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int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
AnonymousSub/SR_specter | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
} | 5 | null | ---
license: mit
tags:
- nowcasting
- forecasting
- timeseries
- remote-sensing
---
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full connected layer. This is joined by
other data inputs like
- pv yield
- time variables
Then there ~4 fully connected layers which end up forecasting the
pv yield / gsp into the future
## Intended uses & limitations
Forecasting short term PV power for different regions and nationally in the UK
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
Training data is EUMETSAT RSS imagery over the UK, on-the-ground PV data, and NWP predictions.
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
AnonymousSub/T5_pubmedqa_question_generation | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"max_length": 200,
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"num_beams": 4,
"prefix": "summarize: "
},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | 2022-09-30T14:29:25Z | ---
language:
- is
- en
- multilingual
tags:
- translation
inference:
parameters:
src_lang: is_IS
tgt_lang: en_XX
decoder_start_token_id: 2
max_length: 512
widget:
- text: Einu sinni átti ég hest. Hann var svartur og hvítur.
---
# mBART based translation model
This model was trained to translate multiple sentences at once, compared to one sentence at a time.
It will occasionally combine sentences or add an extra sentence.
This is the same model as are provided on CLARIN: https://repository.clarin.is/repository/xmlui/handle/20.500.12537/278
|
AnonymousSub/bert-base-uncased_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"prefix": null
}
}
} | 30 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- librispeech_asr
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="librispeech" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-librispeech" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="300" \
--final_generation_num_beams="12" \
--generation_length_penalty="1.6" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 4 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- mozilla-foundation/common_voice_9_0
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="common_voice" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-common-voice" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="200" \
--generation_num_beams="14" \
--generation_length_penalty="1.2" \
--max_eval_duration_in_seconds="20" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
} | 1 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- LIUM/tedlium
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-tedlium" \
--dataset_config_name="tedlium" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-tedlium" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="250" \
--final_generation_num_beams="12" \
--generation_length_penalty="1.5" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"prefix": null
}
}
} | 6 | 2022-09-30T14:39:11Z | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- facebook/voxpopuli
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="voxpopuli" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-voxpopuli" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="1" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="10001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="225" \
--final_generation_num_beams="5" \
--generation_length_penalty="0.8" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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}
}
} | 4 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- speechcolab/gigaspeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="gigaspeech" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-gigaspeech" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="200" \
--final_generation_num_beams="14" \
--generation_length_penalty="1.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_snips | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"translation_en_to_ro": {
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}
}
} | 5 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- kensho/spgispeech
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="spgispeech" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-spgispeech" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="225" \
--final_generation_num_beams="14" \
--generation_length_penalty="1.6" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 2 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- revdotcom/earnings22
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="earnings22" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-earnings22" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="25" \
--max_steps="50000" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--generation_length_penalty="1.2" \
--final_generation_max_length="200" \
--final_generation_num_beams="5" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/bert_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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"prefix": null
}
}
} | 1 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- edinburghcstr/ami
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="ami" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-ami" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="225" \
--final_generation_num_beams="5" \
--generation_length_penalty="1.4" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/cline-papers-biomed-0.618 | [
"pytorch",
"roberta",
"transformers"
]
| null | {
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"LecbertForPreTraining"
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} | 2 | null | ---
license: mit
---
### mate on Stable Diffusion via Dreambooth
#### model by machinelearnear
This your the Stable Diffusion model fine-tuned the mate concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks mate**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AnonymousSub/cline-s10-SR | []
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}
} | 0 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/switchboard
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="switchboard" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-switchboard" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="2" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="260" \
--final_generation_num_beams="5" \
--generation_length_penalty="0.8" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/cline_emanuals | [
"pytorch",
"roberta",
"transformers"
]
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}
} | 3 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/chime-4
---
To reproduce this run, execute:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_seq2seq.py \
--dataset_name="esb/datasets" \
--model_name_or_path="esb/wav2vec2-aed-pretrained" \
--dataset_config_name="chime4" \
--output_dir="./" \
--wandb_name="wav2vec2-aed-chime4" \
--wandb_project="wav2vec2-aed" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="25" \
--max_steps="50001" \
--eval_steps="10000" \
--save_steps="10000" \
--generation_max_length="40" \
--generation_num_beams="1" \
--final_generation_max_length="250" \
--final_generation_num_beams="5" \
--generation_length_penalty="0.6" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--hidden_dropout="0.2" \
--activation_dropout="0.2" \
--feat_proj_dropout="0.2" \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--predict_with_generate \
--do_eval \
--do_train \
--do_predict \
--push_to_hub \
--use_auth_token
```
|
AnonymousSub/cline_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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}
} | 27 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: stbl_clinical_bert_ft_rs7
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. -->
# stbl_clinical_bert_ft_rs7
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0848
- F1: 0.9208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2755 | 1.0 | 101 | 0.0986 | 0.8484 |
| 0.0655 | 2.0 | 202 | 0.0780 | 0.8873 |
| 0.0299 | 3.0 | 303 | 0.0622 | 0.9047 |
| 0.0145 | 4.0 | 404 | 0.0675 | 0.9110 |
| 0.0097 | 5.0 | 505 | 0.0706 | 0.9141 |
| 0.0057 | 6.0 | 606 | 0.0753 | 0.9174 |
| 0.0032 | 7.0 | 707 | 0.0755 | 0.9182 |
| 0.0024 | 8.0 | 808 | 0.0835 | 0.9219 |
| 0.0014 | 9.0 | 909 | 0.0838 | 0.9197 |
| 0.0013 | 10.0 | 1010 | 0.0838 | 0.9204 |
| 0.0009 | 11.0 | 1111 | 0.0850 | 0.9183 |
| 0.0009 | 12.0 | 1212 | 0.0848 | 0.9208 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 4 | null | ---
license: mit
---
# Warning: Heavy Overfitting #
# Description
Trainer: Hank
Demiurge from Overlord
# Dataset
>Training: 6 images
>Regularization: 20 images
# Info
>Model Used: Waifu Diffusion 1.2
>Steps: 4000
>Keyword: Demiurge (Use this in the prompt)
>Class Phrase: anime_man_slick_black_hair_with_glasses
 |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
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}
} | 3 | null | ---
language:
- en
- ja
tags:
- nllb
license: cc-by-nc-4.0
---
# NLLB 1.3B fine-tuned on Japanese to English Light Novel translation
This model was fine-tuned on light and web novel for Japanese to English translation.
It can translate sentences and paragraphs up to 512 tokens.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("thefrigidliquidation/nllb-jaen-1.3B-lightnovels")
model = AutoModelForSeq2SeqLM.from_pretrained("thefrigidliquidation/nllb-jaen-1.3B-lightnovels")
generated_tokens = model.generate(
**inputs,
forced_bos_token_id=tokenizer.lang_code_to_id[tokenizer.tgt_lang],
max_new_tokens=1024,
no_repeat_ngram_size=6,
).cpu()
translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
```
Generating with diverse beam search seems to work best. Add the following to `model.generate`:
```python
num_beams=8,
num_beam_groups=4,
do_sample=False,
```
## Glossary
You can provide up to 10 custom translations for nouns and character names at runtime. To do so, surround the Japanese term with term tokens. Prefix the word with one of `<t0>, <t1>, ..., <t9>` and suffix the word with `</t>`. The term will be translated as the prefix term token which can then be string replaced.
