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CEBaB/roberta-base.CEBaB.causalm.service__food.3-class.exclusive.seed_45 | 558ee8833fdd9181e49f821db4ba24cd2403cc86 | 2022-05-24T10:07:40.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.3-class.exclusive.seed_45 | 1 | null | transformers | 32,400 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.3-class.exclusive.seed_46 | a15995c2823dd9d9f60860a6183475459f16ff97 | 2022-05-24T10:07:42.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.3-class.exclusive.seed_46 | 1 | null | transformers | 32,401 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_42 | 221ac44799e9fb8218890a47a2824bf8796fb229 | 2022-05-24T10:10:24.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,402 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_43 | 350b4fa79d7f6d826b6293f81d089c1a1c970827 | 2022-05-24T10:10:26.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,403 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_44 | 516048f8d31a4b30007c6f84dad0577b4761e87a | 2022-05-24T10:10:28.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,404 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_45 | 5b4362a7677ade068db7a92a5c2ff7e2fd16cb78 | 2022-05-24T10:10:31.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,405 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_46 | e2a5dc65aa64e6939ee7f1d114ce3a7e1c407369 | 2022-05-24T10:10:33.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,406 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_42 | 399bb878b13786b11e71209db154364fefc44f5d | 2022-05-24T10:10:35.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_42 | 1 | null | transformers | 32,407 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_43 | e84014534fba154036e747ebf8a8e4906b975f2b | 2022-05-24T10:10:37.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_43 | 1 | null | transformers | 32,408 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_44 | c35073e961f95a8e097dae77347a711a04d60096 | 2022-05-24T10:10:39.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_44 | 1 | null | transformers | 32,409 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_45 | cfcd1341eb7955e5d12d73b3f95d3a537b5304a7 | 2022-05-24T10:10:41.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_45 | 1 | null | transformers | 32,410 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_46 | fae4afe5aaa26625cc3f93ed93e746aae83bd450 | 2022-05-24T10:10:43.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.food__service.5-class.exclusive.seed_46 | 1 | null | transformers | 32,411 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_42 | 4da21f2648b49da2025ea5fe6ddf27e9e137070e | 2022-05-24T10:10:45.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,412 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_43 | 1f39ca0537e3bf71e5b04e89442185804daed291 | 2022-05-24T10:10:47.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,413 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_44 | 084821a15c7d27d050ffef415884336958724c00 | 2022-05-24T10:10:48.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,414 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_45 | 5c2de4c88cbfdf1271c1de2f36c48ec3ad100670 | 2022-05-24T10:10:50.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,415 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | 221c1af230600ed51d109402476051c175b81d14 | 2022-05-24T10:10:52.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,416 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | 7eeec62a88aa868db3fc0e312230a059fc5eb474 | 2022-05-24T10:10:54.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,417 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 646efc74f7294a70612f3a276953e93a8532bc2a | 2022-05-24T10:10:56.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,418 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | 05b55387ce981b0d4f05c11eb61e5094f0c347f1 | 2022-05-24T10:10:58.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,419 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | 3c127c5194e3cc204078acc7a6d4949e663ca7cb | 2022-05-24T10:11:00.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,420 | Entry not found |
CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | f4d5558ef0d032b4761c275bbe16fc3a24d34485 | 2022-05-24T10:11:02.000Z | [
"pytorch",
"roberta_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/roberta-base.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,421 | Entry not found |
Vkt/victor-hg-ptbr-2.0 | fe52be10b1e9eda9c46715e851d213c63a31cb2d | 2022-05-26T04:10:53.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Vkt | null | Vkt/victor-hg-ptbr-2.0 | 1 | null | transformers | 32,422 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: victor-hg-ptbr-2.0
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. -->
# victor-hg-ptbr-2.0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0240
- Wer: 0.0219
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4069 | 0.21 | 400 | 1.1372 | 0.9140 |
| 0.8079 | 0.43 | 800 | 0.5822 | 0.5339 |
| 0.5821 | 0.64 | 1200 | 0.4226 | 0.4177 |
| 0.5159 | 0.86 | 1600 | 0.4074 | 0.3970 |
| 0.4484 | 1.07 | 2000 | 0.3144 | 0.3220 |
| 0.3937 | 1.29 | 2400 | 0.3160 | 0.3264 |
| 0.3911 | 1.5 | 2800 | 0.2863 | 0.2956 |
| 0.3761 | 1.71 | 3200 | 0.3029 | 0.3128 |
| 0.3722 | 1.93 | 3600 | 0.2771 | 0.2933 |
| 0.3193 | 2.14 | 4000 | 0.2603 | 0.2795 |
| 0.3013 | 2.36 | 4400 | 0.2682 | 0.2703 |
| 0.3039 | 2.57 | 4800 | 0.2630 | 0.2618 |
| 0.3133 | 2.79 | 5200 | 0.2578 | 0.2629 |
| 0.3173 | 3.0 | 5600 | 0.2640 | 0.2746 |
| 0.2521 | 3.22 | 6000 | 0.2797 | 0.2662 |
| 0.2654 | 3.43 | 6400 | 0.2762 | 0.2640 |
| 0.2586 | 3.64 | 6800 | 0.2642 | 0.2596 |
| 0.265 | 3.86 | 7200 | 0.2656 | 0.2794 |
| 0.2432 | 4.07 | 7600 | 0.2459 | 0.2497 |
| 0.226 | 4.29 | 8000 | 0.2533 | 0.2509 |
| 0.2385 | 4.5 | 8400 | 0.2332 | 0.2394 |
| 0.2332 | 4.72 | 8800 | 0.2500 | 0.2569 |
| 0.2358 | 4.93 | 9200 | 0.2384 | 0.2489 |
| 0.2169 | 5.14 | 9600 | 0.2410 | 0.2380 |
| 0.2038 | 5.36 | 10000 | 0.2426 | 0.2333 |
| 0.2109 | 5.57 | 10400 | 0.2480 | 0.2473 |
| 0.2147 | 5.79 | 10800 | 0.2341 | 0.2272 |
| 0.2153 | 6.0 | 11200 | 0.2402 | 0.2424 |
| 0.186 | 6.22 | 11600 | 0.2560 | 0.2489 |
| 0.1854 | 6.43 | 12000 | 0.2444 | 0.2402 |
| 0.1915 | 6.65 | 12400 | 0.2720 | 0.2531 |
| 0.1929 | 6.86 | 12800 | 0.2516 | 0.2342 |
| 0.1842 | 7.07 | 13200 | 0.2480 | 0.2304 |
