Datasets:

dk-crazydiv

/

huggingface-modelhub

like 4

zanderbush/ForceWords2

2021-05-23T13:57:08.000Z

text-generation

zanderbush

transformers

zanderbush/ForceWordsArvix

2021-05-23T13:59:12.000Z

text-generation

zanderbush

transformers

zanderbush/ForceWordsT5

2020-12-07T02:59:10.000Z

text2text-generation

zanderbush

transformers

zanderbush/GPTTitle

2021-05-23T14:00:00.000Z

text-generation

zanderbush

transformers

zanderbush/Intellectual

2021-05-23T14:01:00.000Z

text-generation

zanderbush

transformers

zanderbush/Paraphrase

2021-05-23T14:02:13.000Z

text-generation

zanderbush

transformers

{Input} -> {Output} from transformers import pipeline generator = pipeline('text-generation',model='zandebush/Paraphrase') generator("New York is home to the New York Knicks. ->", num_return_sequences=5) Note: T5 is likely better suited for paraphrasing. That said, this model will afford you mediocre results.

zanderbush/T5ForceWords

2021-01-11T22:59:04.000Z

text2text-generation

zanderbush

transformers

zanderbush/T6

2021-02-15T21:03:57.000Z

text2text-generation

zanderbush

transformers

zanderbush/T7

2021-02-22T03:37:18.000Z

text2text-generation

zanderbush

transformers

zanderbush/TryTokenizer

2021-05-23T14:02:44.000Z

text-generation

zanderbush

transformers

zanderbush/VBG_GPT2

2021-05-23T14:03:30.000Z

text-generation

zanderbush

transformers

zanderbush/VBG_T5

2021-02-09T02:55:58.000Z

text2text-generation

zanderbush

transformers

zanelim/singbert-large-sg

2021-05-20T09:36:17.000Z

zanelim

transformers

--- language: en tags: - singapore - sg - singlish - malaysia - ms - manglish - bert-large-uncased license: mit datasets: - reddit singapore, malaysia - hardwarezone widget: - text: "kopi c siew [MASK]" - text: "die [MASK] must try" --- # Model name SingBert Large - Bert for Singlish (SG) and Manglish (MY). ## Model description Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on [singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data. ## Intended uses & limitations #### How to use ```python >>> from transformers import pipeline >>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg') >>> nlp("kopi c siew [MASK]") [{'sequence': '[CLS] kopi c siew dai [SEP]', 'score': 0.9003700017929077, 'token': 18765, 'token_str': 'dai'}, {'sequence': '[CLS] kopi c siew mai [SEP]', 'score': 0.0779474675655365, 'token': 14736, 'token_str': 'mai'}, {'sequence': '[CLS] kopi c siew. [SEP]', 'score': 0.0032227332703769207, 'token': 1012, 'token_str': '.'}, {'sequence': '[CLS] kopi c siew bao [SEP]', 'score': 0.0017727474914863706, 'token': 25945, 'token_str': 'bao'}, {'sequence': '[CLS] kopi c siew peng [SEP]', 'score': 0.0012526646023616195, 'token': 26473, 'token_str': 'peng'}] >>> nlp("one teh c siew dai, and one kopi [MASK]") [{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]', 'score': 0.5249741077423096, 'token': 1012, 'token_str': '.'}, {'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]', 'score': 0.27349168062210083, 'token': 1051, 'token_str': 'o'}, {'sequence': '[CLS] one teh c siew dai, and one kopi peng [SEP]', 'score': 0.057190295308828354, 'token': 26473, 'token_str': 'peng'}, {'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]', 'score': 0.04022320732474327, 'token': 1039, 'token_str': 'c'}, {'sequence': '[CLS] one teh c siew dai, and one kopi? [SEP]', 'score': 0.01191170234233141, 'token': 1029, 'token_str': '?'