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eldadshulman/distilbert-base-uncased-finetuned-squad | 7d506eb577518a171c31a490d29bbcf0c873ef39 | 2022-05-22T15:44:07.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | eldadshulman | null | eldadshulman/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 32,100 | Entry not found |
stevemobs/deberta-base-finetuned-squad1 | 0f5f8f653d34e92056c2bdad38bfd4b5397ada47 | 2022-05-22T19:54:06.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-finetuned-squad1 | 1 | null | transformers | 32,101 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: deberta-base-finetuned-squad1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-finetuned-squad1
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7928 | 1.0 | 7380 | 0.7810 |
| 0.5795 | 2.0 | 14760 | 0.8037 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Sangita/distilbert-base-uncased-finetuned-squad | da6e92af86592965fdd30c3377b85c01f50f0045 | 2022-05-22T16:42:57.000Z | [
"pytorch",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Sangita | null | Sangita/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 32,102 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Diegomejia/bert-ucb-3 | df097e2487a473933600aba2a90031c0c1ef22eb | 2022-05-22T17:34:54.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Diegomejia | null | Diegomejia/bert-ucb-3 | 1 | null | transformers | 32,103 | Entry not found |
chrisvinsen/wav2vec2-5 | bca3039202a27971dcda8c21bd69ec5690f581f0 | 2022-05-22T21:32:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/wav2vec2-5 | 1 | null | transformers | 32,104 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-5
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. -->
# wav2vec2-5
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: 3.0700
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.4082 | 1.37 | 200 | 3.3181 | 1.0 |
| 2.8798 | 2.74 | 400 | 2.9921 | 1.0 |
| 2.8703 | 4.11 | 600 | 3.1937 | 1.0 |
| 2.8643 | 5.48 | 800 | 3.0304 | 1.0 |
| 2.8655 | 6.85 | 1000 | 3.0321 | 1.0 |
| 2.8655 | 8.22 | 1200 | 3.0716 | 1.0 |
| 2.863 | 9.59 | 1400 | 3.1764 | 1.0 |
| 2.8567 | 10.96 | 1600 | 3.0600 | 1.0 |
| 2.861 | 12.33 | 1800 | 3.1761 | 1.0 |
| 2.8606 | 13.7 | 2000 | 3.1028 | 1.0 |
| 2.8613 | 15.07 | 2200 | 3.2119 | 1.0 |
| 2.8612 | 16.44 | 2400 | 3.1158 | 1.0 |
| 2.8603 | 17.81 | 2600 | 3.1230 | 1.0 |
| 2.8601 | 19.18 | 2800 | 3.0380 | 1.0 |
| 2.856 | 20.55 | 3000 | 3.0729 | 1.0 |
| 2.8557 | 21.92 | 3200 | 3.0511 | 1.0 |
| 2.8556 | 23.29 | 3400 | 3.0710 | 1.0 |
| 2.8552 | 24.66 | 3600 | 3.1364 | 1.0 |
| 2.8574 | 26.03 | 3800 | 3.0104 | 1.0 |
| 2.8543 | 27.4 | 4000 | 3.1068 | 1.0 |
| 2.8558 | 28.77 | 4200 | 3.0700 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
krotima1/mbart-ht2a-c | 1e5103eff3e9bac2d4a2bf90eb6ee6fd635093fd | 2022-05-23T20:37:09.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"cs",
"dataset:private CNC dataset news-based",
"transformers",
"abstractive summarization",
"mbart-cc25",
"Czech",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | krotima1 | null | krotima1/mbart-ht2a-c | 1 | null | transformers | 32,105 | ---
language:
- cs
- cs
tags:
- abstractive summarization
- mbart-cc25
- Czech
license: apache-2.0
datasets:
- private CNC dataset news-based
metrics:
- rouge
- rougeraw
---
# mBART fine-tuned model for Czech abstractive summarization (HT2A-C)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Headline + Text to Abstract`` (HT2A) which consists in generating a multi-sentence summary considered as an abstract from a Czech news text.
## Dataset
The model has been trained on the private CNC dataset provided by Czech News Center. The dataset includes 3/4M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were set to 512 tokens for the encoder and 128 for the decoder.
## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 60 hours. During training, the model has seen 3712K documents corresponding to roughly 5.5 epochs.
# Use
Assuming you are using the provided Summarizer.ipynb file.
