modelId
stringlengths
4
112
sha
stringlengths
40
40
lastModified
stringlengths
24
24
tags
sequence
pipeline_tag
stringclasses
29 values
private
bool
1 class
author
stringlengths
2
38
config
null
id
stringlengths
4
112
downloads
float64
0
36.8M
likes
float64
0
712
library_name
stringclasses
17 values
__index_level_0__
int64
0
38.5k
readme
stringlengths
0
186k
persiannlp/mt5-large-parsinlu-arc-comqa-obqa-multiple-choice
11bb178491c00702ce688de2bb472215512a2f11
2021-09-23T16:20:12.000Z
[ "pytorch", "t5", "text2text-generation", "fa", "multilingual", "dataset:parsinlu", "dataset:commonsenseqa", "dataset:arc", "dataset:openbookqa", "transformers", "multiple-choice", "mt5", "persian", "farsi", "license:cc-by-nc-sa-4.0", "autotrain_compatible" ]
text2text-generation
false
persiannlp
null
persiannlp/mt5-large-parsinlu-arc-comqa-obqa-multiple-choice
2
null
transformers
24,600
--- language: - fa - multilingual thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg tags: - multiple-choice - mt5 - persian - farsi license: cc-by-nc-sa-4.0 datasets: - parsinlu - commonsenseqa - arc - openbookqa metrics: - accuracy --- # Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی) This is a mT5-based model for multiple-choice question answering. Here is an example of how you can run this model: ```python from transformers import MT5ForConditionalGeneration, MT5Tokenizer model_size = "large" model_name = f"persiannlp/mt5-{model_size}-parsinlu-arc-comqa-obqa-multiple-choice" tokenizer = MT5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, **generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, **generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("وسیع ترین کشور جهان کدام است؟ <sep> آمریکا <sep> کانادا <sep> روسیه <sep> چین") run_model("طامع یعنی ؟ <sep> آزمند <sep> خوش شانس <sep> محتاج <sep> مطمئن") run_model( "زمینی به ۳۱ قطعه متساوی مفروض شده است و هر روز مساحت آماده شده برای احداث، دو برابر مساحت روز قبل است.اگر پس از (۵ روز) تمام زمین آماده شده باشد، در چه روزی یک قطعه زمین آماده شده <sep> روز اول <sep> روز دوم <sep> روز سوم <sep> هیچکدام") ``` For more details, visit this page: https://github.com/persiannlp/parsinlu/
pertschuk/0_RoBERTa
066a634e4475f43b1895b261ae7205583f40c274
2020-04-15T23:33:48.000Z
[ "pytorch", "transformers" ]
null
false
pertschuk
null
pertschuk/0_RoBERTa
2
null
transformers
24,601
Entry not found
peter2000/xlm-roberta-base-finetuned-ecoicop
4702cede7d86f3a88b4711b8d682437e0f118ddd
2021-10-27T09:02:06.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
peter2000
null
peter2000/xlm-roberta-base-finetuned-ecoicop
2
null
transformers
24,602
--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-ecoicop results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-ecoicop This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1685 - Acc: 0.9659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.4224 | 1.0 | 2577 | 0.3612 | 0.9132 | | 0.2313 | 2.0 | 5154 | 0.2510 | 0.9441 | | 0.1746 | 3.0 | 7731 | 0.1928 | 0.9569 | | 0.1325 | 4.0 | 10308 | 0.1731 | 0.9640 | | 0.0946 | 5.0 | 12885 | 0.1685 | 0.9659 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
phailyoor/distilbert-base-uncased-finetuned-yahd-2
b69e108b3a795d80c7aa90946113338a6fe74b49
2021-11-10T20:24:46.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
phailyoor
null
phailyoor/distilbert-base-uncased-finetuned-yahd-2
2
null
transformers
24,603
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-yahd-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-yahd-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3850 - Accuracy: 0.2652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.2738 | 1.0 | 9556 | 2.2228 | 0.1996 | | 1.9769 | 2.0 | 19112 | 2.1378 | 0.2321 | | 1.6624 | 3.0 | 28668 | 2.1897 | 0.2489 | | 1.3682 | 4.0 | 38224 | 2.2863 | 0.2538 | | 1.1975 | 5.0 | 47780 | 2.3850 | 0.2652 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
phailyoor/distilbert-base-uncased-finetuned-yahd-twval
452bf1458b355477cdc1608ef03715f76ced904c
2021-11-14T19:41:47.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
phailyoor
null
phailyoor/distilbert-base-uncased-finetuned-yahd-twval
2
null
transformers
24,604
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-yahd-twval results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-yahd-twval This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2540 - Accuracy: 0.2664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.1967 | 1.0 | 10086 | 2.9662 | 0.2068 | | 1.865 | 2.0 | 20172 | 2.9499 | 0.3229 | | 1.5135 | 3.0 | 30258 | 3.3259 | 0.3036 | | 1.2077 | 4.0 | 40344 | 3.8351 | 0.2902 | | 1.0278 | 5.0 | 50430 | 4.2540 | 0.2664 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
phailyoor/distilbert-base-uncased-finetuned-yahd
e340ea58530b5849578f4a1606be14badf87fa55
2021-11-10T18:19:43.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
phailyoor
null
phailyoor/distilbert-base-uncased-finetuned-yahd
2
null
transformers
24,605
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-yahd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-yahd This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.7685 - Accuracy: 0.4010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 2.2439 | 1.0 | 9142 | 2.1898 | 0.2130 | | 1.9235 | 2.0 | 18284 | 2.1045 | 0.2372 | | 1.5915 | 3.0 | 27426 | 2.1380 | 0.2550 | | 1.3262 | 4.0 | 36568 | 2.2544 | 0.2758 | | 1.0529 | 5.0 | 45710 | 2.5662 | 0.2955 | | 0.8495 | 6.0 | 54852 | 2.8731 | 0.3078 | | 0.6779 | 7.0 | 63994 | 3.1980 | 0.3218 | | 0.5546 | 8.0 | 73136 | 3.6289 | 0.3380 | | 0.4738 | 9.0 | 82278 | 3.9732 | 0.3448 | | 0.412 | 10.0 | 91420 | 4.2945 | 0.3565 | | 0.3961 | 11.0 | 100562 | 4.6127 | 0.3772 | | 0.3292 | 12.0 | 109704 | 4.9586 | 0.3805 | | 0.318 | 13.0 | 118846 | 5.2615 | 0.3887 | | 0.2936 | 14.0 | 127988 | 5.4567 | 0.3931 | | 0.2671 | 15.0 | 137130 | 5.6902 | 0.3965 | | 0.2301 | 16.0 | 146272 | 5.7685 | 0.4010 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