| 0.1682 | 7.29 | 13600 | 0.2393 | 0.2276 |
| 0.1753 | 7.5 | 14000 | 0.2514 | 0.2263 |
| 0.1798 | 7.72 | 14400 | 0.2191 | 0.2178 |
| 0.1736 | 7.93 | 14800 | 0.2351 | 0.2197 |
| 0.1668 | 8.15 | 15200 | 0.2315 | 0.2194 |
| 0.1545 | 8.36 | 15600 | 0.2291 | 0.2079 |
| 0.1508 | 8.57 | 16000 | 0.2351 | 0.2134 |
| 0.1662 | 8.79 | 16400 | 0.2298 | 0.2197 |
| 0.1621 | 9.0 | 16800 | 0.2314 | 0.2219 |
| 0.1416 | 9.22 | 17200 | 0.2306 | 0.2192 |
| 0.1455 | 9.43 | 17600 | 0.2466 | 0.2184 |
| 0.1522 | 9.65 | 18000 | 0.2392 | 0.2255 |
| 0.1434 | 9.86 | 18400 | 0.2464 | 0.2208 |
| 0.1362 | 10.08 | 18800 | 0.2351 | 0.2095 |
| 0.127 | 10.29 | 19200 | 0.2373 | 0.2110 |
| 0.133 | 10.5 | 19600 | 0.2269 | 0.2031 |
| 0.1308 | 10.72 | 20000 | 0.2400 | 0.2096 |
| 0.1331 | 10.93 | 20400 | 0.2243 | 0.2083 |
| 0.125 | 11.15 | 20800 | 0.2334 | 0.2063 |
| 0.1236 | 11.36 | 21200 | 0.2195 | 0.2044 |
| 0.1263 | 11.58 | 21600 | 0.2263 | 0.2050 |
| 0.1235 | 11.79 | 22000 | 0.2217 | 0.2087 |
| 0.1301 | 12.0 | 22400 | 0.2332 | 0.2094 |
| 0.1123 | 12.22 | 22800 | 0.2195 | 0.2068 |
| 0.117 | 12.43 | 23200 | 0.2266 | 0.2110 |
| 0.1156 | 12.65 | 23600 | 0.2469 | 0.2063 |
| 0.1117 | 12.86 | 24000 | 0.2379 | 0.2035 |
| 0.1124 | 13.08 | 24400 | 0.2156 | 0.1963 |
| 0.106 | 13.29 | 24800 | 0.2310 | 0.1988 |
| 0.1066 | 13.5 | 25200 | 0.2334 | 0.1950 |
| 0.1069 | 13.72 | 25600 | 0.2230 | 0.2011 |
| 0.1089 | 13.93 | 26000 | 0.2233 | 0.2003 |
| 0.0977 | 14.15 | 26400 | 0.2273 | 0.1895 |
| 0.0972 | 14.36 | 26800 | 0.2265 | 0.1887 |
| 0.1005 | 14.58 | 27200 | 0.2196 | 0.1934 |
| 0.1058 | 14.79 | 27600 | 0.2213 | 0.1870 |
| 0.1027 | 15.01 | 28000 | 0.2361 | 0.1916 |
| 0.0886 | 15.22 | 28400 | 0.2275 | 0.1815 |
| 0.0885 | 15.43 | 28800 | 0.2230 | 0.1891 |
| 0.0911 | 15.65 | 29200 | 0.2237 | 0.1989 |
| 0.0923 | 15.86 | 29600 | 0.2200 | 0.1857 |
| 0.0868 | 16.08 | 30000 | 0.2248 | 0.1875 |
| 0.0812 | 16.29 | 30400 | 0.2240 | 0.1874 |
| 0.0829 | 16.51 | 30800 | 0.2198 | 0.1814 |
| 0.0832 | 16.72 | 31200 | 0.2328 | 0.1892 |
| 0.0822 | 16.93 | 31600 | 0.2283 | 0.1862 |
| 0.0828 | 17.15 | 32000 | 0.2283 | 0.1806 |
| 0.0791 | 17.36 | 32400 | 0.2197 | 0.1787 |
| 0.0801 | 17.58 | 32800 | 0.2249 | 0.1815 |
| 0.0804 | 17.79 | 33200 | 0.2304 | 0.1789 |
| 0.0833 | 18.01 | 33600 | 0.2235 | 0.1832 |
| 0.0762 | 18.22 | 34000 | 0.2358 | 0.1784 |
| 0.0688 | 18.44 | 34400 | 0.2183 | 0.1758 |
| 0.0751 | 18.65 | 34800 | 0.2169 | 0.1805 |
| 0.0729 | 18.86 | 35200 | 0.2296 | 0.1770 |
| 0.0681 | 19.08 | 35600 | 0.2380 | 0.1770 |
| 0.067 | 19.29 | 36000 | 0.2153 | 0.1777 |
| 0.0669 | 19.51 | 36400 | 0.2260 | 0.1742 |
| 0.0824 | 19.72 | 36800 | 0.0289 | 0.0310 |
| 0.0857 | 19.94 | 37200 | 0.0289 | 0.0322 |
| 0.0799 | 20.15 | 37600 | 0.0264 | 0.0298 |
| 0.0767 | 20.36 | 38000 | 0.0273 | 0.0318 |
| 0.079 | 20.58 | 38400 | 0.0274 | 0.0320 |
| 0.0791 | 20.79 | 38800 | 0.0279 | 0.0318 |
| 0.0805 | 21.01 | 39200 | 0.0285 | 0.0330 |
| 0.0622 | 21.22 | 39600 | 0.0263 | 0.0306 |
| 0.0622 | 21.44 | 40000 | 0.0290 | 0.0318 |
| 0.0672 | 21.65 | 40400 | 0.0278 | 0.0330 |
| 0.0706 | 21.86 | 40800 | 0.0270 | 0.0297 |
| 0.0619 | 22.08 | 41200 | 0.0288 | 0.0328 |
| 0.0633 | 22.29 | 41600 | 0.0256 | 0.0303 |
| 0.0618 | 22.51 | 42000 | 0.0263 | 0.0299 |
| 0.0576 | 22.72 | 42400 | 0.0273 | 0.0301 |
| 0.0583 | 22.94 | 42800 | 0.0282 | 0.0297 |
| 0.0565 | 23.15 | 43200 | 0.0256 | 0.0280 |
| 0.0557 | 23.37 | 43600 | 0.0268 | 0.0280 |
| 0.0548 | 23.58 | 44000 | 0.0266 | 0.0291 |
| 0.056 | 23.79 | 44400 | 0.0264 | 0.0290 |
| 0.0546 | 24.01 | 44800 | 0.0273 | 0.0284 |
| 0.0496 | 24.22 | 45200 | 0.0261 | 0.0279 |
| 0.0512 | 24.44 | 45600 | 0.0256 | 0.0281 |
| 0.0482 | 24.65 | 46000 | 0.0264 | 0.0285 |
| 0.0503 | 24.87 | 46400 | 0.0256 | 0.0268 |
| 0.0471 | 25.08 | 46800 | 0.0270 | 0.0282 |
| 0.0453 | 25.29 | 47200 | 0.0255 | 0.0267 |
| 0.0431 | 25.51 | 47600 | 0.0251 | 0.0264 |
| 0.0464 | 25.72 | 48000 | 0.0262 | 0.0261 |
| 0.0431 | 25.94 | 48400 | 0.0257 | 0.0265 |
| 0.0405 | 26.15 | 48800 | 0.0260 | 0.0251 |
| 0.0406 | 26.37 | 49200 | 0.0246 | 0.0250 |
| 0.0397 | 26.58 | 49600 | 0.0252 | 0.0254 |
| 0.0403 | 26.8 | 50000 | 0.0250 | 0.0256 |
| 0.0385 | 27.01 | 50400 | 0.0254 | 0.0241 |
| 0.0398 | 27.22 | 50800 | 0.0255 | 0.0242 |
| 0.0363 | 27.44 | 51200 | 0.0250 | 0.0236 |
| 0.0372 | 27.65 | 51600 | 0.0247 | 0.0232 |
| 0.0362 | 27.87 | 52000 | 0.0240 | 0.0226 |
| 0.0367 | 28.08 | 52400 | 0.0246 | 0.0224 |
| 0.0347 | 28.3 | 52800 | 0.0247 | 0.0229 |
| 0.0348 | 28.51 | 53200 | 0.0241 | 0.0229 |
| 0.0331 | 28.72 | 53600 | 0.0242 | 0.0224 |
| 0.0339 | 28.94 | 54000 | 0.0241 | 0.0220 |
| 0.0336 | 29.15 | 54400 | 0.0244 | 0.0221 |
| 0.0336 | 29.37 | 54800 | 0.0243 | 0.0215 |
| 0.0349 | 29.58 | 55200 | 0.0239 | 0.0217 |
| 0.0308 | 29.8 | 55600 | 0.0240 | 0.0219 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.8.1+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
|
NabilOulbaz/bertweet_retrained_semEval2019 | f7822d0a52beb47dd71eb0fa430c8797778b8769 | 2022-05-24T13:31:09.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | NabilOulbaz | null | NabilOulbaz/bertweet_retrained_semEval2019 | 1 | null | transformers | 32,423 | Entry not found |
ismail-lucifer011/autotrain-name_all-904029569 | 0d9a45f404f8c8a09b0054899aa3a13add72ba39 | 2022-05-24T14:42:21.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:ismail-lucifer011/autotrain-data-name_all",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | ismail-lucifer011 | null | ismail-lucifer011/autotrain-name_all-904029569 | 1 | null | transformers | 32,424 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ismail-lucifer011/autotrain-data-name_all
co2_eq_emissions: 0.527083766435658
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 904029569
- CO2 Emissions (in grams): 0.527083766435658
## Validation Metrics
- Loss: 0.0036354903131723404
- Accuracy: 0.9989951257999512
- Precision: 0.9888963290924173
- Recall: 0.9934437092741895
- F1: 0.9911648034619546
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/ismail-lucifer011/autotrain-name_all-904029569
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("ismail-lucifer011/autotrain-name_all-904029569", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ismail-lucifer011/autotrain-name_all-904029569", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AntoDono/DialoGPT-Bopy-Patch2 | 919713b62eaebf247231d52e5fc87f1186c6ac58 | 2022-05-24T20:15:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | AntoDono | null | AntoDono/DialoGPT-Bopy-Patch2 | 1 | null | transformers | 32,425 | Entry not found |