```python
def summ_config():
cfg = OrderedDict([
# summarization model - checkpoint from website
("model_name", "krotima1/mbart-ht2a-c"),
("inference_cfg", OrderedDict([
("num_beams", 4),
("top_k", 40),
("top_p", 0.92),
("do_sample", True),
("temperature", 0.89),
("repetition_penalty", 1.2),
("no_repeat_ngram_size", None),
("early_stopping", True),
("max_length", 128),
("min_length", 10),
])),
#texts to summarize
("text",
[
"Input your Czech text",
]
),
])
return cfg
cfg = summ_config()
#load model
model = AutoModelForSeq2SeqLM.from_pretrained(cfg["model_name"])
tokenizer = AutoTokenizer.from_pretrained(cfg["model_name"])
# init summarizer
summarize = Summarizer(model, tokenizer, cfg["inference_cfg"])
summarize(cfg["text"])
``` |
stevemobs/deberta-base-finetuned-squad1-aqa | bff4a8b384ccc9f3977ebd9d714c34fe0690fc33 | 2022-05-22T22:10:52.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"dataset:adversarial_qa",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-finetuned-squad1-aqa | 1 | null | transformers | 32,106 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- adversarial_qa
model-index:
- name: deberta-base-finetuned-squad1-aqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-finetuned-squad1-aqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1](https://huggingface.co/stevemobs/deberta-base-finetuned-squad1) on the adversarial_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9115 | 1.0 | 2527 | 1.5572 |
| 1.3429 | 2.0 | 5054 | 1.5912 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
prodm93/gpt2_rn_ep2_model | aae7e0e9f74e2db128c8a5ff0d96c77f6639e5cb | 2022-05-22T21:07:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | prodm93 | null | prodm93/gpt2_rn_ep2_model | 1 | null | transformers | 32,107 | Entry not found |
globuslabs/ScholarBERT_1 | fe600127108bb3de1f4f9f32ff6235d28510831e | 2022-05-24T03:16:16.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"arxiv:2205.11342",
"transformers",
"science",
"multi-displinary",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | globuslabs | null | globuslabs/ScholarBERT_1 | 1 | null | transformers | 32,108 | ---
language: en
tags:
- science
- multi-displinary
license: apache-2.0
---
# ScholarBERT_1 Model
This is the **ScholarBERT_1** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**2.2B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters.
# Model Architecture
| Hyperparameter | Value |
|-----------------|:-------:|
| Layers | 24 |
| Hidden Size | 1024 |
| Attention Heads | 16 |
| Total Parameters | 340M |
# Training Dataset
The vocab and the model are pertrained on **1% of the PRD** scientific literature dataset.
The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”),
a nonprofit organization based in California. This dataset was constructed from a corpus
of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals.
The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences,
Social Sciences, and Technology. The distribution of articles is shown below.
# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2022scholarbert,
doi = {10.48550/ARXIV.2205.11342},
url = {https://arxiv.org/abs/2205.11342},
author = {Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian},
title = {ScholarBERT: Bigger is Not Always Better},
publisher = {arXiv},
year = {2022}
}
``` |
chrisvinsen/wav2vec2-6 | 89af540e2c3cdebd98cc3f7cd0aac56719113f5c | 2022-05-23T03:36:25.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/wav2vec2-6 | 1 | null | transformers | 32,109 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-6
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. -->
# wav2vec2-6
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: 5.2459
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.5873 | 1.56 | 200 | 5.4586 | 1.0 |
| 4.1846 | 3.12 | 400 | 5.2278 | 1.0 |
| 4.1711 | 4.69 | 600 | 5.3131 | 1.0 |
| 4.1581 | 6.25 | 800 | 5.2558 | 1.0 |
| 4.1275 | 7.81 | 1000 | 5.2556 | 1.0 |
| 4.1452 | 9.38 | 1200 | 5.2637 | 1.0 |
| 4.1614 | 10.94 | 1400 | 5.2847 | 1.0 |
| 4.1667 | 12.5 | 1600 | 5.2349 | 1.0 |
| 4.1471 | 14.06 | 1800 | 5.2850 | 1.0 |
| 4.1268 | 15.62 | 2000 | 5.2510 | 1.0 |
| 4.1701 | 17.19 | 2200 | 5.2605 | 1.0 |
| 4.1459 | 18.75 | 2400 | 5.2493 | 1.0 |
| 4.1411 | 20.31 | 2600 | 5.2649 | 1.0 |
| 4.1351 | 21.88 | 2800 | 5.2541 | 1.0 |
| 4.1442 | 23.44 | 3000 | 5.2459 | 1.0 |
| 4.1805 | 25.0 | 3200 | 5.2232 | 1.0 |
| 4.1262 | 26.56 | 3400 | 5.2384 | 1.0 |
| 4.145 | 28.12 | 3600 | 5.2522 | 1.0 |
| 4.142 | 29.69 | 3800 | 5.2459 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Diegomejia/bert-ucb-4 | 4e80d217bfcd36f054e5f412dc607800724007d5 | 2022-05-23T01:11:03.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Diegomejia | null | Diegomejia/bert-ucb-4 | 1 | null | transformers | 32,110 | Entry not found |
