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PSW/low_resource_percent1_max2swap_seed1 | 5a41a281c6a2c27a788119f391be6f8e69a3a64d | 2022-05-12T06:27:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_max2swap_seed1 | 1 | null | transformers | 31,800 | Entry not found |
PSW/low_resource_percent1_max2swap_seed27 | a95562fae23b38c4c2da2754ec69fb02175769df | 2022-05-12T06:40:35.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_max2swap_seed27 | 1 | null | transformers | 31,801 | Entry not found |
PSW/low_resource_percent10_min2swap_seed1 | d7563ff675308be7c7fec18e83e8aa7be1966074 | 2022-05-12T07:11:08.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_min2swap_seed1 | 1 | null | transformers | 31,802 | Entry not found |
PSW/low_resource_percent10_min2swap_seed27 | d9896007a9a965391b8004a2e78fc38b866fb90a | 2022-05-12T07:27:12.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_min2swap_seed27 | 1 | null | transformers | 31,803 | Entry not found |
SebastianS/mt5-finetuned-amazon-en-es-accelerate | 01f0dfcb3c541bd6018e009a92c8dfa201abf1ee | 2022-05-11T22:10:31.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SebastianS | null | SebastianS/mt5-finetuned-amazon-en-es-accelerate | 1 | null | transformers | 31,804 | Entry not found |
PSW/low_resource_percent10_max2swap_seed1 | 71c515c191fbe6a438ea6aff87415f6f00ebf4c3 | 2022-05-12T07:59:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_max2swap_seed1 | 1 | null | transformers | 31,805 | Entry not found |
PSW/low_resource_percent10_max2swap_seed27 | a2efd16d231b25ed304b5c50803016bee991e9c7 | 2022-05-12T08:15:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_max2swap_seed27 | 1 | null | transformers | 31,806 | Entry not found |
PSW/low_resource_percent10_max2swap_seed42 | 2c59a9ca1c935516dd357f5a91475d101fa98138 | 2022-05-12T08:31:53.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_max2swap_seed42 | 1 | null | transformers | 31,807 | Entry not found |
PSW/low_resource_percent20_min2swap_seed27 | 49b4d318220efa834905d703b6b95911a4cb50ec | 2022-05-12T09:12:00.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_min2swap_seed27 | 1 | null | transformers | 31,808 | Entry not found |
PSW/low_resource_percent20_max2swap_seed1 | 4d1956a081906723952177be0680606a7e9ad678 | 2022-05-12T09:53:46.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_max2swap_seed1 | 1 | null | transformers | 31,809 | Entry not found |
PSW/low_resource_percent20_max2swap_seed42 | e75f5910f82dce973c4020d98254e7e690daa886 | 2022-05-12T10:29:02.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_max2swap_seed42 | 1 | null | transformers | 31,810 | Entry not found |
tanviraumi/summary-note | d1c021c3f6c2b5c645221c8eef2997567ea50c7c | 2022-05-11T22:09:53.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | tanviraumi | null | tanviraumi/summary-note | 1 | null | transformers | 31,811 | ---
license: mit
---
|
enoriega/kw_pubmed_5000_0.000006 | 3c508600fdc2be988dd373cb6f1be8b4ccd1defb | 2022-05-12T09:50:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | enoriega | null | enoriega/kw_pubmed_5000_0.000006 | 1 | null | transformers | 31,812 | Entry not found |
SherlockGuo/distilbert-base-uncased-finetuned-squad | aaa4418820d68d665ff6b542d6cd76a8f3111de8 | 2022-05-12T19:32:44.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | SherlockGuo | null | SherlockGuo/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 31,813 | ---
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.