philippelaban/headline_grouping
e8120f3cbc07c11bc80fc1c9fa8514187cb13d59
2021-08-04T20:38:16.000Z
[ "pytorch", "electra", "text-classification", "transformers" ]
text-classification
false
philippelaban
null
philippelaban/headline_grouping
2
1
transformers
24,606
Entry not found
pi3ni0/pubmedqa-scibert-classical
f4a045a58b190b2c9b747199acf4291bf2c89d18
2021-05-20T02:37:34.000Z
[ "pytorch", "jax", "bert", "pretraining", "transformers" ]
null
false
pi3ni0
null
pi3ni0/pubmedqa-scibert-classical
2
null
transformers
24,607
Entry not found
pistachiocow/RoyTBenBot
a5b2815fd30ee6bc7bd399d41e62b3787e81934a
2021-09-12T15:29:41.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
pistachiocow
null
pistachiocow/RoyTBenBot
2
null
transformers
24,608
Entry not found
pmthangk09/bert-base-uncased-sst
d5e88f091a66a363255ca976b845e508ad64757e
2021-05-20T02:49:40.000Z
[ "pytorch", "tf", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
pmthangk09
null
pmthangk09/bert-base-uncased-sst
2
null
transformers
24,609
Entry not found
prajjwal1/ctrl_discovery_10
0d521c97a705e6e3ea12d8557da3a8ba8ff5a367
2021-05-16T16:56:14.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_10
2
null
transformers
24,610
Entry not found
prajjwal1/ctrl_discovery_13
2b5a4716fef44d2cb7821fb865ca99ed2eb68ce1
2021-06-03T22:20:53.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_13
2
null
transformers
24,611
Entry not found
prajjwal1/ctrl_discovery_3
cf6344aa71e060e67a9e4f038f30ef350fd652a1
2021-03-06T16:07:23.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_3
2
null
transformers
24,612
Entry not found
prajjwal1/ctrl_discovery_6
9facec54d3279b6194589b140a74329ba5a57679
2021-04-11T04:41:23.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_6
2
null
transformers
24,613
Entry not found
prajjwal1/ctrl_discovery_7
f7672687e5b115d547237b507c0325882125a330
2021-04-25T18:47:46.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_7
2
null
transformers
24,614
Entry not found
prajjwal1/ctrl_discovery_8
3ef77b196954f8ff5e1d264b300b5019f108f54d
2021-04-25T21:01:29.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_8
2
null
transformers
24,615
Entry not found
prajjwal1/ctrl_discovery_9
c6b1b582c34d2fa145cb488e9cb4cb1473af4304
2021-05-16T16:34:38.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_9
2
null
transformers
24,616
Entry not found
prajjwal1/ctrl_discovery_flipped_1
2cc13302dc0fa2f7efcec940a6764ecd9c0b88f4
2021-03-03T16:03:04.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_flipped_1
2
null
transformers
24,617
Entry not found
prajjwal1/ctrl_discovery_flipped_3
269e50edf14a7cff030bc1ed17ca22f57e8b6b9b
2021-03-30T18:44:22.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_flipped_3
2
null
transformers
24,618
Entry not found
prajjwal1/ctrl_discovery_flipped_4
5f3589229020ae3cc81c1e7297437b10e8f2f676
2021-03-30T19:14:49.000Z
[ "pytorch", "ctrl", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/ctrl_discovery_flipped_4
2
null
transformers
24,619
Entry not found
prajjwal1/gpt2_xl_discovery
738a15a54fa628f2be2ec7178bea10f77ed306de
2021-08-10T01:03:26.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
prajjwal1
null
prajjwal1/gpt2_xl_discovery
2
null
transformers
24,620
Entry not found
prajwalcr/poetry-anticipation_gpt2
3b83a5e63d13630a0a307527a0c6e6f1691440e2
2021-05-29T17:57:38.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
prajwalcr
null
prajwalcr/poetry-anticipation_gpt2
2
null
transformers
24,621
Entry not found
prajwalcr/poetry-joy_gpt2
7730bd18912cf09ab64c2f276f46775137094637
2021-08-03T06:54:39.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
prajwalcr
null
prajwalcr/poetry-joy_gpt2
2
null
transformers
24,622
Entry not found
princeton-nlp/datamux-mnli-10
cb67af5abc076f15164377d734c074b133ad0830
2022-02-16T16:54:02.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/datamux-mnli-10
2
null
transformers
24,623
Entry not found
princeton-nlp/datamux-mnli-40
2dcaf05123f4c271c20230f16106734d19d4e1a9
2022-02-16T16:56:10.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/datamux-mnli-40
2
null
transformers
24,624
Entry not found
princeton-nlp/datamux-retrieval-10
c6b1a0d908e4933525d2823724cf1131ac815238
2022-02-18T03:53:09.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/datamux-retrieval-10
2
null
transformers
24,625
Entry not found
princeton-nlp/datamux-retrieval-40
97553f2a1ad99f179668030cc153cd21f3f9cf83
2022-02-18T03:56:23.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/datamux-retrieval-40
2
null
transformers
24,626
Entry not found
princeton-nlp/densephrases-multi-query-sqd
a4d826ab92fdab03af5c5401a91e7aec47e7b0f8
2021-09-20T21:49:34.000Z
[ "pytorch", "bert", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/densephrases-multi-query-sqd
2
null
transformers
24,627
Entry not found
princeton-nlp/densephrases-multi-query-wq
cbf2a28206b510fe00b799506561b8a92f4abe9f
2021-09-20T21:39:19.000Z
[ "pytorch", "bert", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/densephrases-multi-query-wq
2
null
transformers
24,628
Entry not found
proxyht/mdsister
7ff0d93d9b75d6a7d1e3033686baf1aec5a17a3c
2021-06-29T08:01:18.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
proxyht
null
proxyht/mdsister
2
1
transformers
24,629
Entry not found