Dizzykong/Gusteau | b32164f5366dd90719d84803148aff1cbe0edae9 | 2022-05-23T06:26:23.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | Dizzykong | null | Dizzykong/Gusteau | 1 | null | transformers | 32,111 | ---
tags:
- generated_from_trainer
model-index:
- name: Gusteau
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. -->
# Gusteau
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.16
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Splend1dchan/wav2vec2-large-lv60_t5lephone-small_lnalrdiff_bs64 | d18ad040648cc23023e24ba5f9fe7d0a6611bc12 | 2022-05-23T08:37:55.000Z | [
"pytorch",
"speechmix",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/wav2vec2-large-lv60_t5lephone-small_lnalrdiff_bs64 | 1 | null | transformers | 32,112 | Entry not found |
PSW/samsum_percent1_maxsimdel | 926837524d90fa87b73f38d832a35804520f09e3 | 2022-05-23T02:23:43.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_maxsimdel | 1 | null | transformers | 32,113 | Entry not found |
PSW/samsum_percent1_minsimins | bfc33b9cfc7bde57c2eb5fdb1c0b347ed7326289 | 2022-05-23T02:34:32.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_minsimins | 1 | null | transformers | 32,114 | Entry not found |
PSW/samsum_percent10_maxsimdel | 290b2e3dee9750cccb3a008326f9a50d4dbd5a02 | 2022-05-23T02:48:50.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent10_maxsimdel | 1 | null | transformers | 32,115 | Entry not found |
Dulu/wav2vec2-xlsr-mn-eng-v0 | a3c7dd5ddb9e41f297efff9ef998a92b759c5698 | 2022-05-24T19:27:25.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Dulu | null | Dulu/wav2vec2-xlsr-mn-eng-v0 | 1 | null | transformers | 32,116 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xlsr-mn-eng
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-xlsr-mn-eng
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the IEMOCAP and Common Voice's MN dataset. Can be used to recognize speech on ENG and MN simultaneously.
It achieves the following results on the evaluation set:
- Loss: 0.3087
- Wer: 0.3402
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.8609 | 0.08 | 500 | 3.6078 | 1.0 |
| 3.5494 | 0.15 | 1000 | 3.2044 | 1.0 |
| 3.1699 | 0.23 | 1500 | 3.1560 | 1.0 |
| 3.0955 | 0.3 | 2000 | 3.1087 | 1.0 |
| 2.7918 | 0.38 | 2500 | 2.1146 | 1.0236 |
| 2.0528 | 0.45 | 3000 | 1.4938 | 0.9648 |
| 1.6329 | 0.53 | 3500 | 1.2614 | 0.9198 |
| 1.3932 | 0.6 | 4000 | 1.0504 | 0.8314 |
| 1.2652 | 0.68 | 4500 | 0.9664 | 0.7809 |
| 1.1829 | 0.76 | 5000 | 0.8999 | 0.7381 |
| 1.1674 | 0.83 | 5500 | 0.8200 | 0.6924 |
| 1.0599 | 0.91 | 6000 | 0.7713 | 0.6729 |
| 1.027 | 0.98 | 6500 | 0.7714 | 0.6616 |
| 0.9289 | 1.06 | 7000 | 0.7571 | 0.6433 |
| 0.9192 | 1.13 | 7500 | 0.6899 | 0.6151 |
| 0.8996 | 1.21 | 8000 | 0.7012 | 0.6104 |
| 0.9281 | 1.28 | 8500 | 0.6452 | 0.5914 |
| 0.8656 | 1.36 | 9000 | 0.6162 | 0.5781 |
| 0.8635 | 1.44 | 9500 | 0.6249 | 0.5672 |
| 0.8388 | 1.51 | 10000 | 0.5936 | 0.5558 |
| 0.8087 | 1.59 | 10500 | 0.5844 | 0.5466 |
| 0.7755 | 1.66 | 11000 | 0.5838 | 0.5364 |
| 0.8377 | 1.74 | 11500 | 0.5358 | 0.5202 |
| 0.8308 | 1.81 | 12000 | 0.5333 | 0.5196 |
| 0.7775 | 1.89 | 12500 | 0.5129 | 0.5060 |
| 0.7747 | 1.96 | 13000 | 0.5164 | 0.5096 |
| 0.7115 | 2.04 | 13500 | 0.5056 | 0.4936 |
| 0.6974 | 2.12 | 14000 | 0.4925 | 0.4878 |
| 0.6672 | 2.19 | 14500 | 0.5030 | 0.4908 |
| 0.6396 | 2.27 | 15000 | 0.4821 | 0.4686 |
| 0.6943 | 2.34 | 15500 | 0.4693 | 0.4624 |
| 0.6413 | 2.42 | 16000 | 0.4626 | 0.4636 |
| 0.6446 | 2.49 | 16500 | 0.4513 | 0.4609 |
| 0.6338 | 2.57 | 17000 | 0.4386 | 0.4524 |
| 0.6208 | 2.65 | 17500 | 0.4360 | 0.4445 |
| 0.6397 | 2.72 | 18000 | 0.4348 | 0.4355 |
| 0.6127 | 2.8 | 18500 | 0.4367 | 0.4318 |
| 0.5956 | 2.87 | 19000 | 0.4376 | 0.4322 |
| 0.6345 | 2.95 | 19500 | 0.4050 | 0.4308 |
| 0.572 | 3.02 | 20000 | 0.4211 | 0.4219 |
| 0.5447 | 3.1 | 20500 | 0.4042 | 0.4112 |
| 0.5323 | 3.17 | 21000 | 0.4101 | 0.4153 |
| 0.5677 | 3.25 | 21500 | 0.3952 | 0.4188 |
| 0.5354 | 3.33 | 22000 | 0.3889 | 0.4007 |
| 0.5297 | 3.4 | 22500 | 0.3793 | 0.3997 |
| 0.5314 | 3.48 | 23000 | 0.3684 | 0.3956 |
| 0.5217 | 3.55 | 23500 | 0.3572 | 0.3853 |
| 0.5224 | 3.63 | 24000 | 0.3535 | 0.3867 |
| 0.4983 | 3.7 | 24500 | 0.3636 | 0.3804 |
| 0.5355 | 3.78 | 25000 | 0.3680 | 0.3770 |
| 0.5115 | 3.85 | 25500 | 0.3472 | 0.3752 |
| 0.5416 | 3.93 | 26000 | 0.3280 | 0.3689 |
| 0.5104 | 4.01 | 26500 | 0.3319 | 0.3650 |
| 0.4524 | 4.08 | 27000 | 0.3453 | 0.3632 |
| 0.462 | 4.16 | 27500 | 0.3359 | 0.3600 |
| 0.4823 | 4.23 | 28000 | 0.3268 | 0.3553 |
| 0.4671 | 4.31 | 28500 | 0.3248 | 0.3535 |
| 0.4702 | 4.38 | 29000 | 0.3278 | 0.3501 |
| 0.483 | 4.46 | 29500 | 0.3183 | 0.3492 |
