if the'sentence'
was written with a Prompt elseFalse
.'prompt_data'
: None if'has_prompt'
is False, else:'indices_into_review_text'
: indices of'prompt_sentence'
into the original review in the Yelp Academic Dataset.'review_rating'
: review-level star-rating for the review containing'sentence'
in the Yelp Academic Dataset.'prompt_sentence'
: The prompt text.'review_id'
: review-level identifier for the review from the Yelp Academic Dataset containing'prompt_sentence'
.
'model_1_label'
: prediction of Model 1 as described in the paper. The possible values are'positive'
,'negative'
, and 'neutral'
.'model_1_probs'
: probability distribution predicted by Model 1. The keys are('positive', 'negative', 'neutral')
and the values are floats.'text_id'
: unique identifier for this entry.'label_distribution'
: response distribution from the MTurk validation task. The keys are('positive', 'negative', 'neutral')
and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.'gold_label'
: the label chosen by at least three of the five workers if there is one (possible values:'positive'
,'negative'
, 'neutral'
, and'mixed'
), elseNone
.
To add the review texts to the 'prompt_data'
field, one can extend the code above for Round 1 with the following function:
def add_review_text_round2(dataset, yelp_index):
for d in dataset:
if d['has_prompt']:
prompt_data = d['prompt_data']
review_text = yelp_index[prompt_data['review_id']]
# Check that we can find the sentence as expected:
start, end = prompt_data['indices_into_review_text']
assert review_text[start: end] == prompt_data['prompt_sentence']
prompt_data['review_text'] = review_text
return dataset
SST-dev format
{'hit_ids': ['s20533'],
'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.',
'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))',
'text_id': 'sst-dev-validate-0000437',
'sst_label': '4',
'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
Details:
'hit_ids'
: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.'sentence'
: The example text.'tree'
: The parsetree for the example as given in the SST distribution.'text_id'
: A new identifier for this example.'sst_label'
: The root-node label from the SST. Possible values'0'
,'1'
'2'
,'3'
, and'4'
.'label_distribution':
response distribution from the MTurk validation task. The keys are('positive', 'negative', 'neutral')
and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.'gold_label'
: the label chosen by at least three of the five workers if there is one (possible values:'positive'
,'negative'
, 'neutral'
, and'mixed'
), elseNone
.
Models
Model 0 and Model 1 from the paper are available here:
https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing
This repository includes a Python module dynasent_models.py
that provides a Hugging Face-based wrapper around these (PyTorch) models. Simple examples:
import os
from dynasent_models import DynaSentModel
# `dynasent_model0` should be downloaded from the above Google Drive link and
# placed in the `models` directory. `dynasent_model1` works the same way.
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
examples = [
"superb",
"They said the experience would be amazing, and they were right!",
"They said the experience would be amazing, and they were wrong!"]
model.predict(examples)
This should return the list ['positive', 'positive', 'negative']
.
The predict_proba
method provides access to the predicted distribution over the class labels; see the demo at the bottom of dynasent_models.py
for details.
The following code uses load_dataset
from above to reproduce the Round 2 dev-set report on Model 0 from the paper:
import os
from sklearn.metrics import classification_report
from dynasent_models import DynaSentModel
dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl')
dev = load_dataset(dev_filename)
X_dev, y_dev = zip(*[(d['sentence'], d['gold_label']) for d in dev])
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
preds = model.predict(X_dev)
print(classification_report(y_dev, preds, digits=3))
For a fuller report on these models, see our paper and our model card.
Other files
Analysis notebooks
The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper:
analyses_comparative.ipynb
analysis_round1.ipynb
analysis_round2.ipynb
analysis_sst_dev_revalidate.ipynb
The Python module dynasent_utils.py
contains functions that support those notebooks, and dynasent.mplstyle
helps with styling the plots.
Datasheet
The Datasheet for our dataset:
Model Card
The Model Card for our models:
Tests
The module test_dataset.py
contains PyTest tests for the dataset. To use it, run
py.test -vv test_dataset.py
in the root directory of this repository.
Validation HIT code
The file validation-hit-contents.html
contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window.
License
DynaSent has a Creative Commons Attribution 4.0 International License.
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