It achieves the following results on the evaluation set:
- Loss: 3.7677
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 4.1121 |
| No log | 2.0 | 126 | 3.8248 |
| No log | 3.0 | 189 | 3.7677 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
uhlenbeckmew/distilroberta-base-wiki | 7071f6d15cc942a3fe8bec5c46c88c0ec677f3c5 | 2022-05-12T07:51:34.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | uhlenbeckmew | null | uhlenbeckmew/distilroberta-base-wiki | 1 | null | transformers | 31,814 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-wiki
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. -->
# distilroberta-base-wiki
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0961
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4333 | 1.0 | 1223 | 2.1885 |
| 2.3107 | 2.0 | 2446 | 2.1508 |
| 2.2385 | 3.0 | 3669 | 2.0961 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
cocoshe/bert-base-chinese-finetune-5-trash-email | a2a59ccf7e681ddadc5ca0bbe9c543e4fab1f712 | 2022-05-12T07:35:56.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | cocoshe | null | cocoshe/bert-base-chinese-finetune-5-trash-email | 1 | null | transformers | 31,815 | ---
language: zh
---
# Based on bert-base-chinese
基于bert-base-chinese在`message80W`数据集（垃圾邮件二分类）上做了5个epoch的fine-tune
```python
# evaluate
with torch.no_grad():
model.eval()
eval_steps = 0
pred_list = []
label_list = []
for i, batch in enumerate(tqdm(test_loader)):
input_ids, attention_mask, label = batch
logits = model(input_ids, attention_mask)
pred_list += (torch.argmax(logits, dim=-1))
label_list += label
eval_steps += 1
```
80W数据，shuffled，8：3分train eval
下面是eval结果
|
bbaaaa/custom-resnet50d | bd5deae985f8a33c00410bfaa6f28d3c2d3d64a8 | 2022-05-12T07:57:14.000Z | [
"pytorch",
"resnet",
"transformers"
] | null | false | bbaaaa | null | bbaaaa/custom-resnet50d | 1 | null | transformers | 31,816 | Entry not found |
MagicalCat29/hotel_model | 14800e9f8656ead51d0998a4b8397f5fa22c0578 | 2022-05-18T08:29:47.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | MagicalCat29 | null | MagicalCat29/hotel_model | 1 | null | transformers | 31,817 | Entry not found |
huggingtweets/_is_is_are-newscollected | bb9390bb41f2566b1556109bb2743e785e5fa76e | 2022-05-12T13:31:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/_is_is_are-newscollected | 1 | null | transformers | 31,818 | ---
language: en
thumbnail: http://www.huggingtweets.com/_is_is_are-newscollected/1652362282720/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/1522032150358511616/83U7w6rG_400x400.jpg')">
</div>
<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/1422393503078920232/EWLgCOmZ_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">del co & angelicism01 滲み出るエロス</div>
<div style="text-align: center; font-size: 14px;">@_is_is_are-newscollected</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 del co & angelicism01 滲み出るエロス.
| Data | del co | angelicism01 滲み出るエロス |
| --- | --- | --- |
| Tweets downloaded | 364 | 79 |
| Retweets | 30 | 13 |
| Short tweets | 67 | 3 |
| Tweets kept | 267 | 63 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39vbf25o/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 @_is_is_are-newscollected's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/22o9cdjn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/22o9cdjn/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/_is_is_are-newscollected')
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)
|
MeshalAlamr/wav2vec2-xls-r-300m-ar-6 | a15e6ac7a1aef309cfe93457ab625411b1835b73 | 2022-05-17T03:23:06.000Z | [
"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-6 | 1 | null | transformers | 31,819 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ar-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-xls-r-300m-ar-6
This model is a fine-tuned version of [MeshalAlamr/wav2vec2-xls-r-300m-ar-6](https://huggingface.co/MeshalAlamr/wav2vec2-xls-r-300m-ar-6) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 78.2951
- Wer: 0.2040
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 85 | 75.3576 | 0.2131 |
| No log | 2.0 | 170 | 75.3215 | 0.2150 |
| No log | 3.0 | 255 | 75.5332 | 0.2201 |
| No log | 4.0 | 340 | 81.2835 | 0.2315 |
| 94.75 | 5.0 | 425 | 78.3768 | 0.2422 |
| 94.75 | 6.0 | 510 | 82.9389 | 0.2520 |
| 94.75 | 7.0 | 595 | 76.7272 | 0.2496 |
| 94.75 | 8.0 | 680 | 79.9325 | 0.2506 |
| 94.75 | 9.0 | 765 | 82.2568 | 0.2507 |
| 124.0193 | 10.0 | 850 | 78.7011 | 0.2415 |
| 124.0193 | 11.0 | 935 | 81.2829 | 0.2396 |
| 124.0193 | 12.0 | 1020 | 77.2370 | 0.2357 |
