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abhinav-kumar-thakur/distilbert-base-uncased-finetuned-mrpc | 44523229fd50fb09c92c556a6ebaf3faa4b96654 | 2022-07-01T11:01:01.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | abhinav-kumar-thakur | null | abhinav-kumar-thakur/distilbert-base-uncased-finetuned-mrpc | 5 | null | transformers | 17,500 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8578431372549019
- name: F1
type: f1
value: 0.9006849315068494
---
<!-- 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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5556
- Accuracy: 0.8578
- F1: 0.9007
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3937 | 0.8113 | 0.8670 |
| No log | 2.0 | 460 | 0.3660 | 0.8480 | 0.8967 |
| 0.4387 | 3.0 | 690 | 0.4298 | 0.8529 | 0.8973 |
| 0.4387 | 4.0 | 920 | 0.5573 | 0.8529 | 0.8990 |
| 0.1832 | 5.0 | 1150 | 0.5556 | 0.8578 | 0.9007 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mousaazari/t5-test2sql | fed74e13719275d4315b8a298133a0b6286bc771 | 2022-07-01T12:14:46.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | mousaazari | null | mousaazari/t5-test2sql | 5 | null | transformers | 17,501 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-test2sql
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-test2sql
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Rouge2 Precision: 0.9214
- Rouge2 Recall: 0.4259
- Rouge2 Fmeasure: 0.5578
## 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: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 11 | 2.7293 | 0.1012 | 0.0305 | 0.0453 |
| No log | 2.0 | 22 | 1.9009 | 0.0937 | 0.0292 | 0.0427 |
| No log | 3.0 | 33 | 1.3525 | 0.1002 | 0.0349 | 0.0502 |
| No log | 4.0 | 44 | 0.8837 | 0.1462 | 0.0529 | 0.0744 |
| No log | 5.0 | 55 | 0.6460 | 0.5546 | 0.2531 | 0.3371 |
| No log | 6.0 | 66 | 0.5050 | 0.729 | 0.3571 | 0.4631 |
| No log | 7.0 | 77 | 0.4239 | 0.6944 | 0.3048 | 0.4088 |
| No log | 8.0 | 88 | 0.3799 | 0.7868 | 0.3674 | 0.4807 |
| No log | 9.0 | 99 | 0.3405 | 0.7266 | 0.3126 | 0.4213 |
| No log | 10.0 | 110 | 0.3055 | 0.8447 | 0.3876 | 0.5104 |
| No log | 11.0 | 121 | 0.2741 | 0.8546 | 0.3955 | 0.5201 |
| No log | 12.0 | 132 | 0.2605 | 0.8676 | 0.4049 | 0.5308 |
| No log | 13.0 | 143 | 0.2446 | 0.8424 | 0.3814 | 0.5047 |
| No log | 14.0 | 154 | 0.2287 | 0.8659 | 0.3945 | 0.5238 |
| No log | 15.0 | 165 | 0.2209 | 0.9064 | 0.4273 | 0.556 |
| No log | 16.0 | 176 | 0.1990 | 0.888 | 0.409 | 0.5383 |
| No log | 17.0 | 187 | 0.1941 | 0.9118 | 0.4305 | 0.5602 |
| No log | 18.0 | 198 | 0.1785 | 0.9118 | 0.4305 | 0.5602 |
| No log | 19.0 | 209 | 0.1669 | 0.919 | 0.4324 | 0.5636 |
| No log | 20.0 | 220 | 0.1749 | 0.9138 | 0.4289 | 0.5608 |
| No log | 21.0 | 231 | 0.1598 | 0.9047 | 0.4248 | 0.556 |
| No log | 22.0 | 242 | 0.1501 | 0.9098 | 0.4294 | 0.5596 |
| No log | 23.0 | 253 | 0.1456 | 0.9138 | 0.4307 | 0.5618 |
| No log | 24.0 | 264 | 0.1419 | 0.893 | 0.4185 | 0.5467 |
| No log | 25.0 | 275 | 0.1359 | 0.9005 | 0.4212 | 0.55 |
| No log | 26.0 | 286 | 0.1338 | 0.8979 | 0.4212 | 0.5494 |
| No log | 27.0 | 297 | 0.1319 | 0.9005 | 0.4212 | 0.55 |
| No log | 28.0 | 308 | 0.1325 | 0.9005 | 0.4212 | 0.55 |
| No log | 29.0 | 319 | 0.1335 | 0.9093 | 0.4231 | 0.5529 |
| No log | 30.0 | 330 | 0.1240 | 0.9093 | 0.4231 | 0.5529 |
| No log | 31.0 | 341 | 0.1222 | 0.9053 | 0.4231 | 0.5527 |
| No log | 32.0 | 352 | 0.1265 | 0.9214 | 0.4259 | 0.5578 |
| No log | 33.0 | 363 | 0.1286 | 0.9214 | 0.4259 | 0.5578 |
| No log | 34.0 | 374 | 0.1283 | 0.9214 | 0.4259 | 0.5578 |
| No log | 35.0 | 385 | 0.1279 | 0.9214 | 0.4259 | 0.5578 |
| No log | 36.0 | 396 | 0.1285 | 0.9214 | 0.4259 | 0.5578 |
| No log | 37.0 | 407 | 0.1291 | 0.9093 | 0.4231 | 0.5529 |
| No log | 38.0 | 418 | 0.1270 | 0.9093 | 0.4231 | 0.5529 |
| No log | 39.0 | 429 | 0.1225 | 0.9093 | 0.4231 | 0.5529 |
| No log | 40.0 | 440 | 0.1205 | 0.9093 | 0.4231 | 0.5529 |
| No log | 41.0 | 451 | 0.1210 | 0.9093 | 0.4231 | 0.5529 |
| No log | 42.0 | 462 | 0.1230 | 0.9093 | 0.4231 | 0.5529 |
| No log | 43.0 | 473 | 0.1250 | 0.9093 | 0.4231 | 0.5529 |
| No log | 44.0 | 484 | 0.1223 | 0.9214 | 0.4259 | 0.5578 |
| No log | 45.0 | 495 | 0.1226 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 46.0 | 506 | 0.1213 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 47.0 | 517 | 0.1205 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 48.0 | 528 | 0.1203 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 49.0 | 539 | 0.1206 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 50.0 | 550 | 0.1207 | 0.9214 | 0.4259 | 0.5578 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dminiotas05/distilbert-base-uncased-finetuned-ft500_4 | 7e7259f8aea9fad06cc63707b0997a8ccff3ccf8 | 2022-07-01T12:20:28.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | dminiotas05 | null | dminiotas05/distilbert-base-uncased-finetuned-ft500_4 | 5 | null | transformers | 17,502 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft500_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft500_4