For example, in `マイン、ルッツが迎えに来たよ` if you wish to have `マイン` translated as `Myne` you would replace `マイン` with `<t0>マイン</t>`. The model will translate `<t0>マイン</t>、ルッツが迎えに来たよ` as `<t0>, Lutz is here to pick you up.` Then simply do a string replacement on the output, replacing `<t0>` with `Myne`.
## Honorifics
You can force the model to generate or ignore honorifics.
```python
# default, the model decides whether to use honorifics
tokenizer.tgt_lang = "jpn_Jpan"
# no honorifics, the model is discouraged from using honorifics
tokenizer.tgt_lang = "zsm_Latn"
# honorifics, the model is encouraged to use honorifics
tokenizer.tgt_lang = "zul_Latn"
```
|
AnonymousSub/unsup-consert-base_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
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}
} | 2 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="FIT17/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/unsup-consert-emanuals | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
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} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="FIT17/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSubmission/pretrained-model-1 | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 275.78 +/- 16.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Anonymreign/savagebeta | []
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}
} | 0 | null | ---
license: mit
---
### ChairTest on Stable Diffusion via Dreambooth
#### model by dadosdq
This your the Stable Diffusion model fine-tuned the ChairTest concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **ChA1r**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"RobertaForSequenceClassification"
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}
} | 30 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: microsoft/fluentui-emoji
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# emoji-diffusion
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `microsoft/fluentui-emoji` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: False
### Training results
📈 [TensorBoard logs](https://huggingface.co/rycont/emoji-diffusion/tensorboard?#scalars)
|
ArashEsk95/bert-base-uncased-finetuned-cola | []
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} | 0 | 2022-10-01T13:23:05Z | ---
license: mit
---
### Joseph Russel Ammen on Stable Diffusion via Dreambooth
#### model by wallowbitz
This your the Stable Diffusion model fine-tuned the Joseph Russel Ammen concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **Joseph Russel Ammen**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
ArashEsk95/bert-base-uncased-finetuned-sst2 | []
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} | 0 | 2022-10-01T13:24:06Z | ---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-nftfreaks-nftgirl/1664630772232/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/1572573363255525377/Xz3fufYY_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/1524408283674591232/ZcdTVEPl_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/1384551299681714177/fHRGvDJR_400x400.jpg')">
</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">Elon Musk & NFT Freaks 🗝🏰🦸🏿♂️ & NFTGirl 🖼</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-nftfreaks-nftgirl</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 Elon Musk & NFT Freaks 🗝🏰🦸🏿♂️ & NFTGirl 🖼.
| Data | Elon Musk | NFT Freaks 🗝🏰🦸🏿♂️ | NFTGirl 🖼 |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3247 | 2210 |
| Retweets | 121 | 1753 | 298 |
| Short tweets | 984 | 306 | 395 |
| Tweets kept | 2095 | 1188 | 1517 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aevkd35/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 @elonmusk-nftfreaks-nftgirl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/al5jjb8v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/al5jjb8v/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/elonmusk-nftfreaks-nftgirl')
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)
|
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} | 0 | 2022-10-01T14:28:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets: din0s/msmarco-nlgen
model-index:
- name: t5-base-msmarco-nlgen-cb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-msmarco-nlgen-cb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [MS MARCO Natural Language Generation](https://huggingface.co/datasets/din0s/msmarco-nlgen) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0571
- Rougelsum: 24.7427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 2.1393 | 0.26 | 2500 | 2.1099 | 24.5028 |
| 2.1006 | 0.52 | 5000 | 2.0739 | 24.6017 |
| 2.0694 | 0.78 | 7500 | 2.0571 | 24.7427 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AriakimTaiyo/kumiko | []
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} | 0 | 2022-10-01T15:02:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9415
- name: F1
type: f1
value: 0.9414702638466222
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1764
- Accuracy: 0.9415
- F1: 0.9415
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.436 | 1.0 | 2000 | 0.2178 | 0.93 | 0.9305 |
| 0.1615 | 2.0 | 4000 | 0.1764 | 0.9415 | 0.9415 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Arina/Erine | []
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rwheel/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ArjunKadya/HuggingFace | []
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} | 0 | 2022-10-01T15:12:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets: din0s/asqa
model-index:
- name: t5-base-asqa-ob
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-asqa-ob
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7356
- Rougelsum: 12.0879
## 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: 1e-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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log | 1.0 | 355 | 1.8545 | 11.6549 |
| 2.4887 | 2.0 | 710 | 1.8050 | 11.7533 |
| 1.9581 | 3.0 | 1065 | 1.7843 | 11.8327 |
| 1.9581 | 4.0 | 1420 | 1.7722 | 11.9442 |
| 1.9252 | 5.0 | 1775 | 1.7648 | 11.9331 |
| 1.8853 | 6.0 | 2130 | 1.7567 | 11.9788 |
| 1.8853 | 7.0 | 2485 | 1.7519 | 12.0300 |
| 1.8512 | 8.0 | 2840 | 1.7483 | 12.0225 |
| 1.8328 | 9.0 | 3195 | 1.7451 | 12.0402 |
| 1.8115 | 10.0 | 3550 | 1.7436 | 12.0444 |
| 1.8115 | 11.0 | 3905 | 1.7419 | 12.0850 |
| 1.7878 | 12.0 | 4260 | 1.7408 | 12.1047 |
| 1.774 | 13.0 | 4615 | 1.7394 | 12.0839 |
| 1.774 | 14.0 | 4970 | 1.7390 | 12.0910 |
| 1.7787 | 15.0 | 5325 | 1.7381 | 12.0880 |
| 1.7632 | 16.0 | 5680 | 1.7380 | 12.1088 |
| 1.7623 | 17.0 | 6035 | 1.7370 | 12.1046 |
| 1.7623 | 18.0 | 6390 | 1.7368 | 12.0997 |
| 1.7508 | 19.0 | 6745 | 1.7359 | 12.0902 |
| 1.7597 | 20.0 | 7100 | 1.7356 | 12.0879 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
asaakyan/mbart-poetic-all | []
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} | 0 | 2022-10-01T15:25:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9363425925925926
- name: Recall
type: recall
value: 0.9530461124200605
- name: F1
type: f1
value: 0.9446205170975813
- name: Accuracy
type: accuracy