| 124.0193 | 13.0 | 1105 | 77.4057 | 0.2347 |
| 124.0193 | 14.0 | 1190 | 74.4764 | 0.2271 |
| 112.7824 | 15.0 | 1275 | 78.7320 | 0.2355 |
| 112.7824 | 16.0 | 1360 | 79.0120 | 0.2294 |
| 112.7824 | 17.0 | 1445 | 82.3663 | 0.2240 |
| 112.7824 | 18.0 | 1530 | 79.2765 | 0.2236 |
| 98.8702 | 19.0 | 1615 | 78.1527 | 0.2242 |
| 98.8702 | 20.0 | 1700 | 75.7842 | 0.2198 |
| 98.8702 | 21.0 | 1785 | 78.2980 | 0.2217 |
| 98.8702 | 22.0 | 1870 | 79.3180 | 0.2168 |
| 98.8702 | 23.0 | 1955 | 77.7381 | 0.2155 |
| 84.537 | 24.0 | 2040 | 78.1512 | 0.2131 |
| 84.537 | 25.0 | 2125 | 80.4068 | 0.2116 |
| 84.537 | 26.0 | 2210 | 75.5718 | 0.2075 |
| 84.537 | 27.0 | 2295 | 78.4438 | 0.2078 |
| 84.537 | 28.0 | 2380 | 79.6891 | 0.2086 |
| 74.4149 | 29.0 | 2465 | 77.9115 | 0.2069 |
| 74.4149 | 30.0 | 2550 | 78.2951 | 0.2040 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
vives/distilbert-base-uncased-finetuned-imdb | 9b494fe2266c40d5d1fd32ecb6a46b268b41e1a6 | 2022-05-12T19:31:53.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | vives | null | vives/distilbert-base-uncased-finetuned-imdb | 1 | null | transformers | 31,820 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Dizzykong/gpt2-large-quests-5 | d21d89ea3607416ac47d50d53cc0481bad8af509 | 2022-05-13T23:50:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Dizzykong | null | Dizzykong/gpt2-large-quests-5 | 1 | null | transformers | 31,821 | Entry not found |
Dedemg1988/DialoGPT-small-michaelscott | b6a336e764b6539fa8952bee73e5dd7b788d40b1 | 2022-05-12T18:26:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Dedemg1988 | null | Dedemg1988/DialoGPT-small-michaelscott | 1 | null | transformers | 31,822 | ---
tags:
- conversational
---
# Michael Scott DialoGPT Model |
subhasisj/es-TAPT-MLM-MiniLM | e3a85a0679cd876f737336f1c213dc429d179158 | 2022-05-12T20:21:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | subhasisj | null | subhasisj/es-TAPT-MLM-MiniLM | 1 | null | transformers | 31,823 | ---
tags:
- generated_from_trainer
model-index:
- name: es-TAPT-MLM-MiniLM
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. -->
# es-TAPT-MLM-MiniLM
This model is a fine-tuned version of [subhasisj/MiniLMv2-qa-encoder](https://huggingface.co/subhasisj/MiniLMv2-qa-encoder) 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: 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 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
subhasisj/de-finetuned-squad-qa-minilmv2-16 | d6a584c5855e19fba3b833c6567d78466a8bc078 | 2022-05-12T22:27:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | subhasisj | null | subhasisj/de-finetuned-squad-qa-minilmv2-16 | 1 | null | transformers | 31,824 | ---
tags:
- generated_from_trainer
model-index:
- name: de-finetuned-squad-qa-minilmv2-16
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. -->
# de-finetuned-squad-qa-minilmv2-16
This model is a fine-tuned version of [subhasisj/de-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/de-TAPT-MLM-MiniLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5756
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6022 | 1.0 | 671 | 2.0770 |
| 1.9783 | 2.0 | 1342 | 1.6511 |
| 1.4059 | 3.0 | 2013 | 1.5939 |
| 1.2989 | 4.0 | 2684 | 1.5772 |
| 1.2522 | 5.0 | 3355 | 1.5756 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
aajrami/bert-sr-base | c43b4b7aafa5abf92880489dd4b7b574f5bf73ed | 2022-06-01T11:52:00.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.10415",
"transformers",
"bert",
"license:cc-by-4.0"
] | feature-extraction | false | aajrami | null | aajrami/bert-sr-base | 1 | null | transformers | 31,825 | ---
tags:
- bert
license: cc-by-4.0
---
## bert-sr-base
is a BERT base Language Model with a **shuffle + random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://arxiv.org/abs/2203.10415)
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{alajrami2022does,
title={How does the pre-training objective affect what large language models learn about linguistic properties?},
author={Alajrami, Ahmed and Aletras, Nikolaos},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={131--147},
year={2022}
}
``` |
aajrami/bert-fc-base | 91e1a793eb910f576d852ef7a739c4ed93cb13c7 | 2022-06-01T11:52:44.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.10415",
"transformers",
"bert",
"license:cc-by-4.0"
] | feature-extraction | false | aajrami | null | aajrami/bert-fc-base | 1 | null | transformers | 31,826 | ---
tags:
- bert
license: cc-by-4.0
---
## bert-fc-base
is a BERT base Language Model with a **first character** prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://arxiv.org/abs/2203.10415)