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: 1.1118
- Accuracy: 0.4807
- F1: 0.4638
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1931 | 1.0 | 188 | 1.1525 | 0.4513 | 0.4333 |
| 1.0982 | 2.0 | 376 | 1.1118 | 0.4807 | 0.4638 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
annS/roberta-base-prop-16-train-set | 270012863afe003498416bc22bce9a437f857050 | 2022-07-01T18:39:43.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | annS | null | annS/roberta-base-prop-16-train-set | 5 | null | transformers | 17,503 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-prop-16-train-set
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown 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: 5e-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: 3.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
clevrly/xlnet-base-cased-finetuned-hotpot_qa | 218644e4c5a4763b8689d1d15948ef09fb5b7a53 | 2022-07-01T19:47:44.000Z | [
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | clevrly | null | clevrly/xlnet-base-cased-finetuned-hotpot_qa | 5 | null | transformers | 17,504 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlnet-base-cased-finetuned-hotpot_qa
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. -->
# xlnet-base-cased-finetuned-hotpot_qa
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9574
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.027 | 1.0 | 923 | 1.0340 |
| 0.8758 | 2.0 | 1846 | 0.9574 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Sayan01/tiny-bert-mnli-mm-distilled | f732dc7647df1a12b1000d99d3c423973c463664 | 2022-07-02T14:44:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Sayan01 | null | Sayan01/tiny-bert-mnli-mm-distilled | 5 | null | transformers | 17,505 | Entry not found |
ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-512-5 | b4f3ab8b665e5a157924b18ff9db4e6ba95a438a | 2022-07-04T10:03:46.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-512-5 | 5 | null | transformers | 17,506 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-512-5 | d59d91fe027b6eead72242629cb7ba32b1688aff | 2022-07-04T10:03:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-512-5 | 5 | null | transformers | 17,507 | Entry not found |
ghadeermobasher/BioRed-Dis-Original-PubMedBERT-512-5 | 69ffb1e67ec2c1eda2555c24b65be70d7f72d0a7 | 2022-07-04T10:13:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Original-PubMedBERT-512-5 | 5 | null | transformers | 17,508 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512-5 | 911d46f039c13d24edc6112f7e1447ca13cf62e9 | 2022-07-04T10:15:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512-5 | 5 | null | transformers | 17,509 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-256-5 | a8de116163b3185b342ae31ea08f916ec1d01cfe | 2022-07-04T10:10:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-256-5 | 5 | null | transformers | 17,510 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-256-13 | 156e568bcb1483ec5601096be68dadcc906377a8 | 2022-07-04T10:34:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-256-13 | 5 | null | transformers | 17,511 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-5 | 4ffd7cb689b23e2da81671ac611debb11b187aaa | 2022-07-04T10:27:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-5 | 5 | null | transformers | 17,512 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-13 | 825a084193f7b331fc8f07f77816580fcec71fd6 | 2022-07-04T10:41:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-256-13 | 5 | null | transformers | 17,513 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-384-8 | 8b374099692d9d87cecff6562556b21a31cf6d81 | 2022-07-04T11:26:19.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-384-8 | 5 | null | transformers | 17,514 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-384-8 | 46111ed1e6d7df2b71995becf542e02cd0d53af2 | 2022-07-04T11:29:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-384-8 | 5 | null | transformers | 17,515 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-384-5 | f658cd47364ee34a2ce6e9ed22600d62132dde6f | 2022-07-04T11:54:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-384-5 | 5 | null | transformers | 17,516 | Entry not found |
ghadeermobasher/BioRed-Dis-Original-PubMedBERT-384-5 | 79610d1fd8a13db1d4c12d305b6c4957e6ba29c2 | 2022-07-04T11:54:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Original-PubMedBERT-384-5 | 5 | null | transformers | 17,517 | Entry not found |
ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-384-5 | ea3dd0f14b4b86d80ac1927475bb2de03816a3ff | 2022-07-04T11:55:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-384-5 | 5 | null | transformers | 17,518 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-384-5 | 136fb6d27098dd59b20eaa37941d7ccf90bb86ef | 2022-07-04T11:55:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-384-5 | 5 | null | transformers | 17,519 | Entry not found |