value: 0.986769294166127
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0774
- Precision: 0.9363
- Recall: 0.9530
- F1: 0.9446
- Accuracy: 0.9868
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0273 | 1.0 | 1756 | 0.0787 | 0.9286 | 0.9411 | 0.9348 | 0.9845 |
| 0.0141 | 2.0 | 3512 | 0.0772 | 0.9299 | 0.9504 | 0.9400 | 0.9863 |
| 0.0054 | 3.0 | 5268 | 0.0774 | 0.9363 | 0.9530 | 0.9446 | 0.9868 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Arnold/common_voiceha | []
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} | 0 | 2022-10-01T15:48:32Z | ---
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- image-to-image
datasets:
- rrustom/architecture2022clean
pipeline: image-to-image
--- |
Arnold/wav2vec2-hausa2-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
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} | 9 | 2022-10-01T15:54:14Z | ---
license: mit
---
# Description
Trainer: Chris
Aqua from Konosuba
# Dataset
>Training: 16 images
>Regularization: 3249 images - waifu-research-department reg images
# Info
>Model Used: Waifu Diffusion 1.3 epoch 5
>Steps: 3000
>Keyword: Aqua (Use this in the prompt)
>Class Phrase: Useless_Goddess |
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-ours-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. -->
# roberta-large-finetuned-ours-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3369
- Accuracy: 0.75
- Precision: 0.7054
- Recall: 0.6949
- F1: 0.6974
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0561 | 0.99 | 99 | 0.8773 | 0.615 | 0.4054 | 0.5584 | 0.4591 |
| 0.762 | 1.98 | 198 | 0.6514 | 0.715 | 0.6735 | 0.6672 | 0.6588 |
| 0.5661 | 2.97 | 297 | 0.6806 | 0.71 | 0.6764 | 0.6608 | 0.6435 |
| 0.3699 | 3.96 | 396 | 0.8358 | 0.71 | 0.6611 | 0.6691 | 0.6570 |
| 0.2184 | 4.95 | 495 | 1.1627 | 0.7 | 0.6597 | 0.6337 | 0.6414 |
| 0.1743 | 5.94 | 594 | 1.0544 | 0.725 | 0.6831 | 0.6949 | 0.6831 |
| 0.098 | 6.93 | 693 | 1.4757 | 0.73 | 0.6885 | 0.6902 | 0.6892 |
| 0.0813 | 7.92 | 792 | 1.8146 | 0.73 | 0.6840 | 0.6772 | 0.6800 |
| 0.0435 | 8.91 | 891 | 1.6697 | 0.755 | 0.7141 | 0.7127 | 0.7132 |
| 0.0209 | 9.9 | 990 | 1.8931 | 0.755 | 0.7102 | 0.7070 | 0.7082 |
| 0.0201 | 10.89 | 1089 | 2.1934 | 0.74 | 0.6971 | 0.6866 | 0.6907 |
| 0.0095 | 11.88 | 1188 | 2.1389 | 0.75 | 0.7014 | 0.6915 | 0.6932 |
| 0.0141 | 12.87 | 1287 | 2.1902 | 0.74 | 0.6942 | 0.6943 | 0.6936 |
| 0.0112 | 13.86 | 1386 | 2.5021 | 0.73 | 0.6889 | 0.6669 | 0.6741 |
| 0.0054 | 14.85 | 1485 | 2.3840 | 0.73 | 0.6819 | 0.6715 | 0.6746 |
| 0.0088 | 15.84 | 1584 | 2.3224 | 0.74 | 0.6909 | 0.6825 | 0.6787 |
| 0.003 | 16.83 | 1683 | 2.2641 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
| 0.0017 | 17.82 | 1782 | 2.3361 | 0.75 | 0.7077 | 0.6968 | 0.7012 |
| 0.0014 | 18.81 | 1881 | 2.3041 | 0.755 | 0.7131 | 0.7009 | 0.7051 |
| 0.0083 | 19.8 | 1980 | 2.3369 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ArtemisZealot/DialoGTP-small-Qkarin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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"model_type": "gpt2",
"task_specific_params": {
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets: din0s/msmarco-nlgen
model-index:
- name: t5-base-msmarco-nlgen-ob
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-msmarco-nlgen-ob
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [MS MARCO Natural Language Generation](https://huggingface.co/datasets/din0s/msmarco-nlgen) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
- Rougelsum: 14.4418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.5472 | 0.13 | 2500 | 0.4383 | 14.2497 |
| 0.5238 | 0.26 | 5000 | 0.4252 | 14.4210 |
| 0.501 | 0.39 | 7500 | 0.4093 | 14.3315 |
| 0.4958 | 0.52 | 10000 | 0.4006 | 14.3347 |
| 0.4913 | 0.66 | 12500 | 0.3944 | 14.5925 |
| 0.4873 | 0.79 | 15000 | 0.3914 | 14.4193 |
| 0.466 | 0.92 | 17500 | 0.3874 | 14.4418 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ArthurBaia/bert-base-portuguese-cased-finetuned-squad | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-TRAC-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. -->
# roberta-large-finetuned-TRAC-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8198
- Accuracy: 0.7190
- Precision: 0.6955
- Recall: 0.6979
- F1: 0.6963
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9538 | 1.0 | 612 | 0.8083 | 0.6111 | 0.6192 | 0.6164 | 0.5994 |
| 0.7924 | 2.0 | 1224 | 0.7594 | 0.6601 | 0.6688 | 0.6751 | 0.6424 |
| 0.6844 | 3.0 | 1836 | 0.6986 | 0.7042 | 0.6860 | 0.6969 | 0.6858 |
| 0.5715 | 3.99 | 2448 | 0.7216 | 0.7075 | 0.6957 | 0.6978 | 0.6925 |
| 0.45 | 4.99 | 3060 | 0.7963 | 0.7288 | 0.7126 | 0.7074 | 0.7073 |
| 0.352 | 5.99 | 3672 | 1.0824 | 0.7141 | 0.6999 | 0.6774 | 0.6818 |
| 0.2546 | 6.99 | 4284 | 1.0884 | 0.7230 | 0.7006 | 0.7083 | 0.7028 |
| 0.1975 | 7.99 | 4896 | 1.5338 | 0.7337 | 0.7090 | 0.7063 | 0.7074 |
| 0.1656 | 8.99 | 5508 | 1.8182 | 0.7100 | 0.6882 | 0.6989 | 0.6896 |
| 0.1358 | 9.98 | 6120 | 2.1623 | 0.7173 | 0.6917 | 0.6959 | 0.6934 |
| 0.1235 | 10.98 | 6732 | 2.3249 | 0.7141 | 0.6881 | 0.6914 | 0.6888 |
| 0.1003 | 11.98 | 7344 | 2.3474 | 0.7124 | 0.6866 | 0.6920 | 0.6887 |
| 0.0826 | 12.98 | 7956 | 2.3574 | 0.7083 | 0.6853 | 0.6959 | 0.6874 |
| 0.0727 | 13.98 | 8568 | 2.4989 | 0.7116 | 0.6858 | 0.6934 | 0.6883 |
| 0.0553 | 14.98 | 9180 | 2.8090 | 0.7026 | 0.6747 | 0.6710 | 0.6725 |
| 0.0433 | 15.97 | 9792 | 2.6647 | 0.7255 | 0.7010 | 0.7028 | 0.7018 |
| 0.0449 | 16.97 | 10404 | 2.6568 | 0.7247 | 0.7053 | 0.6997 | 0.7010 |
| 0.0373 | 17.97 | 11016 | 2.7632 | 0.7149 | 0.6888 | 0.6938 | 0.6909 |
| 0.0278 | 18.97 | 11628 | 2.8245 | 0.7124 | 0.6866 | 0.6930 | 0.6889 |
| 0.0288 | 19.97 | 12240 | 2.8198 | 0.7190 | 0.6955 | 0.6979 | 0.6963 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ArvinZhuang/BiTAG-t5-large | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"translation_en_to_de": {
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | null | ---
license: mit
---
### chekos on Stable Diffusion via Dreambooth
#### model by chekos
This your the Stable Diffusion model fine-tuned the chekos concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **chekos**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AshLukass/AshLukass | []
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}
} | 0 | null | ---
tags:
- conversational
---
# Mental Health Chatbot |
Ashl3y/model_name | []
| null | {
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} | 0 | null | ---
license: mit
---
### Leone From Akame Ga Kill V2 on Stable Diffusion via Dreambooth
#### model by Mrkimmon
This your the Stable Diffusion model fine-tuned the Leone From Akame Ga Kill V2 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **an anime woman character of sks**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Aspect11/DialoGPT-Medium-LiSBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 7 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: unknown
datasets:
- Danbooru2021
inference: true
---
## Repo Overview
A version of Waifu Diffusion v1.4 Float32 Booru 110k model and VAE from Waifu Diffusion v1.4 converted to work with Diffusers library.