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{alajrami2022does,
title={How does the pre-training objective affect what large language models learn about linguistic properties?},
author={Alajrami, Ahmed and Aletras, Nikolaos},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={131--147},
year={2022}
}
``` |
aajrami/bert-rand-base | d9ebdee44ea1d750be287de49c53deed4d761be7 | 2022-06-01T11:53:15.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.10415",
"transformers",
"bert",
"license:cc-by-4.0"
] | feature-extraction | false | aajrami | null | aajrami/bert-rand-base | 1 | null | transformers | 31,827 | ---
tags:
- bert
license: cc-by-4.0
---
## bert-rand-base
is a BERT base Language Model with a **random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://arxiv.org/abs/2203.10415)
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{alajrami2022does,
title={How does the pre-training objective affect what large language models learn about linguistic properties?},
author={Alajrami, Ahmed and Aletras, Nikolaos},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={131--147},
year={2022}
}
``` |
kabelomalapane/en_zu_ukuxhumana_model | 1aa33534cf94586893f18afe8bbe66e4790e8dd1 | 2022-05-13T06:09:53.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | kabelomalapane | null | kabelomalapane/en_zu_ukuxhumana_model | 1 | null | transformers | 31,828 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en_zu_ukuxhumana_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# en_zu_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0772
- Bleu: 7.6322
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lilitket/20220513-044812 | cc5e299e2dbad77ec0537613cf51609e5c30fdbd | 2022-05-13T05:06:02.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220513-044812 | 1 | null | transformers | 31,829 | Entry not found |
lilitket/20220513-050608 | fbac826eec4efae8dd30992ad7787f2b00b632bb | 2022-05-13T04:57:21.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220513-050608 | 1 | null | transformers | 31,830 | Entry not found |
anas-awadalla/roberta-large-data-seed-0 | 4d8c0184f5d469f2da39fc1285627722b3273a9b | 2022-05-13T04:07:24.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-data-seed-0 | 1 | null | transformers | 31,831 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-data-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-data-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Khalsuu/filipino-wav2vec2-l-xls-r-300m-official | 06e33a5630c543cffb68a011fef6eea64dcc09d8 | 2022-05-13T05:58:50.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:filipino_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Khalsuu | null | Khalsuu/filipino-wav2vec2-l-xls-r-300m-official | 1 | null | transformers | 31,832 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: filipino-wav2vec2-l-xls-r-300m-official
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. -->
# filipino-wav2vec2-l-xls-r-300m-official
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4672
- Wer: 0.2922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3671 | 2.09 | 400 | 0.5584 | 0.5987 |
| 0.48 | 4.19 | 800 | 0.4244 | 0.4195 |
| 0.2796 | 6.28 | 1200 | 0.3742 | 0.3765 |
| 0.1916 | 8.38 | 1600 | 0.4291 | 0.3667 |
| 0.1463 | 10.47 | 2000 | 0.3745 | 0.3415 |
| 0.1165 | 12.57 | 2400 | 0.4472 | 0.3407 |
| 0.0955 | 14.66 | 2800 | 0.4269 | 0.3290 |
| 0.0823 | 16.75 | 3200 | 0.4608 | 0.3475 |
| 0.0709 | 18.85 | 3600 | 0.4706 | 0.3281 |
| 0.0603 | 20.94 | 4000 | 0.4380 | 0.3183 |
| 0.0527 | 23.04 | 4400 | 0.4473 | 0.3067 |
| 0.0449 | 25.13 | 4800 | 0.4550 | 0.3029 |
| 0.041 | 27.23 | 5200 | 0.4671 | 0.3020 |
| 0.0358 | 29.32 | 5600 | 0.4672 | 0.2922 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
eat-great-food/t5-efficient-tiny-d3st-t5-efficient-tiny | 42992f7edc3bfb47344b36d8aaa971b88919c5ef | 2022-05-13T04:42:32.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | eat-great-food | null | eat-great-food/t5-efficient-tiny-d3st-t5-efficient-tiny | 1 | null | transformers | 31,833 | Entry not found |
anas-awadalla/roberta-large-data-seed-2 | c2678c0b3150dfb8a370a9621e14b7ef55bed8b7 | 2022-05-14T03:54:46.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-data-seed-2 | 1 | null | transformers | 31,834 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-data-seed-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. -->