}] >>> nlp("die [MASK] must try") [{'sequence': '[CLS] die die must try [SEP]', 'score': 0.9921030402183533, 'token': 3280, 'token_str': 'die'}, {'sequence': '[CLS] die also must try [SEP]', 'score': 0.004993876442313194, 'token': 2036, 'token_str': 'also'}, {'sequence': '[CLS] die liao must try [SEP]', 'score': 0.000317625846946612, 'token': 727, 'token_str': 'liao'}, {'sequence': '[CLS] die still must try [SEP]', 'score': 0.0002260878391098231, 'token': 2145, 'token_str': 'still'}, {'sequence': '[CLS] die i must try [SEP]', 'score': 0.00016935862367972732, 'token': 1045, 'token_str': 'i'}] >>> nlp("dont play [MASK] leh") [{'sequence': '[CLS] dont play play leh [SEP]', 'score': 0.9079819321632385, 'token': 2377, 'token_str': 'play'}, {'sequence': '[CLS] dont play punk leh [SEP]', 'score': 0.006846973206847906, 'token': 7196, 'token_str': 'punk'}, {'sequence': '[CLS] dont play games leh [SEP]', 'score': 0.004041737411171198, 'token': 2399, 'token_str': 'games'}, {'sequence': '[CLS] dont play politics leh [SEP]', 'score': 0.003728888463228941, 'token': 4331, 'token_str': 'politics'}, {'sequence': '[CLS] dont play cheat leh [SEP]', 'score': 0.0032805048394948244, 'token': 21910, 'token_str': 'cheat'}] >>> nlp("confirm plus [MASK]") {'sequence': '[CLS] confirm plus chop [SEP]', 'score': 0.9749826192855835, 'token': 24494, 'token_str': 'chop'}, {'sequence': '[CLS] confirm plus chopped [SEP]', 'score': 0.017554156482219696, 'token': 24881, 'token_str': 'chopped'}, {'sequence': '[CLS] confirm plus minus [SEP]', 'score': 0.002725469646975398, 'token': 15718, 'token_str': 'minus'}, {'sequence': '[CLS] confirm plus guarantee [SEP]', 'score': 0.000900257145985961, 'token': 11302, 'token_str': 'guarantee'}, {'sequence': '[CLS] confirm plus one [SEP]', 'score': 0.0004384620988275856, 'token': 2028, 'token_str': 'one'}] >>> nlp("catch no [MASK]") [{'sequence': '[CLS] catch no ball [SEP]', 'score': 0.9381157159805298, 'token': 3608, 'token_str': 'ball'}, {'sequence': '[CLS] catch no balls [SEP]', 'score': 0.060842301696538925, 'token': 7395, 'token_str': 'balls'}, {'sequence': '[CLS] catch no fish [SEP]', 'score': 0.00030917322146706283, 'token': 3869, 'token_str': 'fish'}, {'sequence': '[CLS] catch no breath [SEP]', 'score': 7.552534952992573e-05, 'token': 3052, 'token_str': 'breath'}, {'sequence': '[CLS] catch no tail [SEP]', 'score': 4.208395694149658e-05, 'token': 5725, 'token_str': 'tail'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('zanelim/singbert-large-sg') model = BertModel.from_pretrained("zanelim/singbert-large-sg") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained("zanelim/singbert-large-sg") model = TFBertModel.from_pretrained("zanelim/singbert-large-sg") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` #### Limitations and bias This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias. ## Training data Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew) corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`. ## Training procedure Initialized with [bert large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights). Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab. Pre-training was further finetuned on training data with the following hyperparameters * train_batch_size: 512 * max_seq_length: 128 * num_train_steps: 300000 * num_warmup_steps: 5000 * learning_rate: 2e-5 * hardware: TPU v3-8