^ Quality/Style test
^ Unnatural resolution test
^ Landscape test
^ Extremely long resolution test
## Model Description
The current model has been fine-tuned on a Stable Diffusion 2.1 model with 110k anime-styled images using a technique known as aspect ratio bucketing.
That allows Waifu Diffusion v1.4 to handle different resolutions much better than its previous models.
## Source
WD v1.4 Model:https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/9fa4a42a9c4a0948472fa909e6c1a39be0dda699/models/wd-1-4-float32-booru-110k.ckpt
WD v1.4 VAE: https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt |
Atampy26/GPT-Glacier | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
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} | 5 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/luisbetx9-microversoslt/1664650577553/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/1572306079282872326/rX5Nbrid_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/1296918435469897732/ctOlkbD3_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">BetaP̾e̾t̾r̾a̾ 🅼²🆙 & MicroversosLT</div>
<div style="text-align: center; font-size: 14px;">@luisbetx9-microversoslt</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 BetaP̾e̾t̾r̾a̾ 🅼²🆙 & MicroversosLT.
| Data | BetaP̾e̾t̾r̾a̾ 🅼²🆙 | MicroversosLT |
| --- | --- | --- |
| Tweets downloaded | 3248 | 1105 |
| Retweets | 1892 | 709 |
| Short tweets | 644 | 185 |
| Tweets kept | 712 | 211 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zzml0e6d/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 @luisbetx9-microversoslt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ewz96ki7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ewz96ki7/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/luisbetx9-microversoslt')
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)
|
Atarax/rick | []
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} | 0 | null | ---
license: mit
---
### <852style-girl> on Stable Diffusion
This is the `<852style-girl>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Atchuth/MBOT | []
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} | 0 | null | ---
license: mit
---
### juanma on Stable Diffusion via Dreambooth
#### model by juanmapath
This your the Stable Diffusion model fine-tuned the juanma concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **juanmapath**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Ateeb/QA | [
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"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 4 | null | ---
tags:
- generated_from_trainer
model-index:
- name: nl3
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. -->
# nl3
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 25
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ateeb/SquadQA | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: du_ge_all_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. -->
# du_ge_all_2
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Augustvember/WokkaBot2 | []
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} | 0 | null | ---
license: mit
---
### Nomad on Stable Diffusion
This is the `<nomad>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Augustvember/wokka | [
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
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} | 4 | null | ---
license: mit
---
### kaltsit_v2 on Stable Diffusion via Dreambooth
This your the Stable Diffusion model fine-tuned the kaltsit_v2 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **kaltsit**
v2 update: 1. increase sample size. more stable results. 2. prompt update: kaltsit. 3. prior update: cat girl
Use the model in Google Colab:
[](https://colab.research.google.com/drive/11yzVX9rNEkzMBq6rj1HyQxkDjllI4P1-)
Here is an example output:
prompt = "detailed wallpaper of kaltsit on beach, green animal ears, white hair, green eyes, cleavage breasts and thigh, by ilya kuvshinov and alphonse mucha, strong rim light, splash particles, intense shadows, by Canon EOS, SIGMA Art Lens"
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) |
Aviora/news2vec | []
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} | 0 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
Stable Diffusion model trained on E621 data, specializing on the kinkier side.
Model is also live in my discord server on a free-to-use bot. [The Gooey Pack](https://discord.gg/WBjvffyJZf)
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
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} | 0 | null | ---
license: mit
---
# Lumine (Genshin Impact) on Stable Diffusion
This is the Lumine concept taught to Stable Diffusion via Textual Inversion. You can load this concept into a Stable Diffusion fork such as this [repo](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (Instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase#textual-inversion)).
You can invoke the concept with the keyword lumine_genshin. Note that this should be used in conjunction with the [Waifu-Diffusion](https://huggingface.co/hakurei/waifu-diffusion#model-description) model.
# Example Outputs
Here are several Example outputs
 |
Ayham/albert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null | ---
license: apache-2.0
---
For an eventual web demo of Lyra v2 (SoundStream).
Currently this repo just contains a copy of the model files in the official Lyra repo as of October 2nd 2022: https://github.com/google/lyra/tree/main/model_coeffs
I'm aiming to produce ONNX versions of the models too.