kuttersn/dailydialog-distilgpt2 | 49300501f8c8c10454a85804deed2ff0e8aa6082 | 2022-07-04T11:38:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | kuttersn | null | kuttersn/dailydialog-distilgpt2 | 5 | null | transformers | 17,520 | Entry not found |
ghadeermobasher/BioRed-Dis-Original-PubMedBERT-320-8 | 854fd433cd51df830450aca1304e46f371fd2464 | 2022-07-04T13:17:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Original-PubMedBERT-320-8 | 5 | null | transformers | 17,521 | Entry not found |
ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-128-32 | a92adbb9bb6be99a0365319bcefd167f440ff765 | 2022-07-04T13:31:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-128-32 | 5 | null | transformers | 17,522 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-20 | cfcb54b1ccfb4af89a1ef45ac140f08c539869b9 | 2022-07-04T14:46:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-20 | 5 | null | transformers | 17,523 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-128-20 | 1839355dcc5737893963efcbef4717d0c6bb1626 | 2022-07-04T14:34:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-128-20 | 5 | null | transformers | 17,524 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-5 | 39fb542b513712578ff6e8c3fc5576788ca28659 | 2022-07-04T14:35:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-128-5 | 5 | null | transformers | 17,525 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-128-5 | 26627c4c458bd5d76ba87fcb2431d2da01b971e0 | 2022-07-04T14:35:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-128-5 | 5 | null | transformers | 17,526 | Entry not found |
ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-320-8-10 | d75a027b907ccdee29c6d9fcf1b25058b8d7f9bf | 2022-07-04T16:52:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-320-8-10 | 5 | null | transformers | 17,527 | Entry not found |
ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-384-8-10 | 2528094af2e7c7b1927d8d90e53d2c2528fafb0b | 2022-07-04T17:06:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Dis-Modified-PubMedBERT-384-8-10 | 5 | null | transformers | 17,528 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-320-8-10 | 962ab2f5e095c87e8801a510dd94920fd6d3c664 | 2022-07-04T16:54:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-320-8-10 | 5 | null | transformers | 17,529 | Entry not found |
romainlhardy/distilbart-cnn-12-6-booksum | a3c4f2eb62c93785dbe1b307e176cfb3989b11ae | 2022-07-05T01:12:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | romainlhardy | null | romainlhardy/distilbart-cnn-12-6-booksum | 5 | null | transformers | 17,530 | Entry not found |
Samlit/rare-puppers2 | b4d284b77c99f14accb9179da0ad411b070fea78 | 2022-07-05T06:14:13.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | Samlit | null | Samlit/rare-puppers2 | 5 | null | transformers | 17,531 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6222222447395325
---
# rare-puppers2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### La Goulue Toulouse-Lautrec
#### Marcelle Lender Bolero
#### aristide bruant Lautrec
#### la goulue Toulouse-Lautrec
 |
slabschonoren/bert-encoding-finetuned-try1 | c5a65ef523f0f7f8446666db5dd99931d19710ab | 2022-07-05T12:49:32.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | slabschonoren | null | slabschonoren/bert-encoding-finetuned-try1 | 5 | null | transformers | 17,532 | Entry not found |
Aktsvigun/bart-base_xsum_4837 | b705fb397d95571e12c0691d22d231d2a2ae1ecf | 2022-07-07T14:36:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_xsum_4837 | 5 | null | transformers | 17,533 | Entry not found |
Eleven/xlm-roberta-base-finetuned-panx-all | 0f8a61a27045a28522b66a8ba8ce9996a0773686 | 2022-07-05T17:33:02.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Eleven | null | Eleven/xlm-roberta-base-finetuned-panx-all | 5 | null | transformers | 17,534 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- F1: 0.8557
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3 | 1.0 | 835 | 0.1862 | 0.8114 |
| 0.1552 | 2.0 | 1670 | 0.1758 | 0.8426 |
| 0.1002 | 3.0 | 2505 | 0.1752 | 0.8557 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/fpdm_roberta_pert_sent_0.01_squad2.0 | e15b33c45304772ee997ce69e1c32394d2187792 | 2022-07-06T01:08:35.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/fpdm_roberta_pert_sent_0.01_squad2.0 | 5 | null | transformers | 17,535 | Entry not found |
nawta/wav2vec2-wtimit-finetune | 6022b639fca77e5dcf4a1846232cd4435b98481b | 2022-07-06T16:07:23.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | nawta | null | nawta/wav2vec2-wtimit-finetune | 5 | null | transformers | 17,536 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-wtimit-finetune