zanelim/singbert-lite-sg

2020-12-11T22:05:08.000Z

zanelim

transformers

--- language: en tags: - singapore - sg - singlish - malaysia - ms - manglish - albert-base-v2 license: mit datasets: - reddit singapore, malaysia - hardwarezone widget: - text: "dont play [MASK] leh" - text: "die [MASK] must try" --- # Model name SingBert Lite - Bert for Singlish (SG) and Manglish (MY). ## Model description Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the lite-version, which was initialized from [Albert base v2](https://github.com/google-research/albert#albert), with pre-training finetuned on [singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data. ## Intended uses & limitations #### How to use ```python >>> from transformers import pipeline >>> nlp = pipeline('fill-mask', model='zanelim/singbert-lite-sg') >>> nlp("die [MASK] must try") [{'sequence': '[CLS] die die must try[SEP]', 'score': 0.7731555700302124, 'token': 1327, 'token_str': '▁die'}, {'sequence': '[CLS] die also must try[SEP]', 'score': 0.04763784259557724, 'token': 67, 'token_str': '▁also'}, {'sequence': '[CLS] die still must try[SEP]', 'score': 0.01859409362077713, 'token': 174, 'token_str': '▁still'}, {'sequence': '[CLS] die u must try[SEP]', 'score': 0.015824034810066223, 'token': 287, 'token_str': '▁u'}, {'sequence': '[CLS] die is must try[SEP]', 'score': 0.011271446943283081, 'token': 25, 'token_str': '▁is'}] >>> nlp("dont play [MASK] leh") [{'sequence': '[CLS] dont play play leh[SEP]', 'score': 0.4365769624710083, 'token': 418, 'token_str': '▁play'}, {'sequence': '[CLS] dont play punk leh[SEP]', 'score': 0.06880936771631241, 'token': 6769, 'token_str': '▁punk'}, {'sequence': '[CLS] dont play game leh[SEP]', 'score': 0.051739856600761414, 'token': 250, 'token_str': '▁game'}, {'sequence': '[CLS] dont play games leh[SEP]', 'score': 0.045703962445259094, 'token': 466, 'token_str': '▁games'}, {'sequence': '[CLS] dont play around leh[SEP]', 'score': 0.013458190485835075, 'token': 140, 'token_str': '▁around'}] >>> nlp("catch no [MASK]") [{'sequence': '[CLS] catch no ball[SEP]', 'score': 0.6197211146354675, 'token': 1592, 'token_str': '▁ball'}, {'sequence': '[CLS] catch no balls[SEP]', 'score': 0.08441998809576035, 'token': 7152, 'token_str': '▁balls'}, {'sequence': '[CLS] catch no joke[SEP]', 'score': 0.0676785409450531, 'token': 8186, 'token_str': '▁joke'}, {'sequence': '[CLS] catch no?[SEP]', 'score': 0.040638409554958344, 'token': 60, 'token_str': '?'}, {'sequence': '[CLS] catch no one[SEP]', 'score': 0.03546864539384842, 'token': 53, 'token_str': '▁one'}] >>> nlp("confirm plus [MASK]") [{'sequence': '[CLS] confirm plus chop[SEP]', 'score': 0.9608421921730042, 'token': 17144, 'token_str': '▁chop'}, {'sequence': '[CLS] confirm plus guarantee[SEP]', 'score': 0.011784233152866364, 'token': 9120, 'token_str': '▁guarantee'}, {'sequence': '[CLS] confirm plus confirm[SEP]', 'score': 0.010571340098977089, 'token': 10265, 'token_str': '▁confirm'}, {'sequence': '[CLS] confirm plus egg[SEP]', 'score': 0.0033525123726576567, 'token': 6387, 'token_str': '▁egg'}, {'sequence': '[CLS] confirm plus bet[SEP]', 'score': 0.0008760977652855217, 'token': 5676, 'token_str': '▁bet'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('zanelim/singbert-lite-sg') model = AlbertModel.from_pretrained("zanelim/singbert-lite-sg") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained("zanelim/singbert-lite-sg") model = TFAlbertModel.from_pretrained("zanelim/singbert-lite-sg") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` #### Limitations and bias This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias. ## Training data Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew) corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`. ## Training procedure Initialized with [albert base v2](https://github.com/google-research/albert#albert) vocab and checkpoints (pre-trained weights). Pre-training was further finetuned on training data with the following hyperparameters * train_batch_size: 4096 * max_seq_length: 128 * num_train_steps: 125000 * num_warmup_steps: 5000 * learning_rate: 0.00176 * hardware: TPU v3-8