# roberta-large-data-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-data-seed-4 | a1497606b3855c18c4a2d4853d721b4081430f56 | 2022-05-13T06:24:05.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-data-seed-4 | 1 | null | transformers | 31,835 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-data-seed-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. -->
# roberta-large-data-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jasonyim2/xlm-roberta-base-finetuned-panx-de | 1838d073891a67cf315ca95aecabdc13c16b4b96 | 2022-05-13T06:04:43.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jasonyim2 | null | jasonyim2/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,836 | ---
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 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Milanmg/xlm-roberta-large | 81351339980d7331f1e64a693743ed1bc83b69d4 | 2022-05-13T06:49:55.000Z | [
"pytorch",
"jax",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Milanmg | null | Milanmg/xlm-roberta-large | 1 | null | transformers | 31,837 | Entry not found |
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_kldiv | c89ae8e750b5cd089fc2aeea3d6d508f42cd240b | 2022-05-13T09:50:26.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | AnonymousSub | null | AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_kldiv | 1 | null | transformers | 31,838 | Entry not found |
PSW/cnndm_0.1percent_maxsimins_seed1 | e5d7a93ddff360c88330f8fab6120bcde37e6ce7 | 2022-05-15T21:49:43.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.1percent_maxsimins_seed1 | 1 | null | transformers | 31,839 | Entry not found |
PSW/cnndm_0.1percent_randomsimins_seed1 | aae26c8456c1833f33316a1941f765d24414b841 | 2022-05-16T01:08:36.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.1percent_randomsimins_seed1 | 1 | null | transformers | 31,840 | Entry not found |
SreyanG-NVIDIA/bert-base-cased-finetuned-squad | 177e1edd62052a8c1e81e49e7f4a00ff3bead655 | 2022-05-16T08:39:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | SreyanG-NVIDIA | null | SreyanG-NVIDIA/bert-base-cased-finetuned-squad | 1 | null | transformers | 31,841 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0848
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0337 | 1.0 | 5546 | 1.0150 |
| 0.7546 | 2.0 | 11092 | 1.0015 |
| 0.5537 | 3.0 | 16638 | 1.0848 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davincilee/closure_system_door_inne-roberta-base | 187cd898e23cb07a51768ba338c6b730b4c6ac47 | 2022-05-13T14:24:57.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | Davincilee | null | Davincilee/closure_system_door_inne-roberta-base | 1 | null | transformers | 31,842 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: closure_system_door_inne-roberta-base
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. -->
# closure_system_door_inne-roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6038
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3302 | 1.0 | 3 | 1.6837 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
anas-awadalla/roberta-large-initialization-seed-0 | 98f112d14ac735011f5da126aa9189dfd3ac9f32 | 2022-05-13T16:46:52.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-initialization-seed-0 | 1 | null | transformers | 31,843 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-initialization-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-initialization-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
PSW/cnndm_0.1percent_min2swap_seed1 | 6297b6bd531f7a35cb4ad2d92d0415e028515f2d | 2022-05-16T07:47:36.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.1percent_min2swap_seed1 | 1 | null | transformers | 31,844 | Entry not found |
PSW/cnndm_0.1percent_max2swap_seed1 | 1012aa699de947f7f76eee749bae85bab990f11a | 2022-05-16T11:07:58.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.1percent_max2swap_seed1 | 1 | null | transformers | 31,845 | Entry not found |
anas-awadalla/roberta-large-initialization-seed-2 | 395f26a5a817520721a466c87796e219f519e513 | 2022-05-13T18:58:43.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-initialization-seed-2 | 1 | null | transformers | 31,846 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-initialization-seed-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. -->
# roberta-large-initialization-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
lilitket/20220513-212939 | e7037cd1b15662ad5fab34f92f10d8180cffc7c2 | 2022-05-14T04:20:59.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220513-212939 | 1 | null | transformers | 31,847 | Entry not found |