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-wtimit-finetune
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: 0.0383
- Wer: 0.0160
## 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: 64
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3743 | 2.82 | 500 | 2.9567 | 1.0 |
| 1.866 | 5.65 | 1000 | 0.2856 | 0.2580 |
| 0.2005 | 8.47 | 1500 | 0.0979 | 0.0669 |
| 0.08 | 11.3 | 2000 | 0.0617 | 0.0325 |
| 0.0497 | 14.12 | 2500 | 0.0578 | 0.0284 |
| 0.0348 | 16.95 | 3000 | 0.0557 | 0.0239 |
| 0.0269 | 19.77 | 3500 | 0.0447 | 0.0212 |
| 0.0198 | 22.6 | 4000 | 0.0437 | 0.0177 |
| 0.016 | 25.42 | 4500 | 0.0407 | 0.0164 |
| 0.014 | 28.25 | 5000 | 0.0383 | 0.0160 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
lepowl01/dummy-model | 3cfc3de60e760a66edd90b927b8abe346e3affa7 | 2022-07-06T13:31:09.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lepowl01 | null | lepowl01/dummy-model | 5 | null | transformers | 17,537 | Entry not found |
Evelyn18/distilbert-base-uncased-becasv2-3 | 329f1cdaa661c5f75acb0270e8be3bd88630bf6a | 2022-07-07T04:00:45.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becasv2-3 | 5 | null | transformers | 17,538 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becasv2-3
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-becasv2-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1218
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 4.6377 |
| No log | 2.0 | 18 | 3.8511 |
| No log | 3.0 | 27 | 3.3758 |
| No log | 4.0 | 36 | 3.1910 |
| No log | 5.0 | 45 | 3.1187 |
| No log | 6.0 | 54 | 3.1009 |
| No log | 7.0 | 63 | 3.1131 |
| No log | 8.0 | 72 | 3.1218 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-becasv2-4 | f089fe1e99ca89f7782340c16eb4d45574ee50c9 | 2022-07-07T04:16:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becasv2-4 | 5 | null | transformers | 17,539 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becasv2-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becasv2-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4637
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 5.3677 |
| No log | 2.0 | 12 | 4.6741 |
| No log | 3.0 | 18 | 4.2978 |
| No log | 4.0 | 24 | 3.9963 |
| No log | 5.0 | 30 | 3.7544 |
| No log | 6.0 | 36 | 3.5810 |
| No log | 7.0 | 42 | 3.4932 |
| No log | 8.0 | 48 | 3.4637 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-becasv2-6 | d39a721ab67453b8c6bf1f229534d5fec1fce4aa | 2022-07-07T04:44:16.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becasv2-6 | 5 | null | transformers | 17,540 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becasv2-6
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-becasv2-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8936
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 4.0542 |
| No log | 2.0 | 18 | 3.0865 |
| No log | 3.0 | 27 | 2.8069 |
| No log | 4.0 | 36 | 3.3330 |
| No log | 5.0 | 45 | 3.4108 |
| No log | 6.0 | 54 | 3.5562 |
| No log | 7.0 | 63 | 3.8846 |
| No log | 8.0 | 72 | 3.8936 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ScarlettSun9/autotrain-ZuoZhuan-1100540141 | d963448817fd8ae4baa5bc14d3b1f2e05e283312 | 2022-07-07T07:08:04.000Z | [
"pytorch",
"roberta",
"token-classification",
"unk",
"dataset:ScarlettSun9/autotrain-data-ZuoZhuan",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | ScarlettSun9 | null | ScarlettSun9/autotrain-ZuoZhuan-1100540141 | 5 | null | transformers | 17,541 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ScarlettSun9/autotrain-data-ZuoZhuan
co2_eq_emissions: 8.343592303925112
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1100540141
- CO2 Emissions (in grams): 8.343592303925112
## Validation Metrics
- Loss: 0.38094884157180786
- Accuracy: 0.8795777325860159
- Precision: 0.8171375141922127
- Recall: 0.8417033571821684
- F1: 0.8292385373953709
## 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/ScarlettSun9/autotrain-ZuoZhuan-1100540141
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
tanapatentlm/patentdeberta_large_spec_128_pwi | 83d060b1258d6e2ffc696ed0d48b5c3c66c99651 | 2022-07-13T22:13:56.000Z | [
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tanapatentlm | null | tanapatentlm/patentdeberta_large_spec_128_pwi | 5 | null | transformers | 17,542 | Entry not found |
huggingtweets/mcconaughey | cbc3263f2edb6bc22194784941ddb827a36cb0f0 | 2022-07-07T19:10:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/mcconaughey | 5 | null | transformers | 17,543 | ---
language: en
thumbnail: http://www.huggingtweets.com/mcconaughey/1657221054082/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/1191381171164237824/jdS95Rtm_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">Matthew McConaughey</div>
<div style="text-align: center; font-size: 14px;">@mcconaughey</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 Matthew McConaughey.