zanelim/singbert

2021-05-20T09:38:41.000Z

zanelim

transformers

--- language: en tags: - singapore - sg - singlish - malaysia - ms - manglish - bert-base-uncased license: mit datasets: - reddit singapore, malaysia - hardwarezone widget: - text: "kopi c siew [MASK]" - text: "die [MASK] must try" --- # Model name SingBert - Bert for Singlish (SG) and Manglish (MY). ## Model description [BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on [singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data. ## Intended uses & limitations #### How to use ```python >>> from transformers import pipeline >>> nlp = pipeline('fill-mask', model='zanelim/singbert') >>> nlp("kopi c siew [MASK]") [{'sequence': '[CLS] kopi c siew dai [SEP]', 'score': 0.5092713236808777, 'token': 18765, 'token_str': 'dai'}, {'sequence': '[CLS] kopi c siew mai [SEP]', 'score': 0.3515934646129608, 'token': 14736, 'token_str': 'mai'}, {'sequence': '[CLS] kopi c siew bao [SEP]', 'score': 0.05576375499367714, 'token': 25945, 'token_str': 'bao'}, {'sequence': '[CLS] kopi c siew. [SEP]', 'score': 0.006019321270287037, 'token': 1012, 'token_str': '.'}, {'sequence': '[CLS] kopi c siew sai [SEP]', 'score': 0.0038361591286957264, 'token': 18952, 'token_str': 'sai'}] >>> nlp("one teh c siew dai, and one kopi [MASK].") [{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]', 'score': 0.6176503300666809, 'token': 1039, 'token_str': 'c'}, {'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]', 'score': 0.21094971895217896, 'token': 1051, 'token_str': 'o'}, {'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]', 'score': 0.13027705252170563, 'token': 1012, 'token_str': '.'}, {'sequence': '[CLS] one teh c siew dai, and one kopi! [SEP]', 'score': 0.004680239595472813, 'token': 999, 'token_str': '!'}, {'sequence': '[CLS] one teh c siew dai, and one kopi w [SEP]', 'score': 0.002034128177911043, 'token': 1059, 'token_str': 'w'}] >>> nlp("dont play [MASK] leh") [{'sequence': '[CLS] dont play play leh [SEP]', 'score': 0.9281464219093323, 'token': 2377, 'token_str': 'play'}, {'sequence': '[CLS] dont play politics leh [SEP]', 'score': 0.010990909300744534, 'token': 4331, 'token_str': 'politics'}, {'sequence': '[CLS] dont play punk leh [SEP]', 'score': 0.005583590362221003, 'token': 7196, 'token_str': 'punk'}, {'sequence': '[CLS] dont play dirty leh [SEP]', 'score': 0.0025784350000321865, 'token': 6530, 'token_str': 'dirty'}, {'sequence': '[CLS] dont play cheat leh [SEP]', 'score': 0.0025066907983273268, 'token': 21910, 'token_str': 'cheat'}] >>> nlp("catch no [MASK]") [{'sequence': '[CLS] catch no ball [SEP]', 'score': 0.7922210693359375, 