proycon/robbert2-ner-cased-sonar1-nld
af155036374c5afc5216173fb8d1502211bce320
2021-05-20T19:43:40.000Z
[ "pytorch", "jax", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
proycon
null
proycon/robbert2-ner-cased-sonar1-nld
2
null
transformers
24,630
Entry not found
pszemraj/wavlm-large-timit-100epoch
7e342a32a6e0ffe5310b0ae84e78196e4bfd74ce
2021-12-29T18:11:32.000Z
[ "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
pszemraj
null
pszemraj/wavlm-large-timit-100epoch
2
null
transformers
24,631
--- tags: - generated_from_trainer model-index: - name: timit-demo-wavlm-large --- # timit-demo-wavlm-large This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the [Timit dataset](https://huggingface.co/datasets/timit_asr). It achieves the following results on the evaluation set: - Loss: 0.3784 - Wer: 0.2746 ## Model description Fine tunes `microsoft/wavlm-large` on the [Timit dataset](https://huggingface.co/datasets/timit_asr) for 100 epochs to see results / compare to wav2vec2. ## Intended uses & limitations This should be used primarily for benchmarking / comparison purposes, the Timit dataset **does not** generalize well as you will quickly see from testing inference API. ## Training and evaluation data [Timit](https://huggingface.co/datasets/timit_asr) using standard splits. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.2656 | 4.0 | 500 | 2.9768 | 1.0 | | 1.8004 | 8.0 | 1000 | 0.6151 | 0.6046 | | 0.5425 | 12.0 | 1500 | 0.3802 | 0.4330 | | 0.2647 | 16.0 | 2000 | 0.3015 | 0.3587 | | 0.1697 | 20.0 | 2500 | 0.3225 | 0.3439 | | 0.1164 | 24.0 | 3000 | 0.3162 | 0.3277 | | 0.0951 | 28.0 | 3500 | 0.3102 | 0.3098 | | 0.076 | 32.0 | 4000 | 0.3201 | 0.3052 | | 0.0647 | 36.0 | 4500 | 0.3346 | 0.2990 | | 0.0544 | 40.0 | 5000 | 0.3323 | 0.2955 | | 0.0515 | 44.0 | 5500 | 0.3377 | 0.2898 | | 0.045 | 48.0 | 6000 | 0.3268 | 0.2881 | | 0.0393 | 52.0 | 6500 | 0.3404 | 0.2822 | | 0.0364 | 56.0 | 7000 | 0.3337 | 0.2805 | | 0.0329 | 60.0 | 7500 | 0.3485 | 0.2823 | | 0.0327 | 64.0 | 8000 | 0.3362 | 0.2795 | | 0.0287 | 68.0 | 8500 | 0.3768 | 0.2845 | | 0.0284 | 72.0 | 9000 | 0.3736 | 0.2805 | | 0.0292 | 76.0 | 9500 | 0.3761 | 0.2806 | | 0.0251 | 80.0 | 10000 | 0.3735 | 0.2768 | | 0.0224 | 84.0 | 10500 | 0.3741 | 0.2773 | | 0.0232 | 88.0 | 11000 | 0.3760 | 0.2772 | | 0.0213 | 92.0 | 11500 | 0.3729 | 0.2740 | | 0.0204 | 96.0 | 12000 | 0.3722 | 0.2739 | | 0.0199 | 100.0 | 12500 | 0.3784 | 0.2746 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
rafaelm47labs/spanishnews-classification
688cd461c5623c3851401d2ffa34c1743a833c50
2021-09-02T10:06:33.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
rafaelm47labs
null
rafaelm47labs/spanishnews-classification
2
null
transformers
24,632
ragarwal/args-me-crossencoder-v1
82616e6c86e034c95e168d085589d54c5d63d4e5
2021-05-20T19:47:10.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
false
ragarwal
null
ragarwal/args-me-crossencoder-v1
2
null
transformers
24,633
Entry not found
ragarwal/args-me-roberta-base
11603ec3fc73c17a67e26baacb848fb46499429e
2021-05-20T19:48:38.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ragarwal
null
ragarwal/args-me-roberta-base
2
null
transformers
24,634
modelhub test
rajeshradhakrishnan/malayalam-wiki2021-BERTo
0858f3def56e859fe7aec3e386cdd44c1546185e
2021-11-08T18:02:58.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
rajeshradhakrishnan
null
rajeshradhakrishnan/malayalam-wiki2021-BERTo
2
null
transformers
24,635
Entry not found
rajivratn/gupshup_e2e_pegasus
25239020d2f33fc9d7d40291a51278d50c30eda1
2021-11-06T17:56:51.000Z
[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
rajivratn
null
rajivratn/gupshup_e2e_pegasus
2
null
transformers
24,636
Entry not found
ran/c9
c4c949c5f5e24894353458d48d7053f7c079a6a3
2021-05-20T03:55:20.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
ran
null
ran/c9
2
null
transformers
24,637
Entry not found
ravirajoshi/wav2vec2-large-xls-r-300m-hindi
19d1ee64b6fbb25cfa53decc026d5ff95460ac91
2022-03-24T11:56:00.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hi", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ravirajoshi
null
ravirajoshi/wav2vec2-large-xls-r-300m-hindi
2
null
transformers
24,638
--- language: - hi license: apache-2.0 tags: - generated_from_trainer - robust-speech-event - hf-asr-leaderboard model-index: - name: wav2vec2-large-xls-r-300m-hindi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hindi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7049 - Wer: 0.3200
remotejob/tweetsDISTILGPT2fi_v3
e1e490eca4c8600692002b577a32d63c80e59bc7
2021-11-05T07:05:34.000Z
[ "pytorch", "rust", "gpt2", "text-generation", "transformers" ]
text-generation
false
remotejob
null
remotejob/tweetsDISTILGPT2fi_v3
2
null
transformers
24,639
Entry not found
remotejob/tweetsGPT2fi_v1
21562d194537eb3bb3a5cb8028cd340a2d0bb351
2021-06-12T16:40:33.000Z
[ "pytorch", "rust", "gpt2", "text-generation", "transformers" ]
text-generation
false
remotejob
null
remotejob/tweetsGPT2fi_v1
2
null
transformers
24,640
Entry not found
researchaccount/continue_mlm
50afb2416b745b9bcbcf3a58ba5f3ec745ea20c3
2021-05-20T04:18:46.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
researchaccount
null
researchaccount/continue_mlm
2
null
transformers
24,641
Entry not found
researchaccount/sa_sub3
6b170e6b699b92c44b8d7840ae40e68b62af754c
2021-05-20T04:23:04.000Z
[ "pytorch", "jax", "bert", "text-classification", "en", "transformers" ]
text-classification
false
researchaccount
null
researchaccount/sa_sub3
2
null
transformers
24,642
--- language: en widget: - text: "USER USER USER USER لاحول ولاقوه الا بالله 💔 💔 💔 💔 HASH TAG متي يصدر قرار العشرين ! ! ! ! ! !" --- Sub 3