anablasi/model_10k_qa | 66d343f774a97a496d0a7ae6c66baa2239f39753 | 2022-05-24T22:04:29.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | anablasi | null | anablasi/model_10k_qa | 1 | null | transformers | 32,426 | Entry not found |
chrisvinsen/xlsr-wav2vec2-2 | 2355b754056a85f5a636d0a76d413ceaac788784 | 2022-05-25T10:21:44.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/xlsr-wav2vec2-2 | 1 | null | transformers | 32,427 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xlsr-wav2vec2-2
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. -->
# xlsr-wav2vec2-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5884
- Wer: 0.4301
## 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: 800
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.6058 | 1.38 | 400 | 3.1894 | 1.0 |
| 2.3145 | 2.76 | 800 | 0.7193 | 0.7976 |
| 0.6737 | 4.14 | 1200 | 0.5338 | 0.6056 |
| 0.4651 | 5.52 | 1600 | 0.5699 | 0.6007 |
| 0.3968 | 6.9 | 2000 | 0.4608 | 0.5221 |
| 0.3281 | 8.28 | 2400 | 0.5264 | 0.5209 |
| 0.2937 | 9.65 | 2800 | 0.5366 | 0.5096 |
| 0.2619 | 11.03 | 3200 | 0.4902 | 0.5021 |
| 0.2394 | 12.41 | 3600 | 0.4706 | 0.4908 |
| 0.2139 | 13.79 | 4000 | 0.5526 | 0.4871 |
| 0.2034 | 15.17 | 4400 | 0.5396 | 0.5108 |
| 0.1946 | 16.55 | 4800 | 0.4959 | 0.4866 |
| 0.1873 | 17.93 | 5200 | 0.4898 | 0.4877 |
| 0.1751 | 19.31 | 5600 | 0.5488 | 0.4932 |
| 0.1668 | 20.69 | 6000 | 0.5645 | 0.4986 |
| 0.1638 | 22.07 | 6400 | 0.5367 | 0.4946 |
| 0.1564 | 23.45 | 6800 | 0.5282 | 0.4898 |
| 0.1566 | 24.83 | 7200 | 0.5489 | 0.4841 |
| 0.1522 | 26.21 | 7600 | 0.5439 | 0.4821 |
| 0.1378 | 27.59 | 8000 | 0.5796 | 0.4866 |
| 0.1459 | 28.96 | 8400 | 0.5603 | 0.4875 |
| 0.1406 | 30.34 | 8800 | 0.6773 | 0.5005 |
| 0.1298 | 31.72 | 9200 | 0.5858 | 0.4827 |
| 0.1268 | 33.1 | 9600 | 0.6007 | 0.4790 |
| 0.1204 | 34.48 | 10000 | 0.5716 | 0.4734 |
| 0.113 | 35.86 | 10400 | 0.5866 | 0.4748 |
| 0.1088 | 37.24 | 10800 | 0.5790 | 0.4752 |
| 0.1074 | 38.62 | 11200 | 0.5966 | 0.4721 |
| 0.1018 | 40.0 | 11600 | 0.5720 | 0.4668 |
| 0.0968 | 41.38 | 12000 | 0.5826 | 0.4698 |
| 0.0874 | 42.76 | 12400 | 0.5937 | 0.4634 |
| 0.0843 | 44.14 | 12800 | 0.6056 | 0.4640 |
| 0.0822 | 45.52 | 13200 | 0.5531 | 0.4569 |
| 0.0806 | 46.9 | 13600 | 0.5669 | 0.4484 |
| 0.072 | 48.28 | 14000 | 0.5683 | 0.4484 |
| 0.0734 | 49.65 | 14400 | 0.5735 | 0.4437 |
| 0.0671 | 51.03 | 14800 | 0.5455 | 0.4394 |
| 0.0617 | 52.41 | 15200 | 0.5838 | 0.4365 |
| 0.0607 | 53.79 | 15600 | 0.6233 | 0.4397 |
| 0.0593 | 55.17 | 16000 | 0.5649 | 0.4340 |
| 0.0551 | 56.55 | 16400 | 0.5923 | 0.4392 |
| 0.0503 | 57.93 | 16800 | 0.5858 | 0.4325 |
| 0.0496 | 59.31 | 17200 | 0.5884 | 0.4301 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nobuotto/distilbert-base-uncased-finetuned-imdb | 089d4b96fb093c078029ddc9963e4d5de598553a | 2022-05-25T00:56:20.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | nobuotto | null | nobuotto/distilbert-base-uncased-finetuned-imdb | 1 | null | transformers | 32,428 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4734
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7117 | 1.0 | 157 | 2.4976 |
| 2.5773 | 2.0 | 314 | 2.4243 |
| 2.5263 | 3.0 | 471 | 2.4348 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
sagnikrayc/prajjwal-bert-small-snli | 5ef8796da4f28a27fa8c42003aedb88a08f1f51a | 2022-05-25T02:54:36.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sagnikrayc | null | sagnikrayc/prajjwal-bert-small-snli | 1 | null | transformers | 32,429 | Entry not found |
ChrisKalahiki/mt5-small-finetuned-amazon-en-es | e77e7dea5137c35c809e97274ed2b7b51474aa45 | 2022-05-25T03:11:06.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ChrisKalahiki | null | ChrisKalahiki/mt5-small-finetuned-amazon-en-es | 1 | null | transformers | 32,430 | Entry not found |
morahil/wav2vec2-hindi-new | f768d2ed4d5e0a029017fbff35b293090e671b12 | 2022-05-25T06:09:22.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | morahil | null | morahil/wav2vec2-hindi-new | 1 | null | transformers | 32,431 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-hindi-new
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-hindi-new
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None 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: 0.0001
- 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: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
jihae/kogpt2news | b480fd14067f4f19470a41b4aa45cc0936518ab1 | 2022-06-03T04:37:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jihae | null | jihae/kogpt2news | 1 | null | transformers | 32,432 | Entry not found |
PontifexMaximus/mt5-small-finetuned-fa-to-en | a7fb60c812d42b2fa63a2a98ce3cc8d01049f86b | 2022-05-25T06:58:04.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/mt5-small-finetuned-fa-to-en | 1 | null | transformers | 32,433 | Entry not found |
leonweber/muppet-large-118 | 95fc68bc87f8b217c517b4c6f402b58b91eda85b | 2022-05-25T08:47:07.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | leonweber | null | leonweber/muppet-large-118 | 1 | null | transformers | 32,434 | Entry not found |
ronanki/ml_use_512_MNR_15 | 9be6c92760fae2df6cd27345a1e879d8ac9feacb | 2022-05-25T12:11:55.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | ronanki | null | ronanki/ml_use_512_MNR_15 | 1 | null | sentence-transformers | 32,435 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ronanki/ml_use_512_MNR_15
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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('ronanki/ml_use_512_MNR_15')
embeddings = model.encode(sentences)
print(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=ronanki/ml_use_512_MNR_15)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
neuralmagic/oBERT-12-downstream-pruned-unstructured-80-squadv1 | 40d5b575e3b9f9edb1c841f8330947d7188d20ed | 2022-06-20T11:36:49.000Z | [
"pytorch",
"en",
"dataset:squad",
"arxiv:2203.07259",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression"
] | null | false | neuralmagic | null | neuralmagic/oBERT-12-downstream-pruned-unstructured-80-squadv1 | 1 | null | null | 32,436 | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-12-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 80%`.