| 0.4232 | 4.53 | 30000 | 0.3224 | 0.3470 |
| 0.4227 | 4.61 | 30500 | 0.3171 | 0.3458 |
| 0.4687 | 4.69 | 31000 | 0.3121 | 0.3537 |
| 0.4486 | 4.76 | 31500 | 0.3088 | 0.3424 |
| 0.4459 | 4.84 | 32000 | 0.3101 | 0.3407 |
| 0.4513 | 4.91 | 32500 | 0.3077 | 0.3407 |
| 0.4237 | 4.99 | 33000 | 0.3087 | 0.3402 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
PSW/samsum_percent20_maxsimdel | 28f865f6037ca50e410b05551a7d378a0aa7beaa | 2022-05-23T03:22:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_maxsimdel | 1 | null | transformers | 32,117 | Entry not found |
PSW/samsum_percent20_minsimins | fff29403b875fb96c445cd39d4bb92b8076c1857 | 2022-05-23T03:40:32.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_minsimins | 1 | null | transformers | 32,118 | Entry not found |
chrisvinsen/wav2vec2-7 | f76d0fd94529c300ae257501586b54566d1f6e65 | 2022-05-23T08:09:15.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/wav2vec2-7 | 1 | null | transformers | 32,119 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-7
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. -->
# wav2vec2-7
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: 0.6017
- Wer: 0.5200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1311 | 1.56 | 200 | 2.9839 | 1.0 |
| 2.5727 | 3.12 | 400 | 1.4962 | 1.0209 |
| 1.0187 | 4.69 | 600 | 0.7562 | 0.7859 |
| 0.637 | 6.25 | 800 | 0.6529 | 0.6960 |
| 0.4847 | 7.81 | 1000 | 0.6609 | 0.6745 |
| 0.3952 | 9.38 | 1200 | 0.5808 | 0.6220 |
| 0.3343 | 10.94 | 1400 | 0.5622 | 0.6004 |
| 0.2897 | 12.5 | 1600 | 0.8842 | 0.5980 |
| 0.2549 | 14.06 | 1800 | 0.6047 | 0.5765 |
| 0.2334 | 15.62 | 2000 | 0.6436 | 0.5699 |
| 0.2144 | 17.19 | 2200 | 0.5831 | 0.5593 |
| 0.1982 | 18.75 | 2400 | 0.6327 | 0.5620 |
| 0.1817 | 20.31 | 2600 | 0.8790 | 0.5456 |
| 0.1713 | 21.88 | 2800 | 0.9603 | 0.5362 |
| 0.163 | 23.44 | 3000 | 0.5940 | 0.5384 |
| 0.1539 | 25.0 | 3200 | 0.6058 | 0.5311 |
| 0.1392 | 26.56 | 3400 | 0.6131 | 0.5221 |
| 0.1386 | 28.12 | 3600 | 0.6066 | 0.5258 |
| 0.1351 | 29.69 | 3800 | 0.6017 | 0.5200 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
PSW/samsum_percent1_minsimdel | 407133c0bc339f2056bba3f6d34f4f5d18e5c160 | 2022-05-23T05:19:21.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_minsimdel | 1 | null | transformers | 32,120 | Entry not found |
PSW/samsum_percent1_randomdel | a410c4e0bd6f4543bf1bb927eeb41da65f2099ba | 2022-05-23T05:30:35.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_randomdel | 1 | null | transformers | 32,121 | Entry not found |
PSW/samsum_percent1_maxsimins | 107dc3411830920ba6210eb56ff05733458b64bb | 2022-05-23T05:40:48.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_maxsimins | 1 | null | transformers | 32,122 | Entry not found |
PSW/samsum_percent1_randomins | 1beb50d949e476bb0f6c9dc7eec283470a45a40d | 2022-05-23T05:51:08.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent1_randomins | 1 | null | transformers | 32,123 | Entry not found |
PSW/samsum_percent10_randomdel | dbaa432cc6f67535e126058fdd382a505f37a9d8 | 2022-05-23T06:19:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent10_randomdel | 1 | null | transformers | 32,124 | Entry not found |
PSW/samsum_percent10_randomins | 593205223489e2a1bd32b2fb1ddf93b96cfb4c89 | 2022-05-23T06:49:48.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent10_randomins | 1 | null | transformers | 32,125 | Entry not found |
mriggs/tgb_epoch_1 | f306b58a087c434a5ff62a28978119ed1060c09d | 2022-05-23T06:50:47.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mriggs | null | mriggs/tgb_epoch_1 | 1 | null | transformers | 32,126 | Entry not found |
PSW/samsum_percent20_minsimdel | 6dc06404f1f9796e93978bb1313b844434ef8ed5 | 2022-05-23T07:11:31.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_minsimdel | 1 | null | transformers | 32,127 | Entry not found |
PSW/samsum_percent20_randomdel | ed574c670684299340d756e9ed1c58f49578024e | 2022-05-23T07:29:52.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_randomdel | 1 | null | transformers | 32,128 | Entry not found |
PSW/samsum_percent20_maxsimins | 3f4b1221ac56955f7933c04bf23843aeba1b0dc3 | 2022-05-23T07:49:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_maxsimins | 1 | null | transformers | 32,129 | Entry not found |
PSW/samsum_percent20_randomins | 55246743200ab79dad510833bd8dcea66db827e5 | 2022-05-23T08:10:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_percent20_randomins | 1 | null | transformers | 32,130 | Entry not found |
chrisvinsen/wav2vec2-8 | 1d7e0119d2e1337863b839b361fefcd546525e67 | 2022-05-23T10:57:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/wav2vec2-8 | 1 | null | transformers | 32,131 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-8