reza/xlm-roberta-base-finetuned-marc-en
34dc5c15f5314e93f7fb7dc02beeee58947615f4
2021-10-22T13:15:30.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "dataset:amazon_reviews_multi", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
reza
null
reza/xlm-roberta-base-finetuned-marc-en
2
null
transformers
24,643
--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9569 - Mae: 0.5244 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1386 | 1.0 | 235 | 1.0403 | 0.5122 | | 0.9591 | 2.0 | 470 | 0.9569 | 0.5244 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
rfulton/my_model
0579c641707d1043c7492a445a9cbc616d5d803e
2021-08-23T20:59:25.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
rfulton
null
rfulton/my_model
2
null
transformers
24,644
Entry not found
rg089/distilbart-summarization
9654473ec640cd8df14e04f976f23cfc23265a38
2021-11-27T19:10:50.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
rg089
null
rg089/distilbart-summarization
2
null
transformers
24,645
Entry not found
ricardo-filho/BERT-pt-institutional-corpus-v.1
d6443b74923d87abde908ddfb31ccc58636e6202
2021-07-27T22:29:33.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ricardo-filho
null
ricardo-filho/BERT-pt-institutional-corpus-v.1
2
null
transformers
24,646
Entry not found
ricardo-filho/bertimbau_base_snli_mnrl
e5df684f474ebdc3c63e5b0e8aa43a1e8d8ce207
2021-08-09T21:01:02.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ricardo-filho
null
ricardo-filho/bertimbau_base_snli_mnrl
2
null
sentence-transformers
24,647
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4059 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 405, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 406, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ricardo-filho/sbertimbau-large-allnli-mnrl
f2c8dfe06374382e56a40dad7a53985b758a611e
2021-08-12T19:44:32.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ricardo-filho
null
ricardo-filho/sbertimbau-large-allnli-mnrl
2
1
sentence-transformers
24,648
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 16133 with parameters: ``` {'batch_size': 32} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 1613, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1614, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ristekcsui/bert-base-hs
ccd841a0eb02b67e5a02b74fc791f5a0c71ef5f6
2022-01-31T04:03:56.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
ristekcsui
null
ristekcsui/bert-base-hs
2
null
transformers
24,649
Entry not found
rkmt/repo
6b334850385495bb54acea66ad112623b8dc2d9d
2022-02-13T12:32:08.000Z
[ "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
rkmt
null
rkmt/repo
2
null
transformers
24,650
Entry not found
rndlr96/EnBERT_BCE
4213315c27badfbb2962fa0554aed5a385379067
2021-05-20T04:29:02.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/EnBERT_BCE
2
null
transformers
24,651
Entry not found
rndlr96/label256
7359c6225665a5d8c39decda39410fe729e40ddb
2021-05-20T04:31:56.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/label256
2
null
transformers
24,652
Entry not found
rossanez/t5-base-finetuned-de-en
95a00bcf34ba4499acc700c66ef027f73623cf15
2021-12-01T10:55:50.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wmt14", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
rossanez
null
rossanez/t5-base-finetuned-de-en
2
null
transformers
24,653
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 model-index: - name: t5-base-finetuned-de-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-de-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.4324 | 1.2308 | 17.8904 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rossanez/t5-small-finetuned-de-en-256-lr2e-4
1b2376b915881c6ecc8a51c0dcffe065bb7281e9
2021-12-01T00:40:20.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wmt14", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
rossanez
null
rossanez/t5-small-finetuned-de-en-256-lr2e-4
2
null
transformers
24,654
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 model-index: - name: t5-small-finetuned-de-en-256-lr2e-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-256-lr2e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1169 | 7.6948 | 17.4103 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rossanez/t5-small-finetuned-de-en-256-nofp16
462cead30aae95920be494e74265142f1af02ea9
2021-12-01T00:54:59.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wmt14", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
rossanez
null
rossanez/t5-small-finetuned-de-en-256-nofp16
2
null
transformers
24,655
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 model-index: - name: t5-small-finetuned-de-en-256-nofp16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-256-nofp16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1234 | 7.7305 | 17.4033 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rsedlr/RickBotExample
751dc3aa2835d00a92c8164c180886b210d47c3e
2021-08-09T15:51:39.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rsedlr
null
rsedlr/RickBotExample
2
null
transformers
24,656
--- tags: - conversational --- # RickBot built for [Chai](https://chai.ml/) Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
rywerth/Rupi-or-Not-Rupi
58c0c361408a5c4459cdaba1cca7d4aeef68a969
2021-05-23T12:18:29.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
rywerth
null
rywerth/Rupi-or-Not-Rupi
2
null
transformers
24,657
hello
s3h/arabert-classification
e259873a646468042e626ada13071d74c182be11
2022-01-01T12:18:15.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
s3h
null
s3h/arabert-classification
2