```
Pruning method: oBERT downstream unstructured
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 80%
Number of layers: 12
```
The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
```
| oBERT 80% | F1 | EM |
| ------------ | ----- | ----- |
| seed=42 | 88.95 | 82.08 |
| seed=3407 (*)| 89.16 | 82.05 |
| seed=54321 | 89.01 | 82.12 |
| ------------ | ----- | ----- |
| mean | 89.04 | 82.08 |
| stdev | 0.108 | 0.035 |
```
Code: _coming soon_
## BibTeX entry and citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
``` |
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp | c40a3309f3517062d0bfcc9867fe836481f6bd6b | 2022-06-20T11:40:11.000Z | [
"pytorch",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression"
] | null | false | neuralmagic | null | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp | 1 | null | null | 32,437 | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: qqp
---
# oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - QQP 97%`.
```
Pruning method: oBERT upstream unstructured + sparse-transfer to downstream
Paper: https://arxiv.org/abs/2203.07259
Dataset: QQP
Sparsity: 97%
Number of layers: 12
```
The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
```
| oBERT 97% | acc | F1 |
| ------------ | ----- | ----- |
| seed=42 (*)| 89.85 | 86.41 |
| seed=3407 | 89.72 | 86.42 |
| seed=54321 | 89.70 | 86.24 |
| ------------ | ----- | ----- |
| mean | 89.76 | 86.35 |
| stdev | 0.081 | 0.101 |
```
Code: _coming soon_
## BibTeX entry and citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-6-downstream-dense-squadv1 | 3ad43f2b30b84816b7ff58ec0ce02ad632fe9022 | 2022-06-20T11:36:52.000Z | [
"pytorch",
"en",
"dataset:squad",
"arxiv:2203.07259",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression"
] | null | false | neuralmagic | null | neuralmagic/oBERT-6-downstream-dense-squadv1 | 1 | null | null | 32,438 | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models:
- 80% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1`
- 80% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1`
- 90% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1`
- 90% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1`
SQuADv1 dev-set:
```
EM = 81.17
F1 = 88.32
```
## BibTeX entry and citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
``` |
wrice/wav2vec2-base-timit-demo-google-colab | 68942be183e216eaaa6168eba5684d712fe0a815 | 2022-05-26T00:23:52.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | wrice | null | wrice/wav2vec2-base-timit-demo-google-colab | 1 | null | transformers | 32,439 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6348
- Wer: 0.3204
## 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: 4
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.2767 | 0.5 | 500 | 2.9921 | 1.0 |
| 1.509 | 1.01 | 1000 | 0.8223 | 0.6031 |
| 0.7226 | 1.51 | 1500 | 0.6185 | 0.4935 |
| 0.5777 | 2.01 | 2000 | 0.5600 | 0.4569 |
| 0.4306 | 2.51 | 2500 | 0.4985 | 0.4229 |
| 0.3854 | 3.02 | 3000 | 0.5113 | 0.4200 |
| 0.3161 | 3.52 | 3500 | 0.5197 | 0.4042 |
| 0.2904 | 4.02 | 4000 | 0.4900 | 0.3936 |
| 0.2404 | 4.52 | 4500 | 0.5209 | 0.3797 |
| 0.2546 | 5.03 | 5000 | 0.4836 | 0.3855 |
| 0.2278 | 5.53 | 5500 | 0.5194 | 0.3676 |
| 0.2049 | 6.03 | 6000 | 0.5647 | 0.4042 |
| 0.199 | 6.53 | 6500 | 0.5699 | 0.3932 |
| 0.1932 | 7.04 | 7000 | 0.5498 | 0.3694 |
| 0.1633 | 7.54 | 7500 | 0.5918 | 0.3686 |
| 0.1674 | 8.04 | 8000 | 0.5298 | 0.3716 |
| 0.1496 | 8.54 | 8500 | 0.5788 | 0.3726 |
| 0.1488 | 9.05 | 9000 | 0.5603 | 0.3664 |
| 0.1286 | 9.55 | 9500 | 0.5427 | 0.3550 |
| 0.1364 | 10.05 | 10000 | 0.5794 | 0.3621 |
| 0.1177 | 10.55 | 10500 | 0.5587 | 0.3606 |
| 0.1126 | 11.06 | 11000 | 0.5788 | 0.3519 |
| 0.1272 | 11.56 | 11500 | 0.5859 | 0.3595 |
| 0.1414 | 12.06 | 12000 | 0.5852 | 0.3586 |
| 0.1081 | 12.56 | 12500 | 0.5653 | 0.3727 |
| 0.1073 | 13.07 | 13000 | 0.5653 | 0.3526 |
| 0.0922 | 13.57 | 13500 | 0.5758 | 0.3583 |
| 0.09 | 14.07 | 14000 | 0.5990 | 0.3599 |
| 0.0987 | 14.57 | 14500 | 0.5837 | 0.3516 |
| 0.0823 | 15.08 | 15000 | 0.5639 | 0.3454 |
| 0.0752 | 15.58 | 15500 | 0.5663 | 0.3542 |
| 0.0714 | 16.08 | 16000 | 0.6273 | 0.3419 |
| 0.0693 | 16.58 | 16500 | 0.6389 | 0.3441 |
| 0.0634 | 17.09 | 17000 | 0.6006 | 0.3409 |
| 0.063 | 17.59 | 17500 | 0.6456 | 0.3444 |
| 0.0627 | 18.09 | 18000 | 0.6706 | 0.3458 |
| 0.0519 | 18.59 | 18500 | 0.6370 | 0.3396 |
| 0.059 | 19.1 | 19000 | 0.6602 | 0.3390 |
| 0.0495 | 19.6 | 19500 | 0.6642 | 0.3364 |
| 0.0601 | 20.1 | 20000 | 0.6495 | 0.3408 |
| 0.07 | 20.6 | 20500 | 0.6526 | 0.3476 |
| 0.0517 | 21.11 | 21000 | 0.6265 | 0.3401 |
| 0.0434 | 21.61 | 21500 | 0.6364 | 0.3372 |
| 0.0383 | 22.11 | 22000 | 0.6742 | 0.3377 |
| 0.0372 | 22.61 | 22500 | 0.6499 | 0.3330 |
| 0.0329 | 23.12 | 23000 | 0.6877 | 0.3307 |
| 0.0366 | 23.62 | 23500 | 0.6351 | 0.3303 |
| 0.0372 | 24.12 | 24000 | 0.6547 | 0.3286 |
| 0.031 | 24.62 | 24500 | 0.6757 | 0.3304 |
| 0.0367 | 25.13 | 25000 | 0.6507 | 0.3312 |
| 0.0309 | 25.63 | 25500 | 0.6645 | 0.3298 |
| 0.03 | 26.13 | 26000 | 0.6342 | 0.3325 |
| 0.0274 | 26.63 | 26500 | 0.6614 | 0.3255 |
| 0.0236 | 27.14 | 27000 | 0.6614 | 0.3222 |
| 0.0263 | 27.64 | 27500 | 0.6560 | 0.3242 |
| 0.0264 | 28.14 | 28000 | 0.6337 | 0.3237 |
| 0.0234 | 28.64 | 28500 | 0.6322 | 0.3208 |
| 0.0249 | 29.15 | 29000 | 0.6367 | 0.3218 |
| 0.0252 | 29.65 | 29500 | 0.6348 | 0.3204 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.8.2+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6
|
cristinakuo/wav2vec2-sala1 | eaf4eb561589d71c68554c2b6782a5a01155104e | 2022-05-29T05:18:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | cristinakuo | null | cristinakuo/wav2vec2-sala1 | 1 | null | transformers | 32,440 | Entry not found |
duclee9x/wav2vec2-voa-example | 4a3119d181cd5790f4bfc0f25d20354dea22fc56 | 2022-05-26T08:32:06.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | duclee9x | null | duclee9x/wav2vec2-voa-example | 1 | null | transformers | 32,441 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-voa-example
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-voa-example
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.296 | 4.35 | 500 | 3.7226 | 1.0 |
| 3.027 | 8.7 | 1000 | 3.7233 | 1.0 |
| 3.0376 | 13.04 | 1500 | 3.7246 | 1.0 |
| 3.0221 | 17.39 | 2000 | nan | 1.0 |
| 0.0 | 21.74 | 2500 | nan | 1.0 |
| 0.0 | 26.09 | 3000 | nan | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
shufei/wav2vec2-common_voice-tr-demo | 323ae62254d3ca7c66d091278575bb4e227063a1 | 2022-05-26T02:14:22.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | shufei | null | shufei/wav2vec2-common_voice-tr-demo | 1 | null | transformers | 32,442 | Entry not found |
wrice/wavlm-large-timit-punctuation | 5e26a301c6c16bd5f21d66d26ec970b0558bca64 | 2022-05-31T13:31:43.000Z | [
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | wrice | null | wrice/wavlm-large-timit-punctuation | 1 | null | transformers | 32,443 | ---