WIP demo here: https://github.com/josephrocca/lyra-v2-soundstream-web |
Ayham/bert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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} | 4 | null | ---
tags:
- conversational
---
# DialoGPT Model trained based on testimonial given during Brazilian's ex-president trial. |
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 6 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: yuntian-deng/im2latex-100k
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# latex2im_ss_100
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `yuntian-deng/im2latex-100k` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im_ss_100/tensorboard?#scalars)
|
Ayham/bert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 3 | null | ---
language:
- en
tags:
- text-classification
- claim-detection
license: "mit"
datasets:
- claimbuster
metrics:
- accuracy
value:"0.83"
widget:
- text: "This is the best cast iron skillet you will ever buy."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book"
---
This is the mDeBERTa model finetuned on the ClaimBuster dataset. It is used for claim detection and has an accuracy of 83%. |
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 6 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 127.50 +/- 35.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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}
} | 12 | null | Access to model pigletpeak/dreambooth is restricted and you are not in the authorized list. Visit https://huggingface.co/pigletpeak/dreambooth to ask for access. |
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 10 | null | ---
tags:
- generated_from_trainer
model-index:
- name: greek_legal_bert_v2-finetuned-re-V1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# greek_legal_bert_v2-finetuned-re-V1
This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BAHIJA/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
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} | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-1.1.0
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. -->
# mt5-small-finetuned-1.1.0
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5550
- Rouge1: 18.5458
- Rouge2: 5.7454
- Rougel: 15.5515
- Rougelsum: 15.7806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 15.4799 | 1.0 | 97 | 6.9041 | 16.3755 | 5.6407 | 13.8081 | 13.8801 |
| 9.8046 | 2.0 | 194 | 4.5550 | 18.5458 | 5.7454 | 15.5515 | 15.7806 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BSC-LT/roberta-large-bne-capitel-ner | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"RobertaForTokenClassification"
],
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}
} | 5 | null | # ReadMe
This is a pretrained model based on [gpt2](https://huggingface.co/gpt2) that has been trained on [copenlu/answerable_tydiqa](https://huggingface.co/datasets/copenlu/answerable_tydiqa), specifically the text field of the English samples for 2 epochs.
To use the pretrained head, use: `AutoModelForCausalLM.from_pretrained`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PartiallyTyped/answerable_tydiqa_lm_pretrained_japenese")
tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-small")
```
|
BSC-LT/roberta-large-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
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"RobertaForQuestionAnswering"
],
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}
} | 15 | null | ---
tags:
- text2text-generation
- Trinidadian Creole
- Caribbean dialect
license: apache-2.0
---
# Standard English to Trinidad English Creole Translator
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of English to Trinidad English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/).
___
# Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC")
model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC")
text = "Where are you going now?"
inputs = tokenizer("eng:"+text, truncation=True, return_tensors='pt')
output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
translation=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(translation)) #translation: Weh yuh going now.
```
___
|
Banshee/dialoGPT-luke-small | []
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} | 0 | 2022-10-02T17:06:34Z | ---
language: sw
license: cc-by-4.0
datasets:
- kenyacorpus_v2
model-index:
- name: innocent-charles/Swahili-question-answer-latest-cased
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: kenyacorpus
type: kenyacorpus
config: kenyacorpus
split: validation
metrics:
- type: exact_match
value: 51.9309
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTIyN2VhODRhMTQzOGYwNGU0NjM4NmMyOWQ1ZmM4ODliNGRlNjdjMTY3MWU5YzVkYWJmODhiNTMyZDE4NGQ5ZSIsInZlcnNpb24iOjF9.oVd4HFhao0K7AwV0sZTCy2Sa4mG2LP-BX0ImCynZQJ-zReQtgoK1x0LRn31chEKF_CHOQ4ZZ5SBrOuCwK5KNCQ
- type: f1
value: 63.9501
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2E3YWU0YTljNjI4YmEyNjRkZWFlZTZlZmMzNjc2NzhiMmEzNmNlZDQ1YjEwZGY1MTEzYTUyZWNjMWJiMzBlMiIsInZlcnNpb24iOjF9.x_DxEhpVLb_JRhk0z12lJhVV_ugvUdK_axOe7Cb6oyH7ir7Ky0TJpIDfmk6w7IgNKiYAZ_yObNbjyov6QNoeCw
- type: total
value: 445
name: total
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTFkYzExMDZiZmUwOTA3ZDYyZjhhZjZmZmFhNWU1NDI4NjY4ZTY1NjQxMjhkNjNiMzBmMGY0YTlhNzVjY2NjNyIsInZlcnNpb24iOjF9.RexL6OXVW3eQRdd7tk9RQPNACCFSwXi3DHz0cd77vZ2Jai7ESLTf8vFIM6j7V2nBGcON4-bJ7MQeRrRg16qyCg
---
# SWAHILI QUESTION - ANSWER MODEL
This is the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model, fine-tuned using the [KenyaCorpus](https://github.com/Neurotech-HQ/Swahili-QA-dataset) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering in Swahili Language.
Question answering (QA) is a computer science discipline within the fields of information retrieval and NLP that help in the development of systems in such a way that, given a question in natural language, can extract relevant information from provided data and present it in the form of natural language answers.
## Overview
**Language model used:** bert-base-multilingual-cased
**Language:** Kiswahili
**Downstream-task:** Extractive Swahili QA
**Training data:** KenyaCorpus
**Eval data:** KenyaCorpus
**Code:** See [an example QA pipeline on Haystack](https://blog.neurotech.africa/building-swahili-question-and-answering-with-haystack/)
**Infrastructure**: AWS NVIDIA A100 Tensor Core GPU
## Hyperparameters
```
batch_size = 16
n_epochs = 10
base_LM_model = "bert-base-multilingual-cased"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
```
## Usage
### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased")
# or
reader = TransformersReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased",tokenizer="innocent-charles/Swahili-question-answer-latest-cased")
```
For a complete example of ``Swahili-question-answer-latest-cased`` being used for Swahili Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai)
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "innocent-charles/Swahili-question-answer-latest-cased"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Asubuhi ilitupata pambajioi pa hospitali gani?',
'context': 'Asubuhi hiyo ilitupata pambajioni pa hospitali ya Uguzwa.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Performance
```
"exact": 51.87029394424324,
"f1": 63.91251169582613,
"total": 445,
"HasAns_exact": 50.93522267206478,
"HasAns_f1": 62.02838248389763,
"HasAns_total": 386,
"NoAns_exact": 49.79983179142137,
"NoAns_f1": 60.79983179142137,
"NoAns_total": 59
```
## Special consideration
The project is still going, hence the model is still updated after training the model in more data, Therefore pull requests are welcome to contribute to increase the performance of the model.
## Author
**Innocent Charles:** [email protected]
## About Me
<P>
I build good things using Artificial Intelligence ,Data and Analytics , with over 3 Years of Experience as Applied AI Engineer & Data scientist from a strong background in Software Engineering ,with passion and extensive experience in Data and Businesses.