'token': 3608, 'token_str': 'ball'}, {'sequence': '[CLS] catch no balls [SEP]', 'score': 0.20503675937652588, 'token': 7395, 'token_str': 'balls'}, {'sequence': '[CLS] catch no tail [SEP]', 'score': 0.0006608376861549914, 'token': 5725, 'token_str': 'tail'}, {'sequence': '[CLS] catch no talent [SEP]', 'score': 0.0002158183924620971, 'token': 5848, 'token_str': 'talent'}, {'sequence': '[CLS] catch no prisoners [SEP]', 'score': 5.3481446229852736e-05, 'token': 5895, 'token_str': 'prisoners'}] >>> nlp("confirm plus [MASK]") [{'sequence': '[CLS] confirm plus chop [SEP]', 'score': 0.992355227470398, 'token': 24494, 'token_str': 'chop'}, {'sequence': '[CLS] confirm plus one [SEP]', 'score': 0.0037301010452210903, 'token': 2028, 'token_str': 'one'}, {'sequence': '[CLS] confirm plus minus [SEP]', 'score': 0.0014284878270700574, 'token': 15718, 'token_str': 'minus'}, {'sequence': '[CLS] confirm plus 1 [SEP]', 'score': 0.0011354683665558696, 'token': 1015, 'token_str': '1'}, {'sequence': '[CLS] confirm plus chopped [SEP]', 'score': 0.0003804611915256828, 'token': 24881, 'token_str': 'chopped'}] >>> nlp("die [MASK] must try") [{'sequence': '[CLS] die die must try [SEP]', 'score': 0.9552758932113647, 'token': 3280, 'token_str': 'die'}, {'sequence': '[CLS] die also must try [SEP]', 'score': 0.03644804656505585, 'token': 2036, 'token_str': 'also'}, {'sequence': '[CLS] die liao must try [SEP]', 'score': 0.003282855963334441, 'token': 727, 'token_str': 'liao'}, {'sequence': '[CLS] die already must try [SEP]', 'score': 0.0004937972989864647, 'token': 2525, 'token_str': 'already'}, {'sequence': '[CLS] die hard must try [SEP]', 'score': 0.0003659659414552152, 'token': 2524, 'token_str': 'hard'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('zanelim/singbert') model = BertModel.from_pretrained("zanelim/singbert") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained("zanelim/singbert") model = TFBertModel.from_pretrained("zanelim/singbert") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` #### Limitations and bias This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias. ## Training data Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew) corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`. ## Training procedure Initialized with [bert base uncased](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights). Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab. Pre-training was further finetuned on training data with the following hyperparameters * train_batch_size: 512 * max_seq_length: 128 * num_train_steps: 300000 * num_warmup_steps: 5000 * learning_rate: 2e-5 * hardware: TPU v3-8