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. -->
# wav2vec2-8
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: 3.1169
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.9398 | 1.56 | 200 | 3.1250 | 1.0 |
| 2.8703 | 3.12 | 400 | 3.1608 | 1.0 |
| 2.8632 | 4.69 | 600 | 3.1329 | 1.0 |
| 2.8638 | 6.25 | 800 | 3.0795 | 1.0 |
| 2.8595 | 7.81 | 1000 | 3.1410 | 1.0 |
| 2.8611 | 9.38 | 1200 | 3.0952 | 1.0 |
| 2.861 | 10.94 | 1400 | 3.1391 | 1.0 |
| 2.8603 | 12.5 | 1600 | 3.0639 | 1.0 |
| 2.8568 | 14.06 | 1800 | 3.1180 | 1.0 |
| 2.8563 | 15.62 | 2000 | 3.1170 | 1.0 |
| 2.857 | 17.19 | 2200 | 3.0846 | 1.0 |
| 2.8574 | 18.75 | 2400 | 3.0740 | 1.0 |
| 2.8543 | 20.31 | 2600 | 3.1482 | 1.0 |
| 2.8567 | 21.88 | 2800 | 3.1604 | 1.0 |
| 2.8561 | 23.44 | 3000 | 3.1055 | 1.0 |
| 2.858 | 25.0 | 3200 | 3.0669 | 1.0 |
| 2.8524 | 26.56 | 3400 | 3.0992 | 1.0 |
| 2.8557 | 28.12 | 3600 | 3.1050 | 1.0 |
| 2.8527 | 29.69 | 3800 | 3.1169 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
hamidov02/wav2vec2-large-xls-r-300m-hungarian-colab | fbd5e3292b7d60dbacf7894284e276aa76d09fba | 2022-05-23T13:18:12.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | hamidov02 | null | hamidov02/wav2vec2-large-xls-r-300m-hungarian-colab | 1 | null | transformers | 32,132 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hungarian-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Wer: 0.4662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4833 | 4.0 | 400 | 0.6493 | 0.6491 |
| 0.2282 | 8.0 | 800 | 0.6395 | 0.5555 |
| 0.1612 | 12.0 | 1200 | 0.6841 | 0.5423 |
| 0.1207 | 16.0 | 1600 | 0.6646 | 0.5224 |
| 0.0929 | 20.0 | 2000 | 0.6355 | 0.4908 |
| 0.0713 | 24.0 | 2400 | 0.6410 | 0.4711 |
| 0.0613 | 28.0 | 2800 | 0.6404 | 0.4662 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
tursunali/bpt-2 | 0debf9e5ca418ea15fe81cffebe0ffc1cb29ba5a | 2022-05-24T04:26:57.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"de",
"transformers"
] | text-generation | false | tursunali | null | tursunali/bpt-2 | 1 | null | transformers | 32,133 | ---
language: de
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
---
# BPT2
See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("tursunali/bpt2")
model = AutoModelForCausalLM.from_pretrained("tursunali/bpt2")
prompt = "<your prompt>"
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(pipe(prompt)[0]["generated_text"])
```
Also, two tricks might improve the generated text:
```python
output = model.generate(
# during training an EOS token was used to mark the beginning of each text
# so it can help to insert it at the start
torch.tensor(
[tokenizer.eos_token_id] + tokenizer.encode(prompt)
).unsqueeze(0),
do_sample=True,
# try setting bad_words_ids=[[0]] to disallow generating an EOS token, without this the model is
# prone to ending generation early because a significant number of texts from the training corpus
# is quite short
bad_words_ids=[[0]],
max_length=max_length,
)[0]
print(tokenizer.decode(output))
```
## Citing
Please cite BPT2 as follows:
```
@misc{Backpacker_Trail_German_large_2022,
author = {BackpackerTrail, Tursunali Kholdorov},
title = {{BPT2: Backpacker Trail German versions of BPT2}},
url = {https://github.com/Tursunali-Kholdorov/bptTrainer},
year = {2022}
}
```
|
birdringxD/SSAP_ckpt | c067a05426982970fca04f70b6611c1e369069f3 | 2022-05-23T12:03:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | birdringxD | null | birdringxD/SSAP_ckpt | 1 | null | transformers | 32,134 | Entry not found |
NabilOulbaz/bertweet_retrained_semEval2018 | e7febfb53821bec9222c3eac435b1907baa2f4a4 | 2022-05-23T12:29:01.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | NabilOulbaz | null | NabilOulbaz/bertweet_retrained_semEval2018 | 1 | null | transformers | 32,135 | ---
license: mit
---
|
CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_42 | 513e11bbda191c08131e5253d2d33d3bd0294ee0 | 2022-05-24T10:02:06.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_42 | 1 | null | transformers | 32,136 | Entry not found |
MeshalAlamr/wav2vec2-xls-r-300m-ar-10 | abbce08a08d496384d1140cc24721f0da40fe5f4 | 2022-05-25T00:40:19.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | MeshalAlamr | null | MeshalAlamr/wav2vec2-xls-r-300m-ar-10 | 1 | null | transformers | 32,137 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ar-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ar-10
This model is a fine-tuned version of [MeshalAlamr/wav2vec2-xls-r-300m-ar-9](https://huggingface.co/MeshalAlamr/wav2vec2-xls-r-300m-ar-9) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 87.0172
- Wer: 0.2017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 56.409 | 4.71 | 400 | 81.8407 | 0.2151 |