PSW/cnndm_0.5percent_minsimdel_seed1 | 7e1bdae241a1d1a58dc0d12524253daa13e06f5b | 2022-05-16T17:54:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_minsimdel_seed1 | 1 | null | transformers | 31,848 | Entry not found |
anas-awadalla/roberta-large-initialization-seed-4 | aab8fcf232cb5ed672c8ed288570abeed6604ccc | 2022-05-13T21:07:51.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-initialization-seed-4 | 1 | null | transformers | 31,849 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-initialization-seed-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. -->
# roberta-large-initialization-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
PSW/cnndm_0.5percent_maxsimdel_seed1 | f47d5c8febdf4fe43d100f61f8a3e555b0662f1e | 2022-05-16T21:26:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_maxsimdel_seed1 | 1 | null | transformers | 31,850 | Entry not found |
SebastianS/codeparrot-ds | d8399ea7694bfde780a038db5d4d96334ee859c4 | 2022-05-13T22:28:22.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | SebastianS | null | SebastianS/codeparrot-ds | 1 | null | transformers | 31,851 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
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: 2.4905
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7149 | 0.85 | 1000 | 2.4905 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
PSW/cnndm_0.5percent_randomsimdel_seed1 | f52767130c20dcf96c7b932628bc647952629970 | 2022-05-17T00:59:45.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_randomsimdel_seed1 | 1 | null | transformers | 31,852 | Entry not found |
SNCannon/DialoGPT-medium-merc | 6c813cc5d20ea923e2bd9a5abd45f3a0dcb60435 | 2022-05-13T23:20:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | SNCannon | null | SNCannon/DialoGPT-medium-merc | 1 | null | transformers | 31,853 | ---
tags:
- conversational
---
# Corroded MercBot DialoGPT Model |
PSW/cnndm_0.5percent_minsimins_seed1 | cb1e05d84b4c2ea4b909f76e25957debb9eca0ab | 2022-05-17T04:33:24.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_minsimins_seed1 | 1 | null | transformers | 31,854 | Entry not found |
SebastianS/codeparrot-ds-accelerate | c992e9427ad6c85114418be94e8aa764a00934dd | 2022-05-14T01:28:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | SebastianS | null | SebastianS/codeparrot-ds-accelerate | 1 | null | transformers | 31,855 | Entry not found |
PSW/cnndm_0.5percent_maxsimins_seed1 | 6496cdf224495c28f6fe4dd5c8b1467f3d396dd8 | 2022-05-17T08:06:01.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_0.5percent_maxsimins_seed1 | 1 | null | transformers | 31,856 | Entry not found |
anas-awadalla/spanbert-base-finetuned-squad-r3f | fa3262da2e61ceeb21ca9e1b565a597da3158497 | 2022-05-14T14:00:24.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-finetuned-squad-r3f | 1 | null | transformers | 31,857 | Entry not found |
anas-awadalla/bert-base-cased-finetuned-squad-r3f | f4b594bd087b9b5fb3df653228e99eb4fb5f992d | 2022-05-14T08:07:36.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/bert-base-cased-finetuned-squad-r3f | 1 | null | transformers | 31,858 | Entry not found |
anas-awadalla/roberta-base-finetuned-squad-r3f | ef64d9435ef44354eda7239966371f5db85dd0e6 | 2022-05-14T11:00:05.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-base-finetuned-squad-r3f | 1 | null | transformers | 31,859 | Entry not found |
PSW/cnndm_10percent_minsimdel_seed1 | e348b65ef19a22b8dcf3842fbf1dc62ab54f317d | 2022-05-18T07:08:02.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_10percent_minsimdel_seed1 | 1 | null | transformers | 31,860 | Entry not found |
PSW/cnndm_10percent_maxsimdel_seed1 | e37d115ec7f7ac3fa42da5c47994a9e1f1f65485 | 2022-05-14T12:23:58.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_10percent_maxsimdel_seed1 | 1 | null | transformers | 31,861 | Entry not found |
lilitket/20220514-171236 | d8ae787b925c1932affccb2562fdb26bc0dc07d3 | 2022-05-15T01:57:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220514-171236 | 1 | null | transformers | 31,862 | Entry not found |
huggingtweets/vrsoloviev | 504bee05e4564e4764114501be74aa33021e8125 | 2022-05-14T13:25:22.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/vrsoloviev | 1 | null | transformers | 31,863 | ---
language: en
thumbnail: http://www.huggingtweets.com/vrsoloviev/1652534655103/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/1170975520458203136/4eDVAZZa_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">Vladimir Soloviev</div>
<div style="text-align: center; font-size: 14px;">@vrsoloviev</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 Vladimir Soloviev.