| Data | Matthew McConaughey |
| --- | --- |
| Tweets downloaded | 2519 |
| Retweets | 595 |
| Short tweets | 264 |
| Tweets kept | 1660 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cksy9wk/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 @mcconaughey's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hgi91kg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hgi91kg/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/mcconaughey')
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)
|
sdotmac/SimeBot | 5615f0c350dc318a49242d68042f00b49a9c60e6 | 2022-07-08T05:38:42.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:osl-3.0"
] | text-generation | false | sdotmac | null | sdotmac/SimeBot | 5 | null | transformers | 17,544 | ---
license: osl-3.0
---
|
swtx/ernie-gram-chinese | cd16040bb41feee1999da8c5302ea38934cc0589 | 2022-07-08T09:44:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"chinese",
"arxiv:2010.12148",
"transformers"
] | feature-extraction | false | swtx | null | swtx/ernie-gram-chinese | 5 | null | transformers | 17,545 | ---
language: chinese
---
# ERNIE-Gram-chinese
## Introduction
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
More detail: https://arxiv.org/abs/2010.12148
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-gram-chinese| Chinese |Layer:12, Hidden:768, Heads:12|
This released Pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("swtx/ernie-gram-chinese")
model = AutoModel.from_pretrained("swtx/ernie-gram-chinese")
``` |
jonatasgrosman/exp_w2v2t_th_wav2vec2_s664 | d4c920202f4fcdc7ceb4e3fc4a6ffc1d874c2ac8 | 2022-07-08T10:06:53.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_th_wav2vec2_s664 | 5 | null | transformers | 17,546 | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wav2vec2_s664
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_th_unispeech-sat_s772 | 74a2ebf6f0d64a735d6be2947fe2b3cd83d8535e | 2022-07-08T15:04:41.000Z | [
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_th_unispeech-sat_s772 | 5 | null | transformers | 17,547 | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-sat_s772
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
sl82/swin-tiny-patch4-window7-224-finetuned-eurosat | 38e0cafd26c34d8f8c6b67a7cb60c76f34917a69 | 2022-07-09T03:36:40.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | sl82 | null | sl82/swin-tiny-patch4-window7-224-finetuned-eurosat | 5 | null | transformers | 17,548 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9837037037037037
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0581
- Accuracy: 0.9837
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2666 | 1.0 | 190 | 0.1364 | 0.9541 |
| 0.1735 | 2.0 | 380 | 0.0970 | 0.9663 |
| 0.126 | 3.0 | 570 | 0.0581 | 0.9837 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aktsvigun/bart-base_xsum_6585777 | 8d9c2ae8a034ffba451825366861a767056c3d0e | 2022-07-10T10:21:52.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_xsum_6585777 | 5 | null | transformers | 17,549 | Entry not found |
jonatasgrosman/exp_w2v2t_it_wavlm_s662 | 1c525a018f3d06fffa97298c6a1adfe85c4290ff | 2022-07-08T20:06:11.000Z | [
"pytorch",
"wavlm",
"automatic-speech-recognition",
"it",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_it_wavlm_s662 | 5 | null | transformers | 17,550 | ---
language:
- it
license: apache-2.0
tags:
- automatic-speech-recognition
- it
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_it_wavlm_s662
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_wav2vec2_s227 | 6b3071c2d439da665487cee0de4f200e39fe4eea | 2022-07-08T22:58:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_wav2vec2_s227 | 5 | null | transformers | 17,551 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_wav2vec2_s227
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_wav2vec2_s809 | 0823307e7a2e227e6ae821fc1c63a1ab80146617 | 2022-07-08T23:04:08.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_wav2vec2_s809 | 5 | null | transformers | 17,552 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_wav2vec2_s809
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_wav2vec2_s870 | f1d67dab853edc1b94c6f56479e8b74a831fe010 | 2022-07-08T23:07:27.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_wav2vec2_s870 | 5 | null | transformers | 17,553 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_wav2vec2_s870
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-100k_s688 | 10465c71251c7c909dc27c378dc8426b45581dea | 2022-07-08T23:12:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-100k_s688 | 5 | null | transformers | 17,554 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-100k_s688
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-100k_s509 | 83cc60f0f3e2a239ec702335e5dc7e3251718f50 | 2022-07-08T23:17:07.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-100k_s509 | 5 | null | transformers | 17,555 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-100k_s509
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-100k_s973 | 3f808e7330156f96d462488d3808abf479f5e6a8 | 2022-07-08T23:21:17.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-100k_s973 | 5 | null | transformers | 17,556 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-100k_s973
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_xlsr-53_s286 | 081e0585eb1d04cf3d5d9dd10052aaa99ae45f91 | 2022-07-08T23:25:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_xlsr-53_s286 | 5 | null | transformers | 17,557 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_xlsr-53_s286
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_xlsr-53_s800 | 5c802a890571d383a19d3e1bd4a3f0d9850ad6bd | 2022-07-08T23:28:33.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_xlsr-53_s800 | 5 | null | transformers | 17,558 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_xlsr-53_s800
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_xlsr-53_s539 | 92147e9e845822ef1f664e521a0c6ee3096e4594 | 2022-07-08T23:32:25.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_xlsr-53_s539 | 5 | null | transformers | 17,559 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_xlsr-53_s539
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-sv_s875 | a37b6c45536ecf1920d53e80fde8309faa32e5c8 | 2022-07-09T00:01:55.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-sv_s875 | 5 | null | transformers | 17,560 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-sv_s875
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-sv_s596 | 814d060f8a89a5e8c127fcaabf2df136eacc9bd7 | 2022-07-09T00:05:16.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-sv_s596 | 5 | null | transformers | 17,561 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-sv_s596
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_vp-sv_s877 | 58dbbffad038ceb9b32bf12d7dce4092a190e9ee | 2022-07-09T00:08:48.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_vp-sv_s877 | 5 | null | transformers | 17,562 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_vp-sv_s877
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_no-pretraining_s766 | 3fc36e65ba875b669cd00227736dcac110628291 | 2022-07-09T00:12:36.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_no-pretraining_s766 | 5 | null | transformers | 17,563 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_no-pretraining_s766
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_no-pretraining_s929 | cb21c029b6ef52d99e9b22690391d1417252a247 | 2022-07-09T00:17:54.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_no-pretraining_s929 | 5 | null | transformers | 17,564 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_no-pretraining_s929
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fr_no-pretraining_s208 | b4c50730e4746dca49c6e18a99da1a115176a3db | 2022-07-09T00:24:47.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fr_no-pretraining_s208 | 5 | null | transformers | 17,565 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fr_no-pretraining_s208
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Aktsvigun/bart-base_xsum_919213 | 82b76739fc20eb443639dc6e426ca1ad94d37162 | 2022-07-10T10:19:12.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_xsum_919213 | 5 | null | transformers | 17,566 | Entry not found |
Aktsvigun/bart-base_xsum_5537116 | 2d71515663db5732ff4f680e4501410d316846bc | 2022-07-10T10:16:19.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_xsum_5537116 | 5 | null | transformers | 17,567 | Entry not found |
dingusagar/vit-base-movie-scenes-v1 | 28c56d8a2f6bad19e6b534fbd97268ccfc0b3f69 | 2022-07-09T14:34:10.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | dingusagar | null | dingusagar/vit-base-movie-scenes-v1 | 5 | null | transformers | 17,568 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-movie-scenes-v1
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. -->
# vit-base-movie-scenes-v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
Fine-tuned on movie scene images from batman and harry potter.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/bro_b619 | 05d2f9003ff3958a96b3958b5aa464683d871c44 | 2022-07-09T15:47:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bro_b619 | 5 | null | transformers | 17,569 | ---
language: en
thumbnail: http://www.huggingtweets.com/bro_b619/1657381637888/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/1475310547805425664/2vnSS9WL_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">Brutha B 🧀🌐</div>
<div style="text-align: center; font-size: 14px;">@bro_b619</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 Brutha B 🧀🌐.