| 84.2726 | 9.41 | 800 | 82.6777 | 0.2237 |
| 80.3604 | 14.12 | 1200 | 85.3856 | 0.2226 |
| 70.7446 | 18.82 | 1600 | 87.9551 | 0.2180 |
| 61.3713 | 23.53 | 2000 | 88.0419 | 0.2096 |
| 54.5011 | 28.24 | 2400 | 87.0172 | 0.2017 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_42 | ab62c81ca720ae1747a2d88a57c6a7f7722cc256 | 2022-05-24T10:02:16.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_42 | 1 | null | transformers | 32,138 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_42 | 8bba4a65a06041d528131266192c4da034285874 | 2022-05-24T10:02:26.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_42 | 1 | null | transformers | 32,139 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_42 | 7527836851576547be0437c2d21b97e21b982370 | 2022-05-24T10:02:36.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_42 | 1 | null | transformers | 32,140 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_42 | 260f1fc2071e827bd2dfde833eeb63260d482a9f | 2022-05-24T10:05:25.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_42 | 1 | null | transformers | 32,141 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_42 | e1d87333390596e57b8ddd2f03c71d1047330446 | 2022-05-24T10:05:35.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_42 | 1 | null | transformers | 32,142 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_42 | 391c7f931b419937f4a1d81a63ac2d5304c82f3c | 2022-05-24T10:05:45.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_42 | 1 | null | transformers | 32,143 | Entry not found |
paola-md/recipe-steps-en | 818c590583c77a1d20ebde3a4042333020ae6e14 | 2022-05-23T16:35:26.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | paola-md | null | paola-md/recipe-steps-en | 1 | null | transformers | 32,144 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_42 | f7da9a1eccef5506470e840a933a19e2cdbb9e18 | 2022-05-24T10:05:55.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_42 | 1 | null | transformers | 32,145 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_42 | dc5be89e6a9860e9f089e610ee947c98999a2fee | 2022-05-24T10:08:44.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,146 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_42 | 0268bb5565fe673c7185147f6da0da57296f66cd | 2022-05-24T10:09:04.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,147 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | 107e238c1c5fe619d3bbd66e89b38b86602d7561 | 2022-05-24T10:09:14.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | 1 | null | transformers | 32,148 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_43 | fc4e9b56dc12fdb37fe1775a3aeefc595417e69b | 2022-05-24T10:02:08.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_43 | 1 | null | transformers | 32,149 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_43 | 7b7d1ac58bd18a1d2986dd964ddd4e6356b871cd | 2022-05-24T10:02:18.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_43 | 1 | null | transformers | 32,150 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_43 | d622f1f22d63d0fa87aa39b56bfa5fd23606c143 | 2022-05-24T10:02:28.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_43 | 1 | null | transformers | 32,151 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_43 | f6296bc7f0ef9be4828677fa0245d49f8ce07c0c | 2022-05-24T10:02:38.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_43 | 1 | null | transformers | 32,152 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_43 | b3e9a7a86961169bad674433467870a6a7ead4b3 | 2022-05-24T10:05:27.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_43 | 1 | null | transformers | 32,153 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_43 | d049f7c9b8bbcf2f761e177c2ada83e8a5963b6a | 2022-05-24T10:05:37.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_43 | 1 | null | transformers | 32,154 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_43 | e5c6abe205b0cb561c2f8ab1941b1ad8d5471757 | 2022-05-24T10:05:47.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_43 | 1 | null | transformers | 32,155 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_43 | 7544e909acecd2c7cc12e22957c3a9d6c521c35b | 2022-05-24T10:05:57.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_43 | 1 | null | transformers | 32,156 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_43 | 560c0bbb036261033c5e8886ef2746c755ae03eb | 2022-05-24T10:08:46.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,157 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_43 | 5fed7cbf7633657d2cfdc0542baece126a272f73 | 2022-05-24T10:08:56.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_43 | 1 | null | transformers | 32,158 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_43 | dfdf0343ce8066fc290e556e0a7237f40d0839af | 2022-05-24T10:09:06.