| Data | Vladimir Soloviev |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 9 |
| Short tweets | 29 |
| Tweets kept | 3212 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/elfi2jwn/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 @vrsoloviev's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2m2arnt6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2m2arnt6/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/vrsoloviev')
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)
|
PSW/cnndm_10percent_randomsimdel_seed1 | 34e336fa328cc20e868d2436006ebad3cfd47391 | 2022-05-14T15:36:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/cnndm_10percent_randomsimdel_seed1 | 1 | null | transformers | 31,864 | Entry not found |
mubikan/xlm-roberta-base-finetuned-panx-de | 6c10d1c4d04adc25f96852f98f4e8ac05ef305bb | 2022-05-15T11:48:08.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | mubikan | null | mubikan/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,865 | ---
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: 0.8588964027959312
---
<!-- 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: 0.1383
- F1: 0.8589
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2631 | 1.0 | 525 | 0.1596 | 0.8218 |
| 0.1296 | 2.0 | 1050 | 0.1353 | 0.8479 |
| 0.0821 | 3.0 | 1575 | 0.1383 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_42 | 81503b1d3a4dc16846d419f9973302640fb39051 | 2022-05-14T17:25:19.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_42 | 1 | null | transformers | 31,866 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_77 | aaf69574875220b7722574d4d8c3a917f7c0cbf5 | 2022-05-14T17:35:14.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_77 | 1 | null | transformers | 31,867 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_88 | cf22be2a5524f740875d5c1260539878b410790c | 2022-05-14T17:40:05.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_88 | 1 | null | transformers | 31,868 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_99 | 37f2b93d61001c5a31360abf82c6dc207cb20fb2 | 2022-05-14T17:44:57.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.exclusive.seed_99 | 1 | null | transformers | 31,869 | Entry not found |
huggingtweets/spacex | 779d9d0ee36d354f9ff5a18ea7bb24aa8419b411 | 2022-05-14T18:02:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/spacex | 1 | null | transformers | 31,870 | ---
language: en
thumbnail: http://www.huggingtweets.com/spacex/1652551333667/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/1082744382585856001/rH_k3PtQ_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">SpaceX</div>
<div style="text-align: center; font-size: 14px;">@spacex</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 SpaceX.
| Data | SpaceX |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 539 |
| Short tweets | 157 |
| Tweets kept | 2554 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/562aigw4/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 @spacex's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3b58vg41) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3b58vg41/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/spacex')
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)
|
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_42 | 6d2bab5dc08484809eb3b5d1fbd34324e870c545 | 2022-05-14T17:59:35.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_42 | 1 | null | transformers | 31,871 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_66 | cee15c54b46ad234f385b7ba48102163ad1e072c | 2022-05-14T18:04:21.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_66 | 1 | null | transformers | 31,872 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_77 | d1131e2b7e6b3d01e5c28f4ea0543894b758198d | 2022-05-14T18:09:16.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_77 | 1 | null | transformers | 31,873 | Entry not found |
CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_88 | a14ea19bc1ea9a8f3ecfb8fe1fd8b12488b0b13f | 2022-05-14T18:14:30.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB-challenge.sa.2-class.inclusive.seed_88 | 1 | null | transformers | 31,874 | Entry not found |
anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-0 | d3b844610d162e0a3cdd52ed34b46aca65b8da89 | 2022-05-14T19:26:10.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-0 | 1 | null | transformers | 31,875 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-16-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-large-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-16-finetuned-squad-seed-2 | e69eacea6b4f262ece3845d913fddaea9f1e26f7 | 2022-05-14T19:32:24.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-16-finetuned-squad-seed-2 | 1 | null | transformers | 31,876 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-16-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-2 | 85325f605c24450c7b61f12f22ae363763952a05 | 2022-05-14T19:36:09.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-2 | 1 | null | transformers | 31,877 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-16-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-4 | 8ac859561ad869a69ac78baa7b225cbcf7f559d4 | 2022-05-14T19:46:07.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-16-finetuned-squad-seed-4 | 1 | null | transformers | 31,878 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-16-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-0 | 14b9958dc14381063f00d99aa194b72298797c21 | 2022-05-14T19:53:09.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-0 | 1 | null | transformers | 31,879 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-32-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-32-finetuned-squad-seed-0 | c7789c05c7d2047715cd1592ad48ebb7552982f5 | 2022-05-14T19:56:59.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-32-finetuned-squad-seed-0 | 1 | null | transformers | 31,880 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-32-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-large-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-2 | 8193f064cc285fa93a9159089a69ae045daa0803 | 2022-05-14T20:03:31.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-2 | 1 | null | transformers | 31,881 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-32-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-32-finetuned-squad-seed-2 | c3d7bde66dbb33e79e7e3d54beed9028cc4f01b9 | 2022-05-14T20:07:26.