zbmain/bert_cn_finetuning

2021-05-20T09:39:42.000Z

fill-mask

zbmain

transformers

bert_cn_finetuning model

zbmain/bert_finetuning_test

2020-11-23T19:59:14.000Z

zbmain

zbmain/test

2020-11-24T12:12:29.000Z

zbmain

123

zein/ArXivBert

2021-04-19T18:59:13.000Z

zein

zemin/trans_model

2021-02-10T18:35:40.000Z

zemin

zeonai/deepdelve-model1-qa

2021-05-20T09:40:39.000Z

fill-mask

zeonai

transformers

zeonai/deepdelve-model2-qa

2021-05-20T09:41:32.000Z

fill-mask

zeonai

transformers

zeropointbreakthrough/test

2021-06-02T00:57:13.000Z

zeropointbreakthrough

zhangchulong/bert-large-cased-1

2020-12-23T08:06:05.000Z

zhangchulong

zhangchulong/bert-large-cased-wwm

2020-12-23T03:40:24.000Z

zhangchulong

zhangqi/bert-base-chinese

2021-02-04T12:55:53.000Z

zhangqi

zhangqi/bert-base-uncased

2021-02-05T04:06:31.000Z

zhangqi

zhangqi/bert_base_chinese

2021-02-04T14:14:33.000Z

zhangqi

zhangqi/model_name

2021-02-04T14:22:03.000Z

zhangqi

zhangxy-2019/cu_dstc9_dialoGPT

2021-05-23T14:05:15.000Z

text-generation

zhangxy-2019

transformers

zhangxy-2019/cunlp-gpt2-dialog

2021-05-23T14:07:17.000Z

text-generation

zhangxy-2019

transformers

zhaochaocs/storygen

2021-02-23T22:29:45.000Z

zhaochaocs

zhaochongshan/bert_cn_finetunning

2021-05-08T03:45:51.000Z

zhaochongshan

zharry29/goal_benchmark_bert

2021-05-20T09:42:25.000Z

zharry29

transformers

zharry29/goal_benchmark_gpt

2021-05-23T14:08:46.000Z

zharry29

transformers

zharry29/goal_benchmark_roberta

2021-05-20T23:25:11.000Z

zharry29

transformers

zharry29/goal_benchmark_xlnet

2020-09-16T20:02:36.000Z

zharry29

transformers

zharry29/intent_enwh_rl

2020-09-16T20:10:41.000Z

zharry29

transformers

zharry29/intent_enwh_xlmr

2020-09-16T20:11:13.000Z

zharry29

transformers

zharry29/intent_fb-en_id_rl

2021-05-20T23:27:13.000Z

zharry29

transformers

zharry29/intent_fb-en_id_xlmr

2021-05-20T23:30:29.000Z

zharry29

transformers

zharry29/intent_fb-en_wh_id_rl

2021-05-20T23:33:07.000Z

zharry29

transformers

zharry29/intent_fb-es_enwh_id

2020-09-16T20:13:57.000Z

zharry29

transformers

zharry29/intent_fb-es_id

2020-09-16T20:14:32.000Z

zharry29

transformers

zharry29/intent_fb-es_wh_id

2020-09-16T20:15:03.000Z

zharry29

transformers

zharry29/intent_fb-th_enwh_id

2020-09-16T20:15:38.000Z

zharry29

transformers

zharry29/intent_fb-th_id

2020-09-16T20:16:29.000Z

zharry29

transformers

zharry29/intent_fb-th_wh_id

2020-09-16T20:17:00.000Z

zharry29

transformers

zharry29/intent_sgd_id

2021-05-20T23:36:23.000Z

zharry29

transformers

zharry29/intent_sgd_wh_id

2021-05-20T23:38:40.000Z

zharry29

transformers

zharry29/intent_snips_id

2021-05-20T23:47:11.000Z

zharry29

transformers

zharry29/intent_snips_wh_id

2021-05-20T23:49:50.000Z

zharry29

transformers

zharry29/intent_thwh

2020-09-16T20:44:55.000Z

zharry29

transformers

zharry29/order_benchmark_bert

2021-05-20T09:43:21.000Z

zharry29

transformers

zharry29/order_benchmark_gpt

2021-05-23T14:09:14.000Z

zharry29

transformers

zharry29/order_benchmark_roberta

2021-05-20T23:51:12.000Z

zharry29

transformers

zharry29/order_benchmark_xlnet

2020-09-16T20:03:11.000Z

zharry29

transformers

zharry29/step_benchmark_bert

2021-05-20T09:44:40.000Z

zharry29

transformers

zharry29/step_benchmark_gpt

2021-05-23T14:09:43.000Z

zharry29

transformers

zharry29/step_benchmark_roberta

2021-05-20T23:52:30.000Z

zharry29

transformers

zharry29/step_benchmark_xlnet

2020-09-16T19:57:55.000Z

zharry29

transformers

zhe/sst2

2021-04-24T10:13:56.000Z

zhe

zhewhen/pairwise_similarity

2021-05-28T16:10:42.000Z

zhewhen

zhiheng-huang/bert-base-uncased-embedding-relative-key-query

2021-05-20T09:45:59.000Z

fill-mask

zhiheng-huang

transformers

zhiheng-huang/bert-base-uncased-embedding-relative-key

2021-05-20T09:46:58.000Z

fill-mask

zhiheng-huang

transformers

zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query

2021-05-20T09:48:50.000Z

fill-mask

zhiheng-huang

transformers

zichju/Eelai

2021-01-12T00:53:34.000Z

zichju

zitterbewegung/DialoGPT-medium-ja

2021-05-23T14:11:28.000Z

text-generation

zitterbewegung

transformers

zjt/123

2021-05-11T00:58:44.000Z

zjt

zlucia/bert-double