tags:
- generated_from_trainer
model-index:
- name: wavlm-large-timit-punctuation
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. -->
# wavlm-large-timit-punctuation
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3368
- Wer: 0.2601
## 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: 8
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.2379 | 1.0 | 500 | 3.1228 | 1.0 |
| 2.5847 | 2.01 | 1000 | 1.1550 | 0.9147 |
| 1.0034 | 3.01 | 1500 | 0.5856 | 0.5180 |
| 0.5868 | 4.02 | 2000 | 0.4238 | 0.4229 |
| 0.3892 | 5.02 | 2500 | 0.3356 | 0.3665 |
| 0.2926 | 6.02 | 3000 | 0.3196 | 0.3360 |
| 0.2294 | 7.03 | 3500 | 0.3046 | 0.3170 |
| 0.1976 | 8.03 | 4000 | 0.3032 | 0.3111 |
| 0.1644 | 9.04 | 4500 | 0.2946 | 0.2954 |
| 0.1574 | 10.04 | 5000 | 0.3211 | 0.2998 |
| 0.1391 | 11.04 | 5500 | 0.2986 | 0.2922 |
| 0.1124 | 12.05 | 6000 | 0.2948 | 0.2837 |
| 0.1003 | 13.05 | 6500 | 0.2928 | 0.2788 |
| 0.1031 | 14.06 | 7000 | 0.3230 | 0.2805 |
| 0.0901 | 15.06 | 7500 | 0.3081 | 0.2749 |
| 0.0842 | 16.06 | 8000 | 0.3075 | 0.2726 |
| 0.0809 | 17.07 | 8500 | 0.3215 | 0.2717 |
| 0.0747 | 18.07 | 9000 | 0.3272 | 0.2721 |
| 0.0735 | 19.08 | 9500 | 0.3242 | 0.2684 |
| 0.0631 | 20.08 | 10000 | 0.3216 | 0.2640 |
| 0.0632 | 21.08 | 10500 | 0.3149 | 0.2646 |
| 0.0625 | 22.09 | 11000 | 0.3196 | 0.2630 |
| 0.0611 | 23.09 | 11500 | 0.3244 | 0.2638 |
| 0.0532 | 24.1 | 12000 | 0.3271 | 0.2641 |
| 0.0503 | 25.1 | 12500 | 0.3368 | 0.2636 |
| 0.0534 | 26.1 | 13000 | 0.3393 | 0.2627 |
| 0.049 | 27.11 | 13500 | 0.3389 | 0.2626 |
| 0.0441 | 28.11 | 14000 | 0.3375 | 0.2605 |
| 0.0522 | 29.12 | 14500 | 0.3368 | 0.2601 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.8.2+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6
|
LDD/wwm-2 | 8044009f1efc735aaf172bd5780a2a24629650cc | 2022-05-26T03:36:46.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | LDD | null | LDD/wwm-2 | 1 | null | transformers | 32,444 | Entry not found |
RuiqianLi/one-simple-finetune-test | 1a5109c945f1e352ba92d7c793e3ca63c0a06478 | 2022-05-26T07:41:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:li_singlish",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | RuiqianLi | null | RuiqianLi/one-simple-finetune-test | 1 | null | transformers | 32,445 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- li_singlish
model-index:
- name: one-simple-finetune-test
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. -->
# one-simple-finetune-test
This model is a fine-tuned version of [RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab](https://huggingface.co/RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab) on the li_singlish 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: 0.001
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Giseok/wav2vec2-base-STTTest | 3a8337c467adee456eed5e2458653697bcb73618 | 2022-05-27T09:12:19.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Giseok | null | Giseok/wav2vec2-base-STTTest | 1 | null | transformers | 32,446 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-STTTest
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-base-STTTest
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5198
- Wer: 0.3393
## 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: 8
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.231 | 1.0 | 500 | 0.4337 | 0.4100 |
| 0.1845 | 2.01 | 1000 | 0.4296 | 0.3931 |
| 0.1551 | 3.01 | 1500 | 0.4397 | 0.3770 |
| 0.1479 | 4.02 | 2000 | 0.4524 | 0.3827 |
| 0.1186 | 5.02 | 2500 | 0.5182 | 0.3795 |
| 0.1079 | 6.02 | 3000 | 0.4799 | 0.3737 |
| 0.0974 | 7.03 | 3500 | 0.4966 | 0.3860 |
| 0.0878 | 8.03 | 4000 | 0.4993 | 0.3699 |
| 0.0788 | 9.04 | 4500 | 0.5183 | 0.3678 |
| 0.0732 | 10.04 | 5000 | 0.5064 | 0.3635 |
| 0.0664 | 11.04 | 5500 | 0.5330 | 0.3663 |
| 0.0596 | 12.05 | 6000 | 0.5147 | 0.3516 |
| 0.0538 | 13.05 | 6500 | 0.5254 | 0.3581 |
| 0.0535 | 14.06 | 7000 | 0.4902 | 0.3534 |
| 0.0492 | 15.06 | 7500 | 0.5115 | 0.3488 |
| 0.0455 | 16.06 | 8000 | 0.5250 | 0.3472 |
| 0.0434 | 17.07 | 8500 | 0.5338 | 0.3515 |
| 0.0351 | 18.07 | 9000 | 0.5365 | 0.3444 |
| 0.0341 | 19.08 | 9500 | 0.4886 | 0.3439 |
| 0.0332 | 20.08 | 10000 | 0.5234 | 0.3475 |
| 0.0289 | 21.08 | 10500 | 0.5375 | 0.3464 |
| 0.028 | 22.09 | 11000 | 0.5395 | 0.3478 |
| 0.0225 | 23.09 | 11500 | 0.5236 | 0.3428 |
| 0.0244 | 24.1 | 12000 | 0.5122 | 0.3402 |
| 0.0246 | 25.1 | 12500 | 0.5212 | 0.3390 |
| 0.0214 | 26.1 | 13000 | 0.5198 | 0.3393 |
| 0.0179 | 27.11 | 13500 | 0.5198 | 0.3393 |
| 0.0194 | 28.11 | 14000 | 0.5198 | 0.3393 |
| 0.0193 | 29.12 | 14500 | 0.5198 | 0.3393 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1+cu111
- Datasets 1.18.3
- Tokenizers 0.12.1
|
aioxlabs/dvoice-swahili | 05eb3471d4d24f9eee3b5482362533530145ca3e | 2022-05-28T08:20:36.000Z | [
"wav2vec2",
"feature-extraction",
"sw",
"dataset:commonvoice",
"speechbrain",
"CTC",
"pytorch",
"Transformer",
"license:apache-2.0",
"automatic-speech-recognition"
] | automatic-speech-recognition | false | aioxlabs | null | aioxlabs/dvoice-swahili | 1 | null | speechbrain | 32,447 | ---
language: "sw"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a [DVoice-VoxLingua107](https://zenodo.org/record/6342622) Swahili dataset within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
| DVoice Release | Val. CER | Val. WER | Test CER | Test WER |
|:-------------:|:---------------------------:| -----:| -----:| -----:|
| v2.0 | 8.83 | 22.78 | 9.46 | 23.16 |
# Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions.
- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset.
The obtained final acoustic representation is given to the CTC greedy decoder.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
# Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read the SpeechBrain tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
# Transcribing your own audio files (in Swahili)
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-swahili", savedir="pretrained_models/asr-wav2vec2-dvoice-sw")
asr_model.transcribe_file('./the_path_to_your_audio_file')
```
# Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
# Training
To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
# Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# About DVoice
DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social medias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke.
For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together.
# About AIOX Labs
Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
- He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience.
- AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
- Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
- A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.
Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
# SI2M Laboratory
The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling.
Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique)
# About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
# Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
# Acknowledgements
This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution. |
Kashni/damontvd | 1596c55f02d93be66caff56d552d8edb094f3e1a | 2022-05-26T11:43:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Kashni | null | Kashni/damontvd | 1 | null | transformers | 32,448 | ---
tags:
- conversation
---
#Damon from TVD |
forcorpus/bert-base-uncased-finetune-security | 78f09fc513e9bc8b6104077438e0ba7e41922c5f | 2022-05-26T11:41:39.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | forcorpus | null | forcorpus/bert-base-uncased-finetune-security | 1 | null | transformers | 32,449 | Entry not found |
theojolliffe/bart-large-cnn-pubmed1o3 | c39846503f67948f0ee42798ba7d017ab0e0d485 | 2022-05-27T13:19:47.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-large-cnn-pubmed1o3 | 1 | null | transformers | 32,450 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 36.7566
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9359
- Rouge1: 36.7566
- Rouge2: 14.813
- Rougel: 22.4693
- Rougelsum: 33.4325
- Gen Len: 138.7332
## 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: 2
- eval_batch_size: 2
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 2.028 | 1.0 | 19988 | 1.9359 | 36.7566 | 14.813 | 22.4693 | 33.4325 | 138.7332 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Aiyshwariya/bert-finetuned-squad | 2a7c6f30759cfb0a02b09b9b09190a3555b16d19 | 2022-05-26T20:12:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Aiyshwariya | null | Aiyshwariya/bert-finetuned-squad | 1 | null | transformers | 32,451 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
prodm93/GPT2Dynamic_text_model_v1 | ccf843e0d8573d73d848f3c1c0d1972b55369117 | 2022-05-26T19:00:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | prodm93 | null | prodm93/GPT2Dynamic_text_model_v1 | 1 | null | transformers | 32,452 | Entry not found |
SherryLiu/inst0075model | 63160908e9bbcb2263d5dc68bedbc7dcbcf8dad5 | 2022-05-26T22:45:30.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | fill-mask | false | SherryLiu | null | SherryLiu/inst0075model | 1 | null | transformers | 32,453 | ---
license: afl-3.0
---
|
coreybrady/coreyresults | 9a7b3d6affae0c5472558f6648c496c8a2e36984 | 2022-05-27T19:38:53.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | coreybrady | null | coreybrady/coreyresults | 1 | null | transformers | 32,454 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: coreyresults
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. -->
# coreyresults
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
olpa/xlm-roberta-base-finetuned-panx-de | 287eee13d894eaa24730f6b9b7223e2e0f881752 | 2022-05-30T03:26:44.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | olpa | null | olpa/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 32,455 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8627004891366169
---
<!-- 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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8627
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
geomos/distilbert-base-uncased-finetuned-imdb | 74dbfd0d2074cd22bbd2a93a513f853732873230 | 2022-05-27T04:40:19.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | geomos | null | geomos/distilbert-base-uncased-finetuned-imdb | 1 | null | transformers | 32,456 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2424
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4921 | 1.0 | 479 | 2.3047 |
| 2.3893 | 2.0 | 958 | 2.2607 |
| 2.3571 | 3.0 | 1437 | 2.2481 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 2.2.2
- Tokenizers 0.10.3
|
PSW/samsum_reverse_train | ab36aedc32756d7e4a64128d1dfff6c67bdd323b | 2022-05-31T07:08:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_reverse_train | 1 | null | transformers | 32,457 | Entry not found |
KoichiYasuoka/deberta-large-japanese-upos | 1e70ccea11d3c1dd6121ddca5f82ef8b6cf9a530 | 2022-05-27T06:54:18.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/deberta-large-japanese-upos | 1 | null | transformers | 32,458 | ---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# deberta-large-japanese-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-japanese-upos")
s="国境の長いトンネルを抜けると雪国であった。"
t=tokenizer.tokenize(s)
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
print(list(zip(t,p)))
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-large-japanese-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
chrisvinsen/xlsr-wav2vec2-final-1-lm-1 | 251b3d8389a265061bbf0390ed35b2c383f7b7b3 | 2022-05-29T01:09:25.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/xlsr-wav2vec2-final-1-lm-1 | 1 | null | transformers | 32,459 | CommonVoice Dataset 8.0 --> Train + Test + Validation
WER : 0.216
WER with LM: 0.123 |
dkasti/xlm-roberta-base-finetuned-panx-de | 0d67a2c373cea2b544b847d87dfd6708009ee95e | 2022-06-02T00:32:38.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | dkasti | null | dkasti/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 32,460 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8615769427548178
---
<!-- 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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1401
- F1: 0.8616
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2605 | 1.0 | 525 | 0.1708 | 0.8198 |
| 0.1274 | 2.0 | 1050 | 0.1415 | 0.8449 |
| 0.0819 | 3.0 | 1575 | 0.1401 | 0.8616 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
deathmite/DiabloGPT-small-potaru | 3a5e33031abc4892b03303c7968ba79fae46f6d8 | 2022-05-27T09:01:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | deathmite | null | deathmite/DiabloGPT-small-potaru | 1 | null | transformers | 32,461 | ---
tags:
- conversational
---
# Potaru DiabloGPT model |
huggingtweets/eyeofjackiechan | d31b740b2938a8a9a266d1bffa0e83f37a2880c6 | 2022-06-20T01:04:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/eyeofjackiechan | 1 | null | transformers | 32,462 | ---
language: en
thumbnail: http://www.huggingtweets.com/eyeofjackiechan/1655687093014/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/644052743/logo_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jackie Chan</div>
<div style="text-align: center; font-size: 14px;">@eyeofjackiechan</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jackie Chan.
| Data | Jackie Chan |
| --- | --- |
| Tweets downloaded | 2411 |
| Retweets | 24 |
| Short tweets | 109 |
| Tweets kept | 2278 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/xs2o3djj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @eyeofjackiechan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jlgydkw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jlgydkw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/eyeofjackiechan')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3 | 9db53aea5f4e9bc0481107b13626da3afabde9dd | 2022-05-27T18:59:12.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3 | 1 | null | transformers | 32,463 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.4586
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8817
- Rouge1: 37.4586
- Rouge2: 15.5572
- Rougel: 23.0686
- Rougelsum: 34.1522
- Gen Len: 138.379
## 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: 2
- eval_batch_size: 2
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9586 | 1.0 | 19988 | 1.8817 | 37.4586 | 15.5572 | 23.0686 | 34.1522 | 138.379 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Splend1dchan/wav2vec2-large-lv60_byt5-small_nofreeze_bs64 | 172b8d6d97548c2cb361976ce669bd3ffcff60d2 | 2022-05-31T15:00:45.000Z | [
"pytorch",
"speechmix",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/wav2vec2-large-lv60_byt5-small_nofreeze_bs64 | 1 | null | transformers | 32,464 | Entry not found |
nataliebhuerta/wav2vec2-base-finetuned-ks | 305eb89a86c15156ddd9800e7c59b0ac7a17ae10 | 2022-05-27T14:46:35.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:superb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | nataliebhuerta | null | nataliebhuerta/wav2vec2-base-finetuned-ks | 1 | null | transformers | 32,465 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
model-index:
- name: wav2vec2-base-finetuned-ks
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-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.14.0
- Tokenizers 0.10.3
|
shafin/distilbert-base-uncased-finetuned-cust | 849e6b0f6ea96fdde2b58a4b002628fb95b30ef4 | 2022-05-27T16:42:07.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | shafin | null | shafin/distilbert-base-uncased-finetuned-cust | 1 | null | transformers | 32,466 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-cust
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-cust
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: 2.0735
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4249 | 1.0 | 625 | 2.2071 |
| 2.2697 | 2.0 | 1250 | 2.1411 |
| 2.2092 | 3.0 | 1875 | 2.1255 |
| 2.1674 | 4.0 | 2500 | 2.0682 |
| 2.1499 | 5.0 | 3125 | 2.0667 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Satyamatury/wav2vec2-large-xls-r-300m-hindi-colab | 8a1de2618cda9b25c6eab02968a5715e5842f436 | 2022-06-13T11:08:04.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Satyamatury | null | Satyamatury/wav2vec2-large-xls-r-300m-hindi-colab | 1 | null | transformers | 32,467 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-colab