| Data | Brutha B 🧀🌐 |
| --- | --- |
| Tweets downloaded | 1922 |
| Retweets | 302 |
| Short tweets | 345 |
| Tweets kept | 1275 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2lb73vwt/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 @bro_b619's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xm49vj8a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xm49vj8a/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/bro_b619')
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)
|
huangjia/xlm-roberta-base-finetuned-panx-en | 750cfa06e750c7c41ab189bc425baed45e616137 | 2022-07-09T16:12:45.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | huangjia | null | huangjia/xlm-roberta-base-finetuned-panx-en | 5 | null | transformers | 17,570 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.618063112078346
---
<!-- 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-en
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: 0.4603
- F1: 0.6181
## 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: 48
- eval_batch_size: 48
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 25 | 0.8577 | 0.3917 |
| 1.0821 | 2.0 | 50 | 0.5391 | 0.5466 |
| 1.0821 | 3.0 | 75 | 0.4603 | 0.6181 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.18.4
- Tokenizers 0.10.3
|
jonatasgrosman/exp_w2v2t_fa_unispeech_s364 | 50009b1bf2be05e0031f937524b24c55067e4500 | 2022-07-09T20:26:09.000Z | [
"pytorch",
"unispeech",
"automatic-speech-recognition",
"fa",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fa_unispeech_s364 | 5 | null | transformers | 17,571 | ---
language:
- fa
license: apache-2.0
tags:
- automatic-speech-recognition
- fa
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fa_unispeech_s364
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fa_hubert_s801 | 33148b3a6ea02f29f2ed0a5350ca96837735b30b | 2022-07-09T20:29:40.000Z | [
"pytorch",
"hubert",
"automatic-speech-recognition",
"fa",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fa_hubert_s801 | 5 | null | transformers | 17,572 | ---
language:
- fa
license: apache-2.0
tags:
- automatic-speech-recognition
- fa
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fa_hubert_s801
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_fa_vp-it_s18 | ec5c69992c5a150d46c6aae1edb8c0e959091e3d | 2022-07-09T23:59:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fa",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fa_vp-it_s18 | 5 | null | transformers | 17,573 | ---
language:
- fa
license: apache-2.0
tags:
- automatic-speech-recognition
- fa
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fa_vp-it_s18
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Cleyden/roberta-base-prop-16-train-set | 6e2bb4e6a6e5e955eceea76a05d8f469139833bf | 2022-07-10T03:20:39.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | Cleyden | null | Cleyden/roberta-base-prop-16-train-set | 5 | null | transformers | 17,574 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-prop-16-train-set
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown 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: 5e-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: 3.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_uk_vp-es_s692 | f6ca6674d776812f241349fb69978a0d3857d1c2 | 2022-07-10T14:38:57.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_uk_vp-es_s692 | 5 | null | transformers | 17,575 | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-es_s692
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
nestoralvaro/distilbert-base-uncased-finetuned-ner | 0c572a2485e67945381c34c0e436ebfbc5d7690a | 2022-07-10T21:28:55.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | nestoralvaro | null | nestoralvaro/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 17,576 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
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-ner
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: 0.4253
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9226
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 15 | 0.4677 | 0.0 | 0.0 | 0.0 | 0.9226 |
| No log | 2.0 | 30 | 0.4303 | 0.0 | 0.0 | 0.0 | 0.9226 |
| No log | 3.0 | 45 | 0.4253 | 0.0 | 0.0 | 0.0 | 0.9226 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pl_unispeech_s622 | e01783bc2efccdf21bf0ad36515e8cf6ec23d03a | 2022-07-10T18:50:03.000Z | [
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pl",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_pl_unispeech_s622 | 5 | null | transformers | 17,577 | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_unispeech_s622
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
tner/bert-large-tweetner-2020-2021-continuous | a9d36a5143df0f800a8ba0744b2081c86b57e3e8 | 2022-07-12T09:28:50.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/bert-large-tweetner-2020-2021-continuous | 5 | null | transformers | 17,578 | Entry not found |
jonatasgrosman/exp_w2v2t_es_unispeech_s767 | 82fc37726d7d5c9fa905f324570ac647ce07d2f1 | 2022-07-11T10:46:34.000Z | [
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_es_unispeech_s767 | 5 | null | transformers | 17,579 | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech_s767
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
tner/twitter-roberta-base-2019-90m-tweetner-random | 7a5278e5b0f102bd94e85562b3c438b4e448b7d8 | 2022-07-11T11:21:09.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-2019-90m-tweetner-random | 5 | null | transformers | 17,580 | Entry not found |