2021-06-05T22:34:49.000Z

fill-mask

zlucia

transformers

--- language: en pipeline_tag: fill-mask --- ### BERT (double) Model and tokenizer files for BERT (double) model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://arxiv.org/abs/2104.08671). ### Training Data BERT (double) is pretrained using the same English Wikipedia corpus that the base BERT model (uncased, 110M parameters), [bert-base-uncased](https://huggingface.co/bert-base-uncased), was pretrained on. For more information on the pretraining corpus, refer to the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. ### Training Objective This model is initialized with the base BERT model (uncased, 110M parameters), [bert-base-uncased](https://huggingface.co/bert-base-uncased), and trained for an additional 1M steps on the MLM and NSP objective. This facilitates a direct comparison to our BERT-based models for the legal domain, which are also pretrained for 2M total steps. - Legal-BERT: zlucia/legalbert (https://huggingface.co/zlucia/legalbert) - Custom Legal-BERT: zlucia/custom-legalbert (https://huggingface.co/zlucia/custom-legalbert) ### Usage Please see the [casehold repository](https://github.com/reglab/casehold) for scripts that support computing pretrain loss and finetuning on BERT (double) for classification and multiple choice tasks described in the paper: Overruling, Terms of Service, CaseHOLD. See `demo.ipynb` in the casehold repository for details on calculating domain specificity (DS) scores for tasks or task examples by taking the difference in pretrain loss on BERT (double) and Legal-BERT. DS score may be readily extended to estimate domain specificity of tasks in other domains using BERT (double) and existing pretrained models (e.g., [SciBERT](https://arxiv.org/abs/1903.10676)). ### Citation @inproceedings{zhengguha2021, title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset}, author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho}, year={2021}, eprint={2104.08671}, archivePrefix={arXiv}, primaryClass={cs.CL}, booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law}, publisher={Association for Computing Machinery}, note={(in press)} } Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021, São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 [cs.CL]](https://arxiv.org/abs/2104.08671).

zlucia/custom-legalbert

2021-06-05T22:30:43.000Z

fill-mask

zlucia

transformers

--- language: en pipeline_tag: fill-mask tags: - legal --- ### Custom Legal-BERT Model and tokenizer files for Custom Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://arxiv.org/abs/2104.08671). ### Training Data The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (https://case.law/). The size of this corpus (37GB) is substantial, representing 3,446,187 legal decisions across all federal and state courts, and is larger than the size of the BookCorpus/Wikipedia corpus originally used to train BERT (15GB). ### Training Objective This model is pretrained from scratch for 2M steps on the MLM and NSP objective, with tokenization and sentence segmentation adapted for legal text (cf. the paper). The model also uses a custom domain-specific legal vocabulary. The vocabulary set is constructed using [SentencePiece](https://arxiv.org/abs/1808.06226) on a subsample (approx. 13M) of sentences from our pretraining corpus, with the number of tokens fixed to 32,000. ### Usage Please see the [casehold repository](https://github.com/reglab/casehold) for scripts that support computing pretrain loss and finetuning on Custom Legal-BERT for classification and multiple choice tasks described in the paper: Overruling, Terms of Service, CaseHOLD. ### Citation ``` @inproceedings{zhengguha2021, title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset}, author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho}, year={2021}, eprint={2104.08671}, archivePrefix={arXiv}, primaryClass={cs.CL}, booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law}, publisher={Association for Computing Machinery}, note={(in press) } ``` Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021, São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 \\[cs.CL\\]](https://arxiv.org/abs/2104.08671).