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,159 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 369079d738d680b0de093acbd105fdf1dfd73638 | 2022-05-24T10:09:16.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 1 | null | transformers | 32,160 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_44 | bd71ef37e481501f896900dead6ad5a8e8f5a6f8 | 2022-05-24T10:02:10.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_44 | 1 | null | transformers | 32,161 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_44 | fec62402a2386dd8f7614e4a95ba0848da1cad31 | 2022-05-24T10:02:20.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_44 | 1 | null | transformers | 32,162 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_44 | f40a0b5491047a0a6543319168f8aedf3375d934 | 2022-05-24T10:02:30.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_44 | 1 | null | transformers | 32,163 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_44 | 190996fbd735e0ddb3a022464c5c5ff57a405183 | 2022-05-24T10:02:40.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_44 | 1 | null | transformers | 32,164 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_44 | 40968b63f1577e37dfda198420c2096b22a4a378 | 2022-05-24T10:05:29.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_44 | 1 | null | transformers | 32,165 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_44 | 8df2b0c15f7b4882c91b23cc0d95eedf53926117 | 2022-05-24T10:05:39.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_44 | 1 | null | transformers | 32,166 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_44 | ccca10e4d97f629ac39e49cc707a85cde28e21a0 | 2022-05-24T10:05:49.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_44 | 1 | null | transformers | 32,167 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_44 | 00cc9689ca2c8c31115d681f91d3bafcce56e69b | 2022-05-24T10:05:59.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_44 | 1 | null | transformers | 32,168 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_44 | efc85030849515ac9da5697d6d5a093d644d43a7 | 2022-05-24T10:08:48.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,169 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_44 | bc26bf80af4e3af1a327c831a89b89852b20a025 | 2022-05-24T10:08:58.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_44 | 1 | null | transformers | 32,170 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_44 | 0ebc3d2e8efd6acdaf62d857e3aebe76b1c4ddd5 | 2022-05-24T10:09:08.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,171 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | 6f7701535d0b48946979c647bdec6c63d138307c | 2022-05-24T10:09:18.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | 1 | null | transformers | 32,172 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_45 | e9f377f5f031892472758a4642e1381e64827a43 | 2022-05-24T10:02:12.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_45 | 1 | null | transformers | 32,173 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_45 | 703df9ff5228222501ad5eabb763794d21d73f53 | 2022-05-24T10:02:22.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_45 | 1 | null | transformers | 32,174 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_45 | 9664c38da6d02878480ff4f5745c14b7588b4c89 | 2022-05-24T10:02:32.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_45 | 1 | null | transformers | 32,175 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_45 | c1142523a8e643a5537d7dcf08e247b38e87e707 | 2022-05-24T10:02:42.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_45 | 1 | null | transformers | 32,176 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_45 | 05c7996437bf001f6349407eace20f36664f467d | 2022-05-24T10:05:31.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_45 | 1 | null | transformers | 32,177 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_45 | 18dce6e5076157cdf9e1e632db0d053a13ebbd7c | 2022-05-24T10:05:41.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_45 | 1 | null | transformers | 32,178 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_45 | 87d4253fa72113ced6e63292c3a64f2b9dfc0546 | 2022-05-24T10:05:51.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_45 | 1 | null | transformers | 32,179 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_45 | 2644c8b7eb4b95465c206e51ecc5487f87bcc0a8 | 2022-05-24T10:06:01.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_45 | 1 | null | transformers | 32,180 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_45 | 7a3d6d5fe3553e5acdcca477fff7c84b9a266d4f | 2022-05-24T10:08:50.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,181 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_45 | b3d83fc26ea91f5837643b3aef523d8b29016c9a | 2022-05-24T10:09:00.