null
transformers
24,658
Entry not found
saattrupdan/xlmr-base-texas-squad-es
2b5b2d132fd0c58bf0ecd7095a47d521766aa3cf
2022-03-18T16:51:52.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "question-answering", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
saattrupdan
null
saattrupdan/xlmr-base-texas-squad-es
2
null
transformers
24,659
--- license: mit tags: - generated_from_trainer model-index: - name: xlmr-base-texas-squad-es results: [] widget: - text: "¿Quién invitó a Raísa Gorbachova a tomar una copa?" context: "Las tapas han llegado a convertirse en una señal de identidad española y son ofrecidas en los banquetes de recepción a los más altos dignatarios (en los denominados tapas meeting). Así, durante la Conferencia de Paz de Madrid la Reina Sofía y el alcalde de Madrid José María Álvarez del Manzano invitaron a Raísa Gorbachova a una bebida con tapa durante su visita a la capital española. En la modernidad existen bares que ofrecen especialidades de tapas y a este fenómeno se le ha denominado cocina en miniatura. No obstante, el concepto de tapa ha sido llevado a la alta cocina por el cocinero Ferran Adrià que los emplea como entradas." --- # TExAS-SQuAD-es This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-es dataset. It achieves the following results on the evaluation set: - Exact match: xx.xx% - F1-score: xx.xx% ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0645 | 0.24 | 1000 | 1.7915 | | 1.8458 | 0.47 | 2000 | 1.7873 | | 1.8208 | 0.71 | 3000 | 1.6628 | | 1.7743 | 0.95 | 4000 | 1.5684 | | 1.5636 | 1.18 | 5000 | 1.5686 | | 1.6017 | 1.42 | 6000 | 1.5484 | | 1.6271 | 1.66 | 7000 | 1.5173 | | 1.5975 | 1.89 | 8000 | 1.5209 | | 1.477 | 2.13 | 9000 | 1.5766 | | 1.4389 | 2.37 | 10000 | 1.5392 | | 1.3389 | 2.6 | 11000 | 1.5298 | | 1.437 | 2.84 | 12000 | 1.5504 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
saburbutt/albert_xxlarge_tweetqa_v2
3aa1c0a44a17faac7d337b8c1420e6d145b5ca65
2021-04-13T22:36:46.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saburbutt
null
saburbutt/albert_xxlarge_tweetqa_v2
2
null
transformers
24,660
sagar/pretrained-FinBERT
71d73dec4251bf0e3912c5c1b0d431f3edccbda2
2021-01-04T04:34:18.000Z
[ "pytorch", "transformers" ]
null
false
sagar
null
sagar/pretrained-FinBERT
2
null
transformers
24,661
FinBert Pretrained model to be used with downstream tasks
sagittariusA/media_bias_classifier_cs
4dff5cdc1ffa0898aed43f8756ea5ceacb007a0b
2022-01-07T21:15:38.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
sagittariusA
null
sagittariusA/media_bias_classifier_cs
2
null
transformers
24,662
Entry not found
sagteam/covid-twitter-xlm-roberta-large
28782639dde040b06460d8b5bc09b9525f3e7c34
2022-07-27T11:41:43.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "arxiv:1911.02116", "transformers", "autotrain_compatible" ]
fill-mask
false
sagteam
null
sagteam/covid-twitter-xlm-roberta-large
2
null
transformers
24,663
# COVID-twitter-XLM-Roberta-large ## Model description This is a model based on the [XLM-RoBERTa large](https://huggingface.co/xlm-roberta-large) topology (provided by Facebook, see original [paper](https://arxiv.org/abs/1911.02116)) with additional training on a corpus of unmarked tweets. For more details, please see, our [GitHub repository](https://github.com/sag111/COVID-19-tweets-Russia). ## Training data We formed a corpus of unlabeled twitter messages. The data on keyword "covid" was expanded with texts containing other words often occurred in hashtags on the Covid-19 pandemic: "covid", "stayhome", and "coronavirus" (hereinafter, these are translations of Russian words into English). Separately, messages were collected from Twitter users from large regions of Russia. The search was provided using different word forms of 58 manually selected keywords on Russian related to the topic of coronavirus infection (including: "PCR", "pandemic", "self-isolation", etc.). The unlabeled corpus includes all unique Russian-language tweets from the collected data (>1M tweets). Since modern language models are usually multilingual, about 1M more tweets in other languages were added to this corpus using filtering procedures described above. Thus, in the unlabeled part of the collected data, there were about 2 million messages. ### BibTeX entry and citation info Our GitHub repository: https://github.com/sag111/COVID-19-tweets-Russia If you have found our results helpful in your work, feel free to cite our publication and this repository as: ``` @article{sboev2021russian, title={The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19}, author={Sboev, Alexander and Moloshnikov, Ivan and Naumov, Alexander and Levochkina𝑎, Anastasia and Rybka𝑎, Roman}, year={2021} } ```
sagteam/xlm-roberta-large-sag
ed249e398098a3717c94e8e24d4130bb6f54a5d1
2021-11-24T18:19:22.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "multilingual", "arxiv:1911.02116", "arxiv:2004.03659", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
sagteam
null
sagteam/xlm-roberta-large-sag
2
1
transformers
24,664