zlucia/legalbert

2021-06-05T22:15:13.000Z

fill-mask

zlucia

transformers

--- language: en pipeline_tag: fill-mask tags: - legal --- ### Legal-BERT Model and tokenizer files for Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings](https://arxiv.org/abs/2104.08671). ### Training Data The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (https://case.law/). The size of this corpus (37GB) is substantial, representing 3,446,187 legal decisions across all federal and state courts, and is larger than the size of the BookCorpus/Wikipedia corpus originally used to train BERT (15GB). ### Training Objective This model is initialized with the base BERT model (uncased, 110M parameters), [bert-base-uncased](https://huggingface.co/bert-base-uncased), and trained for an additional 1M steps on the MLM and NSP objective, with tokenization and sentence segmentation adapted for legal text (cf. the paper). ### Usage Please see the [casehold repository](https://github.com/reglab/casehold) for scripts that support computing pretrain loss and finetuning on Legal-BERT for classification and multiple choice tasks described in the paper: Overruling, Terms of Service, CaseHOLD. ### Citation ``` @inproceedings{zhengguha2021, title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset}, author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho}, year={2021}, eprint={2104.08671}, archivePrefix={arXiv}, primaryClass={cs.CL}, booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law}, publisher={Association for Computing Machinery}, note={(in press)} } ``` Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021, São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 \\[cs.CL\\]](https://arxiv.org/abs/2104.08671).

znigeln/test

2021-05-10T14:59:11.000Z

znigeln

zoeozone/gadgetgreen

2021-03-22T22:31:02.000Z

zoeozone

zoeozone/zoeclone

2021-03-30T00:20:32.000Z

zoeozone

zoeozone/zoeozone

2021-03-03T02:00:36.000Z

zoeozone

zoeymeng913/bert_cn_finetuning

2021-05-20T09:54:41.000Z

fill-mask

zoeymeng913

transformers

zoeymeng913/bert_finetuning_test

2021-05-20T09:55:05.000Z

fill-mask

zoeymeng913

transformers

zohaib-khan/bert-medium-legal

2020-12-09T12:52:23.000Z

zohaib-khan

zqf03118/ItcastAI

2021-02-02T14:39:19.000Z

zqf03118

zqf03118/bert_cn_finetuning

2021-05-20T09:55:48.000Z

fill-mask

zqf03118

transformers

zqf03118/bert_finetuning_test

2021-05-20T09:56:44.000Z

fill-mask

zqf03118

transformers

zr19z1211/mask-fill

2021-04-30T07:46:51.000Z

zr19z1211

zundel/model_name

2021-04-06T14:31:51.000Z

zundel

zxsu/test_model

2021-05-10T12:40:19.000Z

zxsu

zyberg2091/distilbert-base-multilingual-toxicity-classifier

2021-01-06T20:43:33.000Z

text-classification

zyberg2091

transformers

versae/mBERT-5lang-adobo2021

2021-06-19T00:23:38.000Z

versae

worsterman/DialoGPT-medium-mulder

2021-06-19T00:46:42.000Z

worsterman

wassemgtk/snippetv2

2021-06-19T01:31:42.000Z

wassemgtk

danurahul/yoav_gpt_neo1.3B_delimiter

2021-06-19T02:27:20.000Z

text-generation

danurahul

transformers

saichandrapandraju/t5_small_tabqgen

2021-06-19T02:33:50.000Z

text2text-generation

saichandrapandraju

transformers

saichandrapandraju/t5_large_tabqgen

2021-06-19T02:50:02.000Z

text2text-generation

saichandrapandraju

transformers

sambelanz/copaamerica2021

2021-06-19T03:24:15.000Z

sambelanz

p208p2002/qmst-qgg-qa

2021-06-19T05:04:13.000Z

text2text-generation

p208p2002

transformers

p208p2002/qmst-qgg

2021-06-19T05:17:43.000Z

text2text-generation

p208p2002

transformers

royam0820/dummy-model

2021-06-19T09:05:32.000Z

royam0820

motiondew/set_date_1-bert

2021-06-19T10:57:12.000Z

fill-mask

motiondew

transformers

noelmathewisaac/inspirational-quotes-distilgpt2

2021-06-19T11:01:28.000Z

text-generation

noelmathewisaac

transformers

harish/AllTokenFineTunedNLI-E1

2021-06-19T11:24:56.000Z

harish

transformers

remotejob/tweetsTINYGPT2fi_v1

2021-06-19T15:35:18.000Z

text-generation

remotejob

transformers