</P>
[Linkedin](https://www.linkedin.com/in/innocent-charles/) | [GitHub](https://github.com/innocent-charles) | [Website](innocentcharles.com)
|
Banshee/dialoGPT-small-luke | []
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} | 0 | 2022-10-02T17:06:56Z | ---
license: mit
---
Aqua_anime_girl_blk_reg_2000.ckpt
# Dataset
>Training: 16 images
>Regularization: 400 images - black reg images
# Info
>Model Used: Waifu Diffusion 1.3 epoch 5
>Steps: 2000
>Keyword: Aqua (Use this in the prompt)
>Class Phrase: Anime girl
Aqua_animegirl_wrd_reg_2000.ckpt
# Dataset
>Training: 16 images
>Regularization: 400 images - Waifu Research Department reg images
# Info
>Model Used: Waifu Diffusion 1.3 epoch 5
>Steps: 2000
>Keyword: Aqua (Use this in the prompt)
>Class Phrase: Anime girl
Aqua_CxRwMwCYViVz2JUH_blk_reg_2000.ckpt
# Dataset
>Training: 16 images
>Regularization: 400 images - black reg images
# Info
>Model Used: Waifu Diffusion 1.3 epoch 5
>Steps: 2000
>Keyword: Aqua (Use this in the prompt)
>Class Phrase: CxRwMwCYViVz2JUH
Aqua_CxRwMwCYViVz2JUH_wrd_reg_2000.ckpt
# Dataset
>Training: 16 images
>Regularization: 400 images - Waifu Research Department reg images
# Info
>Model Used: Waifu Diffusion 1.3 epoch 5
>Steps: 2000
>Keyword: Aqua (Use this in the prompt)
>Class Phrase: CxRwMwCYViVz2JUH |
Barleysack/AERoberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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"RobertaForQuestionAnswering"
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}
} | 7 | 2022-10-02T17:58:34Z | "Question generation model conversational capabilities" |
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es | []
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}
} | 0 | null | ---
license: openrail
---
# <span style="color:blue">FishNet: AI For Fish Stock Estimation</span>
The attached model was trained on a 63000 dataset of fish images belonging to 163
species. First, we trained a detectron2 model to detect and segment fish and fiduciary markers on a board.
The detectron2 model was written in PyTorch, and the final classifier is a ResNet50 keras model.
Below is an example of how to use the 2 models.
## Packages
## Load Models
## load an image and transform
## run the segmentation model
## visualize
## classify the fish |
Baybars/debateGPT | []
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} | 0 | 2022-10-02T02:17:04Z | ---
library_name: pytorch
tags:
- style-transfer
- face-stylization
---
## Model Details
This system provides a web demo for the following paper:
**VToonify: Controllable High-Resolution Portrait Video Style Transfer (TOG/SIGGRAPH Asia 2022)**
- Developed by: Shuai Yang, Liming Jiang, Ziwei Liu and Chen Change Loy
- Resources for more information:
- [Project Page](https://www.mmlab-ntu.com/project/vtoonify/)
- [Research Paper](https://arxiv.org/abs/2209.11224)
- [GitHub Repo](https://github.com/williamyang1991/VToonify)
**Abstract**
> Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency. In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel **VToonify** framework. Specifically, VToonify leverages the mid- and high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output. Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity. This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively. Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls.
## Citation Information
```bibtex
@article{yang2022Vtoonify,
title={VToonify: Controllable High-Resolution Portrait Video Style Transfer},
author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change},
journal={ACM Transactions on Graphics (TOG)},
volume={41},
number={6},
articleno={203},
pages={1--15},
year={2022},
publisher={ACM New York, NY, USA},
doi={10.1145/3550454.3555437},
}
```
## License
[S-Lab License 1.0](https://github.com/williamyang1991/VToonify/blob/main/LICENSE.md)
|
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 13 | 2022-10-02T21:06:00Z | ---
license: wtfpl
language: es
tags:
- gpt-j
- spanish
- gpt-j-6b
- LLM
- Spanish GPT-J
---
# BERTIN-GPT-J-6B with 8-bit weights (Quantized)
This model (and model card) is an adaptation of [hivemind/gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit), so all credits to him/her.
This is a version of the **latest checkpoint (1M steps)** **[bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B)** that is modified so you can generate **and fine-tune the model in Colab or equivalent desktop GPU**.
Here's how to run it: [](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/check_perplexity.ipynb) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using [this notebook](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
### How to use
```sh
wget https://huggingface.co/mrm8488/bertin-gpt-j-6B-ES-v1-8bit/resolve/main/utils.py -O Utils.py
pip install transformers
pip install bitsandbytes-cuda111==0.26.0
```
```py
import transformers
import torch
from Utils import GPTJBlock, GPTJForCausalLM
device = "cuda" if torch.cuda.is_available() else "cpu"
transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock # monkey-patch GPT-J
ckpt = "mrm8488/bertin-gpt-j-6B-ES-v1-8bit"
tokenizer = transformers.AutoTokenizer.from_pretrained(ckpt)
model = GPTJForCausalLM.from_pretrained(ckpt, pad_token_id=tokenizer.eos_token_id, low_cpu_mem_usage=True).to(device)
prompt = tokenizer("El sentido de la vida es", return_tensors='pt')
feats = {key: value.to(device) for key, value in prompt.items()}
out = model.generate(**feats, max_length=64, do_sample=True)
print(tokenizer.decode(out[0]))
``` |
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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}
} | 1,816 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets: din0s/asqa
model-index:
- name: t5-base-pt-asqa-ob
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-pt-asqa-ob
This model is a fine-tuned version of [din0s/t5-base-msmarco-nlgen-ob](https://huggingface.co/din0s/t5-base-msmarco-nlgen-ob) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7481
- Rougelsum: 12.3722
## 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: 1e-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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log | 1.0 | 355 | 1.8760 | 11.5138 |
| 2.1344 | 2.0 | 710 | 1.8322 | 11.6843 |
| 1.979 | 3.0 | 1065 | 1.8109 | 11.8592 |
| 1.979 | 4.0 | 1420 | 1.7967 | 11.9466 |
| 1.9493 | 5.0 | 1775 | 1.7871 | 12.0333 |
| 1.9099 | 6.0 | 2130 | 1.7778 | 12.0805 |
| 1.9099 | 7.0 | 2485 | 1.7720 | 12.1659 |
| 1.8748 | 8.0 | 2840 | 1.7668 | 12.2039 |
| 1.8584 | 9.0 | 3195 | 1.7628 | 12.2506 |
| 1.8362 | 10.0 | 3550 | 1.7601 | 12.2557 |
| 1.8362 | 11.0 | 3905 | 1.7575 | 12.2718 |
| 1.8134 | 12.0 | 4260 | 1.7562 | 12.2789 |
| 1.7996 | 13.0 | 4615 | 1.7538 | 12.3179 |
| 1.7996 | 14.0 | 4970 | 1.7529 | 12.3035 |
| 1.8049 | 15.0 | 5325 | 1.7519 | 12.3317 |
| 1.7898 | 16.0 | 5680 | 1.7510 | 12.3717 |
| 1.7872 | 17.0 | 6035 | 1.7497 | 12.3750 |
| 1.7872 | 18.0 | 6390 | 1.7486 | 12.3580 |
| 1.7759 | 19.0 | 6745 | 1.7483 | 12.3698 |
| 1.785 | 20.0 | 7100 | 1.7481 | 12.3722 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BenQLange/HF_bot | []
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} | 0 | 2022-10-02T22:34:58Z | ---
tags:
- generated_from_trainer
model-index:
- name: OscarRoBERTo_pt
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. -->
# OscarRoBERTo_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi2-colab | []
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} | 0 | 2022-10-03T00:44:12Z | ---
license: apache-2.0
---
This model receives scrambled or incoherent sentences as input and returns a meaningful sentence using the same words in the input . A form of grammar correction if you may .