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_45 | 1 | null | transformers | 32,182 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_45 | 7b08f8a66dc2e12d717ecd38f4f0d873a403822d | 2022-05-24T10:09:10.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,183 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | c12f827d790fb6b4509d9251422530acb3183c65 | 2022-05-24T10:09:20.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | 1 | null | transformers | 32,184 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_46 | 1ccba82d9a467adf6a3cf9e8878eb88fb8219fb3 | 2022-05-24T10:02:14.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.2-class.exclusive.seed_46 | 1 | null | transformers | 32,185 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_46 | bde06f221b8e4d0c4d61d19826363b815ce5bc37 | 2022-05-24T10:02:24.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.2-class.exclusive.seed_46 | 1 | null | transformers | 32,186 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_46 | e94dad614b1444aa94026f409f460860299a5302 | 2022-05-24T10:02:34.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.2-class.exclusive.seed_46 | 1 | null | transformers | 32,187 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_46 | fe302e1f96792ef18d7d20c42e1715172892a576 | 2022-05-24T10:02:44.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.2-class.exclusive.seed_46 | 1 | null | transformers | 32,188 | Entry not found |
CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_46 | 06a5e7817c0e14d4cb00dc8d46736a611e7344b9 | 2022-05-24T10:05:33.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.3-class.exclusive.seed_46 | 1 | null | transformers | 32,189 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_46 | df8bfbc04c6903c933fda0cade177a9e2b565cb3 | 2022-05-24T10:05:43.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.3-class.exclusive.seed_46 | 1 | null | transformers | 32,190 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_46 | a9887176c6bc21715f154b2334cddb020235fdf0 | 2022-05-24T10:05:53.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.3-class.exclusive.seed_46 | 1 | null | transformers | 32,191 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_46 | f9c9f2857385835dd15863ae7f934c0f6486bc51 | 2022-05-24T10:06:03.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.3-class.exclusive.seed_46 | 1 | null | transformers | 32,192 | Entry not found |
transformertroy/t5-small-finetuned-tds | 62b4a76a051d0fe7a26d7927457d3c6e0f223b14 | 2022-06-01T17:10:46.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"medium-summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | transformertroy | null | transformertroy/t5-small-finetuned-tds | 1 | null | transformers | 32,193 | ---
license: apache-2.0
tags:
- medium-summarization
- generated_from_trainer
model-index:
- name: t5-small-finetuned-tds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-tds
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-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: 8
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_46 | 6a54b4c84b81392d68e5efad8deb294686637696 | 2022-05-24T10:08:52.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.ambiance__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,194 | Entry not found |
CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_46 | 90a3f68e07ace9a08b597d6703efa2f833717a10 | 2022-05-24T10:09:02.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.food__service.5-class.exclusive.seed_46 | 1 | null | transformers | 32,195 | Entry not found |
CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | ecace0c0ccdd765f183f3c16de1560523f1ce2b4 | 2022-05-24T10:09:12.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,196 | Entry not found |
CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | 8c339ddbee561de0ef2c812c3bb69ec341f758d5 | 2022-05-24T10:09:22.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | 1 | null | transformers | 32,197 | Entry not found |
CEBaB/lstm.CEBaB.causalm.None__None.2-class.exclusive.seed_42 | 8da581f7cd927a7488a9db7b04fe5fcd1b41431d | 2022-05-24T10:01:56.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.None__None.2-class.exclusive.seed_42 | 1 | null | transformers | 32,198 | Entry not found |
CEBaB/lstm.CEBaB.causalm.None__None.3-class.exclusive.seed_42 | 01e073c324fd67657ca54b0c336e954538ae2753 | 2022-05-24T10:05:15.000Z | [
"pytorch",
"lstm_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/lstm.CEBaB.causalm.None__None.3-class.exclusive.seed_42 | 1 | null | transformers | 32,199 | Entry not found |
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