tner/bertweet-base-tweetner-random | 3cc6ff7d4c8815b3eadc397caea571989ea1fe66 | 2022-07-11T16:05:38.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/bertweet-base-tweetner-random | 5 | null | transformers | 17,581 | Entry not found |
ManqingLiu/pegasus-samsum | 3068c04c734baa02d57d76874811e9b5e4667e2b | 2022-07-11T22:33:51.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ManqingLiu | null | ManqingLiu/pegasus-samsum | 5 | null | transformers | 17,582 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4858
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7236 | 0.54 | 500 | 1.4858 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.10.3
|
Evelyn18/legalectra-small-spanish-becasv3-1 | 8d1e390c0cd0deff6b641034d4337e352bbbadad | 2022-07-12T03:54:49.000Z | [
"pytorch",
"tensorboard",
"electra",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/legalectra-small-spanish-becasv3-1 | 5 | null | transformers | 17,583 | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-1
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. -->
# legalectra-small-spanish-becasv3-1
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5694
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 8 | 5.8980 |
| No log | 2.0 | 16 | 5.8136 |
| No log | 3.0 | 24 | 5.7452 |
| No log | 4.0 | 32 | 5.6940 |
| No log | 5.0 | 40 | 5.6554 |
| No log | 6.0 | 48 | 5.6241 |
| No log | 7.0 | 56 | 5.5997 |
| No log | 8.0 | 64 | 5.5830 |
| No log | 9.0 | 72 | 5.5730 |
| No log | 10.0 | 80 | 5.5694 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-512-5-30 | e8041f58b37dc2b81490aad0dfca1ed5ec46d862 | 2022-07-12T11:29:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-512-5-30 | 5 | null | transformers | 17,584 | Entry not found |
andreaschandra/xlm-roberta-base-finetuned-panx-de | c45a0bdca885932d5d37fb1fd3d7a5125706a668 | 2022-07-12T13:52:44.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | andreaschandra | null | andreaschandra/xlm-roberta-base-finetuned-panx-de | 5 | null | transformers | 17,585 | ---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
andreaschandra/xlm-roberta-base-finetuned-panx-it | 408f78625ea702492939d5100146042210b5bca2 | 2022-07-12T15:34:53.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | andreaschandra | null | andreaschandra/xlm-roberta-base-finetuned-panx-it | 5 | null | transformers | 17,586 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8288879770209273
---
<!-- 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-it
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: 0.2380
- F1: 0.8289
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7058 | 1.0 | 70 | 0.3183 | 0.7480 |
| 0.2808 | 2.0 | 140 | 0.2647 | 0.8070 |
| 0.1865 | 3.0 | 210 | 0.2380 | 0.8289 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ilmariky/bert-base-finnish-cased-squad1-fi | 997f36aaa049ea452aa8c87b7873ddd01e059c00 | 2022-07-12T19:09:57.000Z | [
"pytorch",
"bert",
"question-answering",
"fi",
"dataset:SQuAD_v2_fi + Finnish partition of TyDi-QA",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | question-answering | false | ilmariky | null | ilmariky/bert-base-finnish-cased-squad1-fi | 5 | null | transformers | 17,587 | ---
language: fi
datasets:
- SQuAD_v2_fi + Finnish partition of TyDi-QA
license: gpl-3.0
---
# bert-base-finnish-cased-v1 for QA
This is the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model, fine-tuned using an automatically translated [Finnish version of the SQuAD2.0 dataset](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) in combination with the Finnish partition of the [TyDi-QA](https://github.com/google-research-datasets/tydiqa) dataset. It's been trained on question-answer pairs, **excluding unanswerable questions**, for the task of question answering.
Another QA model that has been fine-tuned with also unanswerable questions is also available: [bert-base-finnish-cased-squad2-fi](https://huggingface.co/ilmariky/bert-base-finnish-cased-squad1-fi).
## Overview
**Language model:** bert-base-finnish-cased-v1
**Language:** Finnish
**Downstream-task:** Extractive QA
**Training data:** Answerable questions from [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
**Eval data:** Answerable questions from [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "ilmariky/bert-base-finnish-cased-squad1-fi"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Mikä tämä on?',
'context': 'Tämä on testi.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Performance
Evaluated with a slightly modified version of the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
{
"exact": 58.00497718788884,
"f1": 69.90891092523077,
"total": 4822,
"HasAns_exact": 58.00497718788884,
"HasAns_f1": 69.90891092523077,
"HasAns_total": 4822
}
```
|
huggingtweets/majigglydoobers | 060a3d7270bffc8fb0b1e24188bab03c9b7eef8e | 2022-07-13T02:58:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/majigglydoobers | 5 | null | transformers | 17,588 | ---
language: en
thumbnail: http://www.huggingtweets.com/majigglydoobers/1657681081092/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/1542204712455241729/6E7rxSrt_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">doobers 👻❤️🩹</div>
<div style="text-align: center; font-size: 14px;">@majigglydoobers</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 doobers 👻❤️🩹.