--- language: multilingual thumbnail: "url to a thumbnail used in social sharing" tags: exbert license: apache-2.0 --- # XLM-RoBERTa-large-sag ## Model description This is a model based on the [XLM-RoBERTa large](https://huggingface.co/xlm-roberta-large) topology (provided by Facebook, see original [paper](https://arxiv.org/abs/1911.02116)) with additional training on two sets of medicine-domain texts: * about 250.000 text reviews on medicines (1000-tokens-long in average) collected from the site irecommend.ru; * the raw part of the [RuDReC corpus](https://github.com/cimm-kzn/RuDReC) (about 1.4 million texts, see [paper](https://arxiv.org/abs/2004.03659)). The XLM-RoBERTa-large calculations for one epoch on this data were performed using one Nvidia Tesla v100 and the Huggingface Transformers library. ## BibTeX entry and citation info If you have found our results helpful in your work, feel free to cite our publication as: ``` @article{sboev2021analysis, title={An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets}, author={Sboev, Alexander and Sboeva, Sanna and Moloshnikov, Ivan and Gryaznov, Artem and Rybka, Roman and Naumov, Alexander and Selivanov, Anton and Rylkov, Gleb and Ilyin, Viacheslav}, journal={arXiv preprint arXiv:2105.00059}, year={2021} } ```
saibo/random-bert-base-uncased
ac861c74b23a9821aea308267fa3ca4283782811
2021-07-29T14:36:42.000Z
[ "pytorch", "tf", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
saibo
null
saibo/random-bert-base-uncased
2
null
transformers
24,665
Entry not found
saichandrapandraju/t5_base_tabqgen
cecc1ea44263bc1ad8aa533f75bea9c23ed86d54
2021-06-23T14:04:10.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
saichandrapandraju
null
saichandrapandraju/t5_base_tabqgen
2
null
transformers
24,666
Entry not found
sam890914/autonlp-roberta-large2-479012819
520da5554581303c82e6511374f476ac2ff62fe9
2022-01-06T08:46:51.000Z
[ "pytorch", "roberta", "text-classification", "unk", "dataset:sam890914/autonlp-data-roberta-large2", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
sam890914
null
sam890914/autonlp-roberta-large2-479012819
2
null
transformers
24,667
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - sam890914/autonlp-data-roberta-large2 co2_eq_emissions: 71.60954851696604 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 479012819 - CO2 Emissions (in grams): 71.60954851696604 ## Validation Metrics - Loss: 0.22774338722229004 - Accuracy: 0.9395126938149599 - Precision: 0.9677075940383251 - Recall: 0.9117352056168505 - AUC: 0.9862377263827619 - F1: 0.9388879325185058 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/sam890914/autonlp-roberta-large2-479012819 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sam890914/autonlp-roberta-large2-479012819", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sam890914/autonlp-roberta-large2-479012819", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
samkphd31/ASRS-CMFS
83e2c4fb773adbae379bae26ab709a3d7ca5232c
2021-11-17T14:36:13.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
samkphd31
null
samkphd31/ASRS-CMFS
2
null
transformers
24,668
Entry not found
sammy786/wav2vec2-xlsr-interlingua
e8b38c0848fd7afdc3aaa01775dcfec6d8315180
2022-03-24T11:56:13.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "ia", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-xlsr-interlingua
2
null
transformers
24,669
--- language: - ia license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - ia - robust-speech-event - model_for_talk - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: sammy786/wav2vec2-xlsr-interlingua results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: ia metrics: - name: Test WER type: wer value: 16.81 - name: Test CER type: cer value: 4.76 --- # sammy786/wav2vec2-xlsr-interlingua This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ia dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: 5.44 - Wer: 19.78 ## Model description "facebook/wav2vec2-xls-r-1b" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 4.649200 | 0.483339 | 0.511322 | | 400 | 0.764700 | 0.133428 | 0.251288 | | 600 | 0.563700 | 0.099292 | 0.227745 | | 800 | 0.438800 | 0.087545 | 0.217445 | | 1000 | 0.406800 | 0.072313 | 0.213848 | | 1200 | 0.237500 | 0.066965 | 0.213766 | | 1400 | 0.177800 | 0.064419 | 0.208126 | | 1600 | 0.157100 | 0.065962 | 0.214011 | | 1800 | 0.146600 | 0.059477 | 0.202076 | | 2000 | 0.132800 | 0.055015 | 0.201831 | | 2200 | 0.122000 | 0.055421 | 0.201749 | | 2400 | 0.115700 | 0.054462 | 0.197826 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-interlingua --dataset mozilla-foundation/common_voice_8_0 --config ia --split test ```
sammy786/wav2vec2-xlsr-romansh_sursilvan
7f80a82062614bc9347256a9209471334c6c294a
2022-03-24T11:58:43.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "rm-sursilv", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-xlsr-romansh_sursilvan
2
null
transformers
24,670
--- language: - rm-sursilv license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - rm-sursilv - robust-speech-event - model_for_talk - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: sammy786/wav2vec2-xlsr-romansh_sursilvan results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: rm-sursilv metrics: - name: Test WER type: wer value: 13.82 - name: Test CER type: cer value: 3.02 --- # sammy786/wav2vec2-xlsr-romansh_sursilvan This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-sursilv dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: 16.38 - Wer: 21.25 ## Model description "facebook/wav2vec2-xls-r-1b" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 4.825500 | 2.932350 | 1.000000 | | 400 | 1.325600 | 0.292645 | 0.415436 | | 600 | 0.709800 | 0.219167 | 0.324451 | | 800 | 0.576800 | 0.174390 | 0.275477 | | 1000 | 0.538100 | 0.183737 | 0.272116 | | 1200 | 0.475200 | 0.159078 | 0.253871 | | 1400 | 0.420400 | 0.167277 | 0.240907 | | 1600 | 0.393500 | 0.167216 | 0.247269 | | 1800 | 0.407500 | 0.178282 | 0.239827 | | 2000 | 0.374400 | 0.184590 | 0.239467 | | 2200 | 0.382600 | 0.164106 | 0.227824 | | 2400 | 0.363100 | 0.162543 | 0.228544 | | 2600 | 0.199000 | 0.172903 | 0.231665 | | 2800 | 0.150800 | 0.160117 | 0.222662 | | 3000 | 0.101100 | 0.169553 | 0.222662 | | 3200 | 0.104200 | 0.161056 | 0.220622 | | 3400 | 0.096900 | 0.161562 | 0.216781 | | 3600 | 0.092200 | 0.163880 | 0.212580 | | 3800 | 0.089200 | 0.162288 | 0.214140 | | 4000 | 0.076200 | 0.160470 | 0.213540 | | 4200 | 0.087900 | 0.162827 | 0.213060 | | 4400 | 0.066200 | 0.161096 | 0.213300 | | 4600 | 0.076000 | 0.162060 | 0.213660 | | 4800 | 0.071400 | 0.162045 | 0.213300 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-romansh_sursilvan --dataset mozilla-foundation/common_voice_8_0 --config rm-sursilv --split test ```