It was trained on a dataset of permutated sentences derived from wikipedia pages as input with the correct arrangement of words as labels .
It is an encoder-decoder model that uses BERT's weight in both it's encoder and decoder .
|
Bhuvana/t5-base-spellchecker | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
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}
} | 93 | 2022-10-03T01:07:42Z | ---
license: apache-2.0
---
A simple text generation model trained on 17+K "Elon Musk tweets" with an accuracy of 92%. |
Biasface/DDDC | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 14 | null | # Description
Trainer: naotsue
Ayanokouji Kiyotaka from Classroom of the Elite
# Dataset
>Training: 20 images
>Regularization: 300 images
# Info
>Model Used: Waifu Diffusion 1.3 (Epoch 6)
>Steps: 3000
>Keyword: AYANOKOUJI (Use this in the prompt)
>Class Phrase: 1boy
 |
BigSalmon/BlankSlots | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
],
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"prefix": "translate English to Romanian: "
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}
} | 4 | null | ---
pipeline_tag: text-generation
widget:
text: You could not prevent a thunderstorm, but you could use
---
Levanter GPT is trained on OpenWebText2.
More complete model card will be made in the future. |
BigSalmon/FormalRobertaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 5 | null | ---
license: mit
---
### New priests on Stable Diffusion
This is the `<new-priest>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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}
} | 12 | null | ---
license: mit
---
### Liminalspaces on Stable Diffusion
This is the `<liminal image>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
BigSalmon/FroBurta | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6566666666666666
- name: F1
type: f1
value: 0.6555183946488293
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.6751
- Accuracy: 0.6567
- F1: 0.6555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigSalmon/GoodMaskResults | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
} | 9 | null | ---
tags:
- generated_from_trainer
datasets:
- invoice
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Invoice
type: invoice
args: invoice
metrics:
- name: Precision
type: precision
value: 1.0
- name: Recall
type: recall
value: 1.0
- name: F1
type: f1
value: 1.0
- name: Accuracy
type: accuracy
value: 1.0
---
# LayoutLM-v3 model fine-tuned on invoice dataset
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the Rhenus dataset.
We use Microsoft’s LayoutLMv3 trained on Eurocorporation Dataset to predict the labels. To use it, simply upload an image. Results will show up in a few seconds.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
All the training codes are available from the below GitHub link.
The model can be evaluated at the HuggingFace Spaces link:
https://huggingface.co/DataIntelligenceTeam/Rhenus
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Step | Training Loss | Validation Loss| Precision | Recall | F1 | Accuracy |
|:---:|:------------:|:--------------:|:---------:|:------:|:------:|:---------:|
|100 | No log | 0.57765 |0.568773 | 0.600000 |0.583969| 0.895848|
|200 | No log | 0.181364 | 0.933594| 0.937255| 0.935421| 0.988037|
|300 | No log | 0.091626| 0.945312| 0.949020| 0.947162| 0.991555|
|400 | No log | 0.060504| 0.964981| 0.972549| 0.968750| 0.995074|
|500 | 0.360900 | 0.046041| 0.988327| 0.996078| 0.992188| 0.999296|
|600 | 0.360900 | 0.036889| 0.988327| 0.996078| 0.992188| 0.999296|
|700 | 0.360900 | 0.032077| 0.988327| 0.996078| 0.992188| 0.999296|
|800 | 0.360900 | 0.028109| 0.988327| 0.996078| 0.992188| 0.999296|
|900 | 0.360900 | 0.027945| 0.988327| 0.996078| 0.992188| 0.999296|
|1000| 0.037800 | 0.027469| 0.988327| 0.996078| 0.992188| 0.999296
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: krm/my_exercice_mrpc
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# krm/my_exercice_mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6942
- Train Accuracy: 0.6200
- Validation Loss: 0.6486
- Validation Accuracy: 0.6838
- Epoch: 2
## Model description
Ce modèle n'est pas à utiliser. Il s'agit d'un petit essai.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6860 | 0.6314 | 0.7727 | 0.6838 | 0 |
| 0.6862 | 0.6347 | 0.6326 | 0.6838 | 1 |
| 0.6942 | 0.6200 | 0.6486 | 0.6838 | 2 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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},
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}
} | 5 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- librispeech_asr
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="librispeech" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-librispeech" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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}
}
} | 9 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- LIUM/tedlium
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="tedlium" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-tedlium" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 9 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- speechcolab/gigaspeech
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="gigaspeech" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-gigaspeech" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 9 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/chime-4
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="chime4" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-chime4" \
--dropout_rate="0.1" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 13 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- mozilla-foundation/common_voice_9_0
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="common_voice" \
--output_dir="./" \
--run_name="conformer-rnnt-common-voice" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--max_eval_duration_in_seconds="20" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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}
}
} | 10 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- facebook/voxpopuli
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="voxpopuli" \
--output_dir="./" \
--run_name="conformer-rnnt-voxpopuli" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/Points | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 13 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- LIUM/tedlium
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="tedlium" \
--output_dir="./" \
--run_name="rnnt-tedlium-baseline" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/Points2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
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"max_length": 50
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 12 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- kensho/spgispeech
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="spgispeech" \
--output_dir="./" \
--run_name="conformer-rnnt-spgispeech" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 17 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9244334678544425
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2185
- Accuracy: 0.9245
- F1: 0.9244
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.847 | 1.0 | 250 | 0.3228 | 0.8985 | 0.8951 |
| 0.2543 | 2.0 | 500 | 0.2185 | 0.9245 | 0.9244 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu116
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigSalmon/T5F | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/switchboard
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="switchboard" \
--output_dir="./" \
--run_name="conformer-rnnt-switchboard" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"early_stopping": true,
"length_penalty": 2,
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"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- revdotcom/earnings22
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="earnings22" \
--output_dir="./" \
--run_name="conformer-rnnt-earnings22" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"max_length": null
},
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"early_stopping": true,
"length_penalty": 2,
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"text-generation": {
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"translation_en_to_de": {
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"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 13 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/chime-4
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--dataset_config_name="chime4" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--output_dir="./" \