| Data | doobers 👻❤️🩹 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 2046 |
| Short tweets | 199 |
| Tweets kept | 1004 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36h6xok5/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 @majigglydoobers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/emkivtny) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/emkivtny/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/majigglydoobers')
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)
|
ahadda5/bart_wikikp_kp20k | 6a5b2cbf01e6a28fa204307c991cb461bdcf1a01 | 2022-07-13T12:30:37.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ahadda5 | null | ahadda5/bart_wikikp_kp20k | 5 | null | transformers | 17,589 | bart trained on wikikp then midas/kp20k |
jordyvl/udpos28-sm-first-POS | 5fedbcc0468feae273198688406116251642eb1d | 2022-07-13T12:53:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:udpos28",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | jordyvl | null | jordyvl/udpos28-sm-first-POS | 5 | null | transformers | 17,590 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: udpos28-sm-first-POS
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: udpos28
type: udpos28
args: en
metrics:
- name: Precision
type: precision
value: 0.9511089206505667
- name: Recall
type: recall
value: 0.9546093116207286
- name: F1
type: f1
value: 0.9528559014062253
- name: Accuracy
type: accuracy
value: 0.9559133601686793
---
<!-- 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. -->
# udpos28-sm-first-POS
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1896
- Precision: 0.9511
- Recall: 0.9546
- F1: 0.9529
- Accuracy: 0.9559
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1696 | 1.0 | 4978 | 0.1700 | 0.9440 | 0.9464 | 0.9452 | 0.9472 |
| 0.0973 | 2.0 | 9956 | 0.1705 | 0.9487 | 0.9533 | 0.9510 | 0.9543 |
| 0.0508 | 3.0 | 14934 | 0.1896 | 0.9511 | 0.9546 | 0.9529 | 0.9559 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
RJ3vans/ElectraCCVspanTagger | cd5563d42e2c7cee72413a446f6fd12ca47ed8ce | 2022-07-13T16:11:08.000Z | [
"pytorch",
"electra",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/ElectraCCVspanTagger | 5 | null | transformers | 17,591 | Entry not found |
kuttersn/test-clm | 0ffb306943a919c706f0e491aeb0fd8e710b42f8 | 2022-07-15T02:04:32.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | kuttersn | null | kuttersn/test-clm | 5 | null | transformers | 17,592 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test-clm
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. -->
# test-clm
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5311
- Accuracy: 0.3946
## 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: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/Modifiedbiobert-v1.1-BioRED-CD-128-32-30 | 7dbee0b8a1f295283a18c92e2657bbd65526aca6 | 2022-07-13T17:48:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modifiedbiobert-v1.1-BioRED-CD-128-32-30 | 5 | null | transformers | 17,593 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: Modifiedbiobert-v1.1-BioRED-CD-128-32-30
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. -->
# Modifiedbiobert-v1.1-BioRED-CD-128-32-30
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.10.3
|
ghadeermobasher/Modifiedbiobert-v1.1-BioRED-CD-256-16-5 | bd37b8ebc9b2b0f809d6b706ff5db398c339a60a | 2022-07-13T19:49:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modifiedbiobert-v1.1-BioRED-CD-256-16-5 | 5 | null | transformers | 17,594 | Entry not found |
Evelyn18/distilbert-base-uncased-prueba2 | 89428ebb280adac9ac0af0458972ba6d63945449 | 2022-07-13T21:14:13.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-prueba2 | 5 | null | transformers | 17,595 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-prueba2
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-prueba2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6356
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 3.9054 |
| No log | 2.0 | 18 | 3.1893 |
| No log | 3.0 | 27 | 2.9748 |
| No log | 4.0 | 36 | 3.1541 |
| No log | 5.0 | 45 | 3.2887 |
| No log | 6.0 | 54 | 3.5055 |
| No log | 7.0 | 63 | 3.5902 |
| No log | 8.0 | 72 | 3.6356 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
RJ3vans/DeBERTaSSCCVspanTagger | 9d847ea9c5ec97f9366ca5a2e40f0957f24febb3 | 2022-07-14T15:23:22.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/DeBERTaSSCCVspanTagger | 5 | null | transformers | 17,596 | Entry not found |
RJ3vans/DeBERTaCCVspanTagger | 7a0de436ae510f3894f913575d3dc8fd4ab141eb | 2022-07-14T16:31:09.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/DeBERTaCCVspanTagger | 5 | null | transformers | 17,597 | Entry not found |
Sayan01/tiny-bert-qnli-128-distilled | 8488aca135ad04a50958e90b84bdce3868ef2414 | 2022-07-15T04:08:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Sayan01 | null | Sayan01/tiny-bert-qnli-128-distilled | 5 | null | transformers | 17,598 | Entry not found |
CennetOguz/bert-large-uncased-finetuned-youcook_2 | 7bc559a4132a473efbbda939e2c2c34cf2c5ad20 | 2022-07-15T00:16:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | CennetOguz | null | CennetOguz/bert-large-uncased-finetuned-youcook_2 | 5 | null | transformers | 17,599 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-finetuned-youcook_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. -->
# bert-large-uncased-finetuned-youcook_2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9929
## 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: 5
- eval_batch_size: 5
- seed: 42
- distributed_type: multi-GPU
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3915 | 1.0 | 206 | 2.1036 |
| 2.0412 | 2.0 | 412 | 2.2207 |
| 1.9062 | 3.0 | 618 | 1.7281 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0a0+17540c5
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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