sana-ngu/Hat5-Roberta
9acae6280268216ffc5df811c7c529713f9cdc73
2022-02-09T04:26:50.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
sana-ngu
null
sana-ngu/Hat5-Roberta
2
null
transformers
24,671
Entry not found
sanayAI/output
5f199eae8d4b4e54b7c5982f2d3f95680b711ead
2021-05-20T04:41:38.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
sanayAI
null
sanayAI/output
2
null
transformers
24,672
Entry not found
sancharidan/scibet_expertfinder
5c47620f6a8ff65b4d59989a87b263e23c44ea37
2021-07-18T06:51:45.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
sancharidan
null
sancharidan/scibet_expertfinder
2
null
transformers
24,673
Entry not found
sanchit-gandhi/wav2vec2-2-bart-large-frozen-enc
2e4c59ae933be93a9b8e370de9f835f82941d80e
2022-02-22T15:43:21.000Z
[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "dataset:librispeech_asr", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
sanchit-gandhi
null
sanchit-gandhi/wav2vec2-2-bart-large-frozen-enc
2
null
transformers
24,674
--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.3123 - Wer: 0.0908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.4937 | 0.28 | 500 | 5.2312 | 0.9660 | | 3.821 | 0.56 | 1000 | 4.5810 | 0.9066 | | 1.2129 | 0.84 | 1500 | 1.3723 | 0.3928 | | 0.6575 | 1.12 | 2000 | 0.6645 | 0.1810 | | 0.489 | 1.4 | 2500 | 0.5523 | 0.1479 | | 0.3541 | 1.68 | 3000 | 0.4585 | 0.1195 | | 0.3573 | 1.96 | 3500 | 0.3859 | 0.1066 | | 0.2437 | 2.24 | 4000 | 0.3747 | 0.1015 | | 0.1406 | 2.52 | 4500 | 0.3346 | 0.0952 | | 0.1468 | 2.8 | 5000 | 0.3123 | 0.0908 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
sangrimlee/mt5-small-e2e-qg
ff7a0b28907eb6e09f39d251e219a40ae085cc68
2021-06-23T16:34:09.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
sangrimlee
null
sangrimlee/mt5-small-e2e-qg
2
null
transformers
24,675
Entry not found
sangrimlee/mt5-small-qg-hl
15489773d297075acd948273f54de29312e4fe18
2021-06-23T16:36:01.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
sangrimlee
null
sangrimlee/mt5-small-qg-hl
2
null
transformers
24,676
Entry not found
sankhajay/mt5-base-sinaha-qa
aa1d99b7150eb7bbf963d493adee978888916566
2022-01-27T05:35:18.000Z
[ "pytorch", "t5", "text2text-generation", "si", "transformers", "question-answering", "Sinhala", "autotrain_compatible" ]
question-answering
false
sankhajay
null
sankhajay/mt5-base-sinaha-qa
2
null
transformers
24,677
\n --- language: si tags: - question-answering - Sinhala widget: - context: "ශ්‍රී ලංකාව යනු ඉන්දියානු සාගරයේ පිහිටි මනරම් දුපතකි." text: "ශ්‍රී ලංකාව පිහිටා ඇත්තේ කොහෙද ?" --- # mt5-base-sinhala-qa This is an mt5-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k questions. The translation was done by google translate API. The training was done on Google Colab TPU environment with parallel training techniques. The training was done on around 9k data points which consists of context, question, answer trios for the Sinhala language. Evaluation is done using standard SQuAD evaluation script on around 1k data points which gave following results on the best parameter setting. Evaluation matrices used are EM matric and F1 score matric. Evaluation - {'EM': 39.413680781758956, 'f1': 66.16331104953571}
santhoshkolloju/ans_gen2
923df92a5877b292c9abd255bc6933490175159d
2021-06-23T14:07:52.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
santhoshkolloju
null
santhoshkolloju/ans_gen2
2
null
transformers
24,678
Entry not found
santhoshkolloju/t5_qg_multi3
157b3328f256b33e4b6d4b4a3e010435abe7b81f
2021-06-23T14:10:18.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
santhoshkolloju
null
santhoshkolloju/t5_qg_multi3
2
null
transformers
24,679
Entry not found
saraks/cuad-distil-document_name-08-25
27657914558539e500421b0a214efa9d5f2c1ed7
2021-08-25T10:39:43.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-document_name-08-25
2
null
transformers
24,680
Entry not found
saraks/cuad-distil-governing_law-08-25
e4767e1fa19411ac235fe86de30143e4b16d0a34
2021-08-25T16:29:52.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-governing_law-08-25
2
null
transformers
24,681
Entry not found
saraks/cuad-distil-parties-08-25
4290aab4166e5c1c32cb21ca76c155d5bead7839
2021-08-25T10:32:00.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-parties-08-25
2
null
transformers
24,682
Entry not found
saraks/cuad-distil-parties-cased-08-31-v1
b4f517909117c036ba272392ecc9e649dd6cd1b3
2021-08-31T16:36:18.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-parties-cased-08-31-v1
2
null
transformers
24,683
Entry not found
saraks/cuad-distil-parties-dates-law-08-18-id-question1
1d37116bc1e02b32d7b38ce69116002466ab529e
2021-08-18T17:49:38.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-parties-dates-law-08-18-id-question1
2
null
transformers
24,684
Entry not found
satishjasthij/cola
578ac3ee70e0993d84268155e7a8c2f23c07ff6b
2022-02-24T05:59:13.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
satishjasthij
null
satishjasthij/cola
2
null
transformers
24,685
Entry not found
scasutt/Prototype_training