--run_name="conformer-rnnt-chime4" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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}
}
} | 7 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
}
} | 16 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- edinburghcstr/ami
---
To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
--config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
--model_name_or_path="stt_en_conformer_transducer_xlarge" \
--dataset_name="esb/datasets" \
--tokenizer_path="tokenizer" \
--vocab_size="1024" \
--max_steps="100000" \
--dataset_config_name="ami" \
--output_dir="./" \
--run_name="conformer-rnnt-ami" \
--wandb_project="rnnt" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="4" \
--logging_steps="50" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--save_strategy="steps" \
--save_steps="20000" \
--evaluation_strategy="steps" \
--eval_steps="20000" \
--report_to="wandb" \
--preprocessing_num_workers="4" \
--fused_batch_size="4" \
--length_column_name="input_lengths" \
--fuse_loss_wer \
--group_by_length \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--use_auth_token
```
|
BigTooth/Megumin-v0.2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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}
}
} | 13 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- facebook/voxpopuli
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="voxpopuli" \
--max_steps="5000" \
--output_dir="./" \
--run_name="whisper-voxpopuli" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 76 | null | ---
license: mit
language:
- en
library_name: adapter-transformers
--- |
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-tour-demo-v5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-tour-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1467
- Accuracy: 0.4880
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4573 | 0.13 | 100 | 3.2038 | 0.3334 |
| 2.9547 | 0.27 | 200 | 2.8725 | 0.3672 |
| 2.6093 | 0.4 | 300 | 2.7619 | 0.3954 |
| 2.6212 | 0.54 | 400 | 2.6269 | 0.3942 |
| 2.5063 | 0.67 | 500 | 2.5060 | 0.4211 |
| 2.3113 | 0.81 | 600 | 2.5348 | 0.4201 |
| 2.5702 | 0.94 | 700 | 2.3345 | 0.4502 |
| 2.0479 | 1.08 | 800 | 2.3183 | 0.4484 |
| 1.754 | 1.21 | 900 | 2.2546 | 0.4661 |
| 1.7772 | 1.34 | 1000 | 2.1994 | 0.4794 |
| 1.9276 | 1.48 | 1100 | 2.1672 | 0.4731 |
| 1.6621 | 1.61 | 1200 | 2.1676 | 0.4845 |
| 1.7063 | 1.75 | 1300 | 2.1446 | 0.4806 |
| 1.8655 | 1.88 | 1400 | 2.1121 | 0.4933 |
| 1.4577 | 2.02 | 1500 | 2.0934 | 0.4955 |
| 1.1857 | 2.15 | 1600 | 2.1128 | 0.4906 |
| 1.1684 | 2.28 | 1700 | 2.1218 | 0.4941 |
| 1.3873 | 2.42 | 1800 | 2.1108 | 0.4957 |
| 1.3545 | 2.55 | 1900 | 2.0985 | 0.4992 |
| 0.9789 | 2.69 | 2000 | 2.0997 | 0.4961 |
| 1.1772 | 2.82 | 2100 | 2.1141 | 0.4951 |
| 1.0968 | 2.96 | 2200 | 2.1097 | 0.4922 |
| 0.7883 | 3.09 | 2300 | 2.1170 | 0.5067 |
| 0.7593 | 3.23 | 2400 | 2.1516 | 0.4847 |
| 0.5671 | 3.36 | 2500 | 2.1414 | 0.4925 |
| 0.6442 | 3.49 | 2600 | 2.1498 | 0.4880 |
| 0.516 | 3.63 | 2700 | 2.1442 | 0.4878 |
| 0.6283 | 3.76 | 2800 | 2.1518 | 0.4882 |
| 0.5629 | 3.9 | 2900 | 2.1467 | 0.4880 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Bryan190/Aguy190 | []
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}
} | 0 | null | You can use Stable Diffusion with Dream's face on it!
The object name is: dream-youtuber, and the class name is male.
You can use it in the prompts like:
photo of dream-youtuber male, digital painting
Now to use this model:
In AUTOMATIC1111's notebook (https://github.com/AUTOMATIC1111/stable-diffusion-webui) just below the Normal 1.4 Model block. Insert this block:
````
#@title Stable-Dreamfusion Model
# get a token from https://huggingface.co/settings/tokens
user_token = "" #@param {type:"string"}
user_header = f"\"Authorization: Bearer {user_token}\""
!wget --header={user_header} https://huggingface.co/whyperr/stable-dreamfusion/resolve/main/model.ckpt -O models/sd-v1-4.ckpt
````
Run the next steps normally, and you should be able to generate juicy dream faces!
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"min_length": null,
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},
"text-generation": {
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
} | 42 | null | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegagus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegagus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6985 | 0.54 | 500 | 1.4876 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 2.0.0
- Tokenizers 0.10.3
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 71 | 2022-10-03T17:16:25Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 229.59 +/- 12.98
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
} | 580 | 2022-10-03T17:35:27Z | ---
language:
- ru
license: apache-2.0
---
# Model DmitryPogrebnoy/distilbert-base-russian-cased
# Model Description
This model is russian version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased).
The code for the transforming process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/distilbert_base_russian_cased/distilbert_from_multilang_to_ru.ipynb).
This model give exactly the same representations produced by the original model which preserves the original accuracy.
There is a similar model of [Geotrend/distilbert-base-ru-cased](https://huggingface.co/Geotrend/distilbert-base-ru-cased).
However, our model is derived from a slightly different approach.
Instead of using wikipedia's Russian dataset to pick the necessary tokens,
we used regular expressions in this model to select only Russian tokens, punctuation marks, numbers and other service tokens.
Thus, our model contains several hundred tokens, which have been filtered out in [Geotrend/distilbert-base-ru-cased](https://huggingface.co/Geotrend/distilbert-base-ru-cased).
This model was created as part of a master's project to develop a method for correcting typos
in medical histories using BERT models as a ranking of candidates.
The project is open source and can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker).
# How to Get Started With the Model
You can use the model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/distilbert-base-russian-cased')
>>> pipeline("Я [MASK] на заводе.")
[{'score': 0.11498937010765076,
'token': 1709,
'token_str': 'работал',
'sequence': 'Я работал на заводе.'},
{'score': 0.07212855666875839,
'token': 12375,
'token_str': '##росла',
'sequence': 'Яросла на заводе.'},
{'score': 0.03575785085558891,
'token': 4059,
'token_str': 'находился',
'sequence': 'Я находился на заводе.'},
{'score': 0.02496381290256977,
'token': 5075,
'token_str': 'работает',
'sequence': 'Я работает на заводе.'},
{'score': 0.020675526931881905,
'token': 5774,
'token_str': '##дро',
'sequence': 'Ядро на заводе.'}]
```
Or you can load the model and tokenizer and do what you need to do:
```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/distilbert-base-russian-cased")
>>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/distilbert-base-russian-cased")
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 34 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-5000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9104
- name: F1
type: f1
value: 0.9115673114883537
---
<!-- 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. -->
# finetuning-sentiment-model-5000-samples
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: 0.5802
- Accuracy: 0.9104
- F1: 0.9116
## 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: 10
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,860 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8166666666666667
- name: F1
type: f1
value: 0.8307692307692307
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.6069
- Accuracy: 0.8167
- F1: 0.8308
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6633333333333333
- name: F1
type: f1
value: 0.7247956403269755
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.6551
- Accuracy: 0.6633
- F1: 0.7248
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 62 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9202951757885023
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2245
- Accuracy: 0.92
- F1: 0.9203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8171 | 1.0 | 250 | 0.3222 | 0.907 | 0.9055 |
| 0.2546 | 2.0 | 500 | 0.2245 | 0.92 | 0.9203 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.8.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,862 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.82
- name: F1
type: f1
value: 0.8235294117647058
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.6107
- Accuracy: 0.82
- F1: 0.8235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
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