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-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7529
- Wer: 0.9130
## 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: 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: 500
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2923 | 44.42 | 400 | 1.7529 | 0.9130 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Santiagot1105/wav2vec2-l-xlsr-es-col-pro-noise | fccf80847d0f48ad595cd5cbbb000708a54a057f | 2022-05-30T06:08:39.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Santiagot1105 | null | Santiagot1105/wav2vec2-l-xlsr-es-col-pro-noise | 1 | null | transformers | 32,468 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-l-xlsr-es-col-pro-noise
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-l-xlsr-es-col-pro-noise
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0677
- Wer: 0.0380
## 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: 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.94 | 1.21 | 400 | 0.0800 | 0.0814 |
| 0.4711 | 2.42 | 800 | 0.0730 | 0.0692 |
| 0.3451 | 3.62 | 1200 | 0.0729 | 0.0669 |
| 0.2958 | 4.83 | 1600 | 0.0796 | 0.0667 |
| 0.2544 | 6.04 | 2000 | 0.0808 | 0.0584 |
| 0.227 | 7.25 | 2400 | 0.0791 | 0.0643 |
| 0.2061 | 8.46 | 2800 | 0.0718 | 0.0582 |
| 0.1901 | 9.67 | 3200 | 0.0709 | 0.0587 |
| 0.179 | 10.87 | 3600 | 0.0698 | 0.0558 |
| 0.1693 | 12.08 | 4000 | 0.0709 | 0.0530 |
| 0.1621 | 13.29 | 4400 | 0.0640 | 0.0487 |
| 0.1443 | 14.5 | 4800 | 0.0793 | 0.0587 |
| 0.1408 | 15.71 | 5200 | 0.0741 | 0.0528 |
| 0.1377 | 16.92 | 5600 | 0.0702 | 0.0462 |
| 0.1292 | 18.13 | 6000 | 0.0822 | 0.0539 |
| 0.1197 | 19.33 | 6400 | 0.0625 | 0.0436 |
| 0.1137 | 20.54 | 6800 | 0.0650 | 0.0419 |
| 0.1017 | 21.75 | 7200 | 0.0630 | 0.0392 |
| 0.0976 | 22.96 | 7600 | 0.0630 | 0.0387 |
| 0.0942 | 24.17 | 8000 | 0.0631 | 0.0380 |
| 0.0924 | 25.38 | 8400 | 0.0645 | 0.0374 |
| 0.0862 | 26.59 | 8800 | 0.0677 | 0.0402 |
| 0.0831 | 27.79 | 9200 | 0.0680 | 0.0393 |
| 0.077 | 29.0 | 9600 | 0.0677 | 0.0380 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
sanbohork/Caso3_T5 | 359fe741dc8872759179c2400f2153b146fede0b | 2022-05-28T13:35:02.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:other",
"autotrain_compatible"
] | text2text-generation | false | sanbohork | null | sanbohork/Caso3_T5 | 1 | null | transformers | 32,469 | ---
license: other
---
Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-for-text2text-and-building-a-demo-with-streamlit-c72009631887
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto |
ElMuchoDingDong/DialoGPT-medium-AudreyHepburn_v4 | 7113d4a433c6fc57c8f7c37d4fd1aa6f85d56736 | 2022-05-27T20:27:21.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ElMuchoDingDong | null | ElMuchoDingDong/DialoGPT-medium-AudreyHepburn_v4 | 1 | null | transformers | 32,470 | ---
tags:
- conversational
---
#Audrey Hepburn DialoGPT Model |
eugenecamus/resnet-50-base-beans-demo | 0fbcde2fc6b720e18e4a94f5d6ccf7bccea41b0f | 2022-05-31T17:47:56.000Z | [
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"dataset:beans",
"transformers",
"vision",
"generated_from_trainer",
"model-index"
] | image-classification | false | eugenecamus | null | eugenecamus/resnet-50-base-beans-demo | 1 | null | transformers | 32,471 | ---
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: resnet-50-base-beans-demo
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9022556390977443
---
<!-- 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. -->
# resnet-50-base-beans-demo
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2188
- Accuracy: 0.9023
## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5679 | 1.0 | 130 | 0.2188 | 0.9023 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
jplu/adel-dbpedia-retrieval | 89b7e1e6d35306d21678b7cb794885b8fa0ff21a | 2022-05-27T21:58:29.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | jplu | null | jplu/adel-dbpedia-retrieval | 1 | null | sentence-transformers | 32,472 | ---
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, mean 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**:
`torch.utils.data.dataloader.DataLoader` of length 71 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`beir.losses.margin_mse_loss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 11,
"evaluation_steps": 10000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 --> |
Julietheg/checkpoint-1000 | a55afa8f11ffc26d533886b8d6fb8cdd2bff8600 | 2022-05-28T00:57:02.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Julietheg | null | Julietheg/checkpoint-1000 | 1 | null | transformers | 32,473 | ---
tags:
- generated_from_keras_callback
model-index:
- name: checkpoint-1000
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# checkpoint-1000
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
edharepe/T5_generacion_titulos | b4578843e0d1dbd99f5690f6b8251e6daf1f5a00 | 2022-05-28T05:32:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | edharepe | null | edharepe/T5_generacion_titulos | 1 | null | transformers | 32,474 | Este modelo ha sido creado a partir de T5 Fine tuning with PyTorch.ipynb de Shivanand Roy y entrenado con un dataset de noticias de un diario uruguayo, en el repositorio se encuentra todos los archivos resultante del procesos de entrenamiento |
aiface/test285 | d84e1b8d1caeddcc2e6747b87ff27e96096ce3a5 | 2022-05-28T08:57:56.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:vivos_dataset",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | aiface | null | aiface/test285 | 1 | null | transformers | 32,475 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- vivos_dataset
model-index:
- name: test285
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. -->
# test285
This model is a fine-tuned version of [aiface/cv8](https://huggingface.co/aiface/cv8) on the vivos_dataset dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3865
- eval_wer: 0.3012
- eval_runtime: 39.5722
- eval_samples_per_second: 19.205
- eval_steps_per_second: 2.401
- epoch: 1.1
- step: 400
## 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: 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
KoichiYasuoka/deberta-small-coptic | 3e8086b8f36eea9409631f544972068e6f05b0a3 | 2022-05-28T08:48:57.000Z | [
"pytorch",
"deberta-v2",
"fill-mask",
"cop",
"transformers",
"coptic",
"masked-lm",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | KoichiYasuoka | null | KoichiYasuoka/deberta-small-coptic | 1 | null | transformers | 32,476 | ---
language:
- "cop"
tags:
- "coptic"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# deberta-small-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-small-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-small-coptic-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-small-coptic")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-small-coptic")
```
|
KoichiYasuoka/deberta-small-coptic-upos | 2517d835b70ad887bd29b59fbed4813fe45aedc9 | 2022-05-28T09:15:07.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"cop",
"dataset:universal_dependencies",
"transformers",
"coptic",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/deberta-small-coptic-upos | 1 | null | transformers | 32,477 | ---
language:
- "cop"
tags:
- "coptic"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·"
- text: "ⲙⲟⲟϣⲉϩⲱⲥϣⲏⲣⲉⲙ̄ⲡⲟⲩⲟⲉⲓⲛ·"
---
# deberta-small-coptic-upos
## Model Description
This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-small-coptic](https://huggingface.co/KoichiYasuoka/deberta-small-coptic). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-small-coptic-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-small-coptic-upos")
```
or
```
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-small-coptic-upos")
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3 | 38b237719a0a09c73ece656a4573718c10a03ef8 | 2022-05-28T14:46:08.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3 | 1 | null | transformers | 32,478 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.5622
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8540
- Rouge1: 37.5622
- Rouge2: 15.5848
- Rougel: 23.1384
- Rougelsum: 34.2695
- Gen Len: 138.0326
## 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: 2
- eval_batch_size: 2
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.9205 | 1.0 | 19987 | 1.8540 | 37.5622 | 15.5848 | 23.1384 | 34.2695 | 138.0326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
gary109/ai-light-dance_pretrain_wav2vec2-large-lv60 | 940ab8af063590ef66750ab60796e9b4ea7c40ee | 2022-06-10T17:53:36.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"transformers"
] | null | false | gary109 | null | gary109/ai-light-dance_pretrain_wav2vec2-large-lv60 | 1 | 1 | transformers | 32,479 | Entry not found |
autoevaluate/summarization | 3f3246b0f042523dc5580308c2ee61931903efa1 | 2022-05-28T13:18:28.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | autoevaluate | null | autoevaluate/summarization | 1 | null | transformers | 32,480 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 23.9405
---
<!-- 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. -->
# summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6690
- Rouge1: 23.9405
- Rouge2: 5.0879
- Rougel: 18.4981
- Rougelsum: 18.5032
- Gen Len: 18.7376