883a6b51b6cc06d624eb29f01ce0d71d96c1109d
2022-01-04T14:59:34.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
scasutt
null
scasutt/Prototype_training
2
null
transformers
24,686
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Prototype_training results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Prototype_training This model is a fine-tuned version of [scasutt/Prototype_training](https://huggingface.co/scasutt/Prototype_training) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3719 - Wer: 0.4626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3853 | 1.47 | 100 | 0.3719 | 0.4626 | | 0.3867 | 2.94 | 200 | 0.3719 | 0.4626 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
scasutt/Prototype_training_large_model
fdc5615888fe5d74b938f3bed98cdad3b54fab91
2021-12-30T14:40:39.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
scasutt
null
scasutt/Prototype_training_large_model
2
null
transformers
24,687
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Prototype_training_large_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Prototype_training_large_model This model is a fine-tuned version of [scasutt/Prototype_training_large_model](https://huggingface.co/scasutt/Prototype_training_large_model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2585 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.0545 | 1.47 | 100 | 3.2604 | 1.0 | | 3.0413 | 2.93 | 200 | 3.2585 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
sdadas/polish-roberta-base-v1
2ce118eda32d6b81cf06be5d3d1b831ecf85322d
2022-02-19T10:01:21.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "license:lgpl-3.0", "autotrain_compatible" ]
fill-mask
false
sdadas
null
sdadas/polish-roberta-base-v1
2
null
transformers
24,688
--- license: lgpl-3.0 ---
seanbethard/autonlp-summarization_model-8771942
284ea1a366a8ca13e9f9ac4f26fa45c8996a4094
2021-08-26T19:34:20.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:seanbethard/autonlp-data-summarization_model", "transformers", "autonlp", "autotrain_compatible" ]
text2text-generation
false
seanbethard
null
seanbethard/autonlp-summarization_model-8771942
2
null
transformers
24,689
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - seanbethard/autonlp-data-summarization_model --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 8771942 ## Validation Metrics - Loss: 0.7463301420211792 - Rouge1: 19.9454 - Rouge2: 13.0362 - RougeL: 17.5797 - RougeLsum: 17.7459 - Gen Len: 19.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/seanbethard/autonlp-summarization_model-8771942 ```
sebaverde/bertitude-ita-tweets
92b0fff26077e4621499e76fb20633021ba32849
2021-05-20T05:09:11.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
sebaverde
null
sebaverde/bertitude-ita-tweets
2
null
transformers
24,690
Entry not found
seduerr/lang_det
51aa5add46ef8ff2b8e5f1219639f72a4e9b8ef2
2021-06-23T14:11:46.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/lang_det
2
null
transformers
24,691
Entry not found
seduerr/pai_exin
a99ecb147976d2278c53ef6898db14fbc0b0a44b
2021-07-08T08:46:58.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_exin
2
null
transformers
24,692
Entry not found
seduerr/pai_formtrans
21b506dd3f45b30aa67ddbb2fe7457f9a533fabc
2021-06-23T14:14:21.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_formtrans
2
null
transformers
24,693
Entry not found
seduerr/pai_fuser_short
9e4a2d404fe126bef6b573a0de5c6a6ff114ba9d
2021-05-01T13:43:18.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_fuser_short
2
null
transformers
24,694
Entry not found
seduerr/pai_m2f
d6dffa2a977bc262d7d2e7c6d3a0a02de5974acf
2021-06-23T14:14:58.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_m2f
2
null
transformers
24,695
Entry not found
seduerr/pai_paraph
9c3030ffe757506dad899777100598440a29ae66
2021-06-08T08:43:43.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_paraph
2
null
transformers
24,696
input_ = paraphrase: + str(input_) + ' </s>'
sello-ralethe/bert-base-generics-mlm
d94730c31ffa81fdf1c6f2ff1d823b87ecf7de7f
2021-05-20T05:12:57.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
sello-ralethe
null
sello-ralethe/bert-base-generics-mlm
2
null
transformers
24,697
Entry not found
serenay/autonlp-Emotion-14722565
88c6ada300f8b9b1571f59682393fab5ea53e351
2021-10-04T08:49:20.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:serenay/autonlp-data-Emotion", "transformers", "autonlp" ]
text-classification
false
serenay
null
serenay/autonlp-Emotion-14722565
2
null
transformers
24,698
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - serenay/autonlp-data-Emotion --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 14722565 ## Validation Metrics - Loss: 0.6077525615692139 - Accuracy: 0.7745398773006135 - Macro F1: 0.7287152925396537 - Micro F1: 0.7745398773006135 - Weighted F1: 0.7754701717098939 - Macro Precision: 0.7282186282186283 - Micro Precision: 0.7745398773006135 - Weighted Precision: 0.7787550922520248 - Macro Recall: 0.7314173610899214 - Micro Recall: 0.7745398773006135 - Weighted Recall: 0.7745398773006135 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/serenay/autonlp-Emotion-14722565 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("serenay/autonlp-Emotion-14722565", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("serenay/autonlp-Emotion-14722565", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
sergiyvl/just_first_try_to_my_diplom_onBert
3a18c52a42c6ad0433d163ba80f52e677c968647
2021-05-20T05:37:44.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
sergiyvl
null
sergiyvl/just_first_try_to_my_diplom_onBert
2
null
transformers
24,699
Entry not found