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-32-finetuned-squad-seed-2 | 1 | null | transformers | 31,882 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-32-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-4 | 9d6259f5c3812e3bd00ae5d06f4f4a6a0f541f4f | 2022-05-14T20:13:53.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-32-finetuned-squad-seed-4 | 1 | null | transformers | 31,883 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-32-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-0 | 3e6f70073ea2ead6b5bbcfdf92ba50ec10092355 | 2022-05-14T20:28:59.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-0 | 1 | null | transformers | 31,884 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-64-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-large-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-64-finetuned-squad-seed-2 | 5df711b6fcfec52fa100dfd17b570f1419d4eeca | 2022-05-14T20:35:00.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-64-finetuned-squad-seed-2 | 1 | null | transformers | 31,885 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-64-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-64-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-2 | 4d5efbc83b1e0568d9c39755c0e87b8b7d682ea5 | 2022-05-14T20:39:24.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-2 | 1 | null | transformers | 31,886 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-64-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-64-finetuned-squad-seed-2
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-64-finetuned-squad-seed-4 | 3a37390f7785120c2ec8f78e3fbb8fe393925cd6 | 2022-05-14T20:46:57.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-64-finetuned-squad-seed-4 | 1 | null | transformers | 31,887 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-64-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-4 | 233a2811e8529c4fb1c8f2a3bc9e5c638ad2de02 | 2022-05-14T20:49:53.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-64-finetuned-squad-seed-4 | 1 | null | transformers | 31,888 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-64-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-0 | 21e51e8a9e2f887ad6d632e2498bdf88a1eae2f4 | 2022-05-14T21:00:55.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-0 | 1 | null | transformers | 31,889 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-128-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-large-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-2 | 2507aee0c1fe358804904e3e1b43914c077a19eb | 2022-05-14T21:08:49.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-2 | 1 | null | transformers | 31,890 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-128-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-2 | 0f96fd8f238084d963e53a2efb5b5616ee2d5c0b | 2022-05-14T21:14:58.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-128-finetuned-squad-seed-2 | 1 | null | transformers | 31,891 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-128-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-4 | 88c9bba9380ee7126179f8d5aa0eb7d615f3eae6 | 2022-05-14T21:28:38.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-128-finetuned-squad-seed-4 | 1 | null | transformers | 31,892 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-128-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-0 | d10176044b9678920ca7bcc1cafa85ecfcbefbf0 | 2022-05-14T21:40:52.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-0 | 1 | null | transformers | 31,893 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-256-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-large-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-0 | 551f50da446e1efbf0ae3e884118c615af31d254 | 2022-05-14T21:40:29.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-0 | 1 | null | transformers | 31,894 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-256-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
prashanth/mbart-large-cc25-ind_finetun-hi-to-en | 02bb99aaea341ec7f3fa684d45cdfaf44b1b143b | 2022-05-14T22:03:05.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:hindi_english_machine_translation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | prashanth | null | prashanth/mbart-large-cc25-ind_finetun-hi-to-en | 1 | null | transformers | 31,895 | ---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
metrics:
- bleu
model-index:
- name: mbart-large-cc25-ind_finetun-hi-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: hindi_english_machine_translation
type: hindi_english_machine_translation
args: hi-en
metrics:
- name: Bleu
type: bleu
value: 15.9135
---
<!-- 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. -->
# mbart-large-cc25-ind_finetun-hi-to-en
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the hindi_english_machine_translation dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4042
- Bleu: 15.9135
- Gen Len: 70.155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 2.3854 | 1.0 | 620 | 1.4042 | 15.9135 | 70.155 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-2 | d251ea1269ab55b82b6853a0e3df26f7601b2839 | 2022-05-14T21:52:18.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-2 | 1 | null | transformers | 31,896 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-256-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-2 | d05a49267000c564ac620302d16b104990e0f001 | 2022-05-14T21:51:44.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-256-finetuned-squad-seed-2 | 1 | null | transformers | 31,897 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-256-finetuned-squad-seed-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. -->
# roberta-large-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-4 | 8ee2b4dcbf66cad3ef928749dc805c57a877d500 | 2022-05-14T22:03:24.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-4 | 1 | null | transformers | 31,898 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-256-finetuned-squad-seed-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. -->
# splinter-large-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-few-shot-k-512-finetuned-squad-seed-0 | 3df538bf60ba8ebee03ea572b4613bc8b3d99acb | 2022-05-14T22:17:30.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/roberta-large-few-shot-k-512-finetuned-squad-seed-0 | 1 | null | transformers | 31,899 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-few-shot-k-512-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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