## 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
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9249 | 0.08 | 1000 | 2.6690 | 23.9405 | 5.0879 | 18.4981 | 18.5032 | 18.7376 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ruselkomp/deeppavlov-framebank-full-5epochs | af8687e68222f0eee34841d84d7046b19e045cd9 | 2022-05-29T16:05:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | ruselkomp | null | ruselkomp/deeppavlov-framebank-full-5epochs | 1 | null | transformers | 32,481 | ---
tags:
- generated_from_trainer
model-index:
- name: deeppavlov-framebank-full-5epochs
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. -->
# deeppavlov-framebank-full-5epochs
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4206
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0742 | 1.0 | 2827 | 1.0130 |
| 0.7934 | 2.0 | 5654 | 1.0363 |
| 0.5931 | 3.0 | 8481 | 1.1527 |
| 0.4166 | 4.0 | 11308 | 1.2754 |
| 0.3145 | 5.0 | 14135 | 1.4206 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
Jefferson/PruebaPLN | a37f0abc3d124ee6ba24e63f52758f7ba188ebce | 2022-05-28T13:16:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jefferson | null | Jefferson/PruebaPLN | 1 | null | transformers | 32,482 | Entry not found |
stevemobs/deberta-base-combined-squad1-aqa-and-newsqa | e9fce73b49bb64b53fc171def33eb7479408f967 | 2022-05-29T01:57:06.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-combined-squad1-aqa-and-newsqa | 1 | null | transformers | 32,483 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-combined-squad1-aqa-and-newsqa
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. -->
# deberta-base-combined-squad1-aqa-and-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7527
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6729 | 1.0 | 17307 | 0.7076 |
| 0.4631 | 2.0 | 34614 | 0.7527 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
JuanForeroNeme/ES_UC_MODELO_NPL_E3 | 081f1fe289c190d7a4e4d24f889be951628cb671 | 2022-05-28T16:05:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | JuanForeroNeme | null | JuanForeroNeme/ES_UC_MODELO_NPL_E3 | 1 | null | transformers | 32,484 | Entry not found |
ahGadji/dummy-model | 49e9f898e6d4ecd4b8bfa123bbb410da2893e836 | 2022-05-28T16:56:37.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ahGadji | null | ahGadji/dummy-model | 1 | null | transformers | 32,485 | Entry not found |
JuanForeroNeme/ES_UC_MODELO_NPL_E3_V0 | f50d5a6b98daae04ab16787d2c1e7f1bce316abd | 2022-05-28T17:30:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | JuanForeroNeme | null | JuanForeroNeme/ES_UC_MODELO_NPL_E3_V0 | 1 | null | transformers | 32,486 | Entry not found |
Anjoe/german-poetry-bert | 2bf96e8111b4196389786717014b5044adbe4daf | 2022-07-21T14:27:42.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | Anjoe | null | Anjoe/german-poetry-bert | 1 | null | transformers | 32,487 | ---
license: mit
---
|
subhasisj/squad-qa-minilmv2-XLMTokeinizer-8 | 4b784a0439916cc4eaed836baa0ed06d534bbcce | 2022-05-28T19:40:23.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | subhasisj | null | subhasisj/squad-qa-minilmv2-XLMTokeinizer-8 | 1 | null | transformers | 32,488 | Entry not found |
stevemobs/deberta-base-finetuned-squad1-aqa-newsqa | 5bb9645f0bf1eaa3d0856459a9e18213f5446930 | 2022-05-29T00:44:00.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-finetuned-squad1-aqa-newsqa | 1 | null | transformers | 32,489 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-finetuned-squad1-aqa-newsqa
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. -->
# deberta-base-finetuned-squad1-aqa-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1-aqa](https://huggingface.co/stevemobs/deberta-base-finetuned-squad1-aqa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7525
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.659 | 1.0 | 17307 | 0.7169 |
| 0.4718 | 2.0 | 34614 | 0.7525 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
stevemobs/deberta-base-newsqa | 65199077bad3f015c46e2d91396aae696d75fc0d | 2022-05-29T10:41:08.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-newsqa | 1 | null | transformers | 32,490 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-newsqa
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. -->
# deberta-base-newsqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7628
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6847 | 1.0 | 17307 | 0.7396 |
| 0.4916 | 2.0 | 34614 | 0.7628 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln47 | 2086d9d0dd740fcd30120a986c49f226984625e8 | 2022-05-29T01:56:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln47 | 1 | null | transformers | 32,491 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: pragmatic
1800s english: rational, strategic, judicious, reasonable, circumspect, commonsensical, calculating, cool-headed, intentional, far-sighted
***
input: not loyal
1800s english: two-faced, inimical, perfidious, duplicitous, mendacious, double-dealing, shifty
***
input:``` |
stevemobs/deberta-base-finetuned-aqa-squad1-newsqa | 54df3d93d80d70eb75908d7a4cae0a1d4cfc607b | 2022-05-29T09:16:31.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-finetuned-aqa-squad1-newsqa | 1 | null | transformers | 32,492 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-finetuned-aqa-squad1-newsqa
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. -->
# deberta-base-finetuned-aqa-squad1-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-aqa-squad1](https://huggingface.co/stevemobs/deberta-base-finetuned-aqa-squad1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7523
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.681 | 1.0 | 17307 | 0.7207 |
| 0.4682 | 2.0 | 34614 | 0.7523 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln48 | bba4dee290d27de3b776b0a816047c8d0d06783a | 2022-05-30T19:19:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln48 | 1 | null | transformers | 32,493 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
``` |
Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs64_forST.cy.en | eaec2a5f72f202bab41765706eeb61640efeb2af | 2022-05-29T08:01:29.000Z | [
"pytorch",
"speechmix",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs64_forST.cy.en | 1 | null | transformers | 32,494 | Entry not found |
chrisvinsen/wav2vec2-13 | 744807846e6ebc1c28cbaf092a31ef30cd4e947d | 2022-05-29T09:11:01.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/wav2vec2-13 | 1 | null | transformers | 32,495 | Entry not found |
Flem/DialoGPT-medium-alastor | 3e8552b5e82d4437adbbb2cf77021b4839b1c5ea | 2022-05-29T09:45:48.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Flem | null | Flem/DialoGPT-medium-alastor | 1 | null | transformers | 32,496 | ---
tags:
- conversational
---
# Alastor The Radio Demon Demon DialoGPT Model |
Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs16_forMINDS | 69dfe37ae1cafd862fd3b53c042ec7624637eb03 | 2022-05-29T12:08:39.000Z | [
"pytorch",
"speechmix",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/wav2vec2-large-lv60_t5lephone-small_nofreeze_bs16_forMINDS | 1 | null | transformers | 32,497 | Entry not found |
MeshalAlamr/wav2vec2-xls-r-300m-ar-11 | 7dd8365d45514aed3581c4ae5473e568ad16fed9 | 2022-05-30T02:26:00.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | MeshalAlamr | null | MeshalAlamr/wav2vec2-xls-r-300m-ar-11 | 1 | null | transformers | 32,498 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ar-11
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-xls-r-300m-ar-11
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 60.5659
- Wer: 0.2144
## 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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 16849.2529 | 1.0 | 85 | 1458.4645 | 1.0 |
| 4474.9085 | 2.0 | 170 | 687.2793 | 1.0 |
| 2937.0309 | 3.0 | 255 | 632.0456 | 1.0 |
| 2853.7682 | 4.0 | 340 | 621.7872 | 1.0 |
| 2786.243 | 5.0 | 425 | 611.4717 | 1.0 |
| 2738.1844 | 6.0 | 510 | 578.0577 | 1.0 |
| 2118.4608 | 7.0 | 595 | 253.0534 | 0.9927 |
| 1026.4239 | 8.0 | 680 | 140.3523 | 0.6430 |
| 682.4369 | 9.0 | 765 | 106.5226 | 0.4990 |
| 516.4381 | 10.0 | 850 | 85.3184 | 0.4126 |
| 434.9369 | 11.0 | 935 | 79.4750 | 0.3683 |
| 369.3786 | 12.0 | 1020 | 73.2318 | 0.3290 |
| 324.2687 | 13.0 | 1105 | 69.6444 | 0.3160 |
| 292.8527 | 14.0 | 1190 | 66.7714 | 0.2922 |
| 266.229 | 15.0 | 1275 | 68.2237 | 0.2839 |
| 242.3606 | 16.0 | 1360 | 66.0233 | 0.2745 |
| 227.9846 | 17.0 | 1445 | 66.8503 | 0.2668 |
| 210.1087 | 18.0 | 1530 | 63.1035 | 0.2539 |
| 201.326 | 19.0 | 1615 | 63.9665 | 0.2481 |
| 189.019 | 20.0 | 1700 | 60.9628 | 0.2418 |
| 181.3091 | 21.0 | 1785 | 62.5716 | 0.2387 |
| 168.631 | 22.0 | 1870 | 62.4718 | 0.2342 |
| 165.8396 | 23.0 | 1955 | 61.0784 | 0.2287 |
| 161.4992 | 24.0 | 2040 | 62.2299 | 0.2257 |
| 153.6809 | 25.0 | 2125 | 60.4889 | 0.2235 |
| 145.4282 | 26.0 | 2210 | 60.8189 | 0.2208 |
| 144.6855 | 27.0 | 2295 | 61.8122 | 0.2203 |
| 138.6269 | 28.0 | 2380 | 60.4600 | 0.2172 |
| 137.6246 | 29.0 | 2465 | 61.4417 | 0.2167 |
| 134.6211 | 30.0 | 2550 | 60.5659 | 0.2144 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
dexay/Ner1HgF | 29795dd335762ff873f4d394a8ad70d628af4688 | 2022-05-30T16:48:24.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dexay | null | dexay/Ner1HgF | 1 | null | transformers | 32,499 | Entry not found |
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