modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
Waynehillsdev/Waynehills_summary_tensorflow
|
[
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
text2text-generation
|
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| 5
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en): `vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-5000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-en | vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-5000 |
|:---------------------------|:-------------------------------------------|:-------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 89,885,955 |
| parameter_size_embedding | 692,451,072 | 3,841,536 |
| vocab_size | 901,629 | 5,002 |
| compression_rate_full | 100.0 | 11.55 |
| compression_rate_embedding | 100.0 | 0.55 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 5000 | 2 |
|
Waynehillsdev/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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| 5
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_xsum_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_xsum_billsum_model
This model is a fine-tuned version of [ShubhamSP/my_awesome_billsum_model](https://huggingface.co/ShubhamSP/my_awesome_billsum_model) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9629
- Rouge1: 0.247
- Rouge2: 0.0733
- Rougel: 0.1645
- Rougelsum: 0.164
- Gen Len: 67.05
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2452 | 1.0 | 40 | 2.8062 | 0.243 | 0.0724 | 0.1745 | 0.1737 | 67.15 |
| 0.7262 | 2.0 | 80 | 2.9629 | 0.247 | 0.0733 | 0.1645 | 0.164 | 67.05 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Waynehillsdev/waynehills_sentimental_kor
|
[
"pytorch",
"electra",
"text-classification",
"transformers"
] |
text-classification
|
{
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"ElectraForSequenceClassification"
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| 33
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en): `vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-10000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-en | vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-10000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 93,725,955 |
| parameter_size_embedding | 692,451,072 | 7,681,536 |
| vocab_size | 901,629 | 10,002 |
| compression_rate_full | 100.0 | 12.04 |
| compression_rate_embedding | 100.0 | 1.11 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 10000 | 2 |
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
{
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| 3
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en): `vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-15000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-en | vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-15000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 97,565,955 |
| parameter_size_embedding | 692,451,072 | 11,521,536 |
| vocab_size | 901,629 | 15,002 |
| compression_rate_full | 100.0 | 12.53 |
| compression_rate_embedding | 100.0 | 1.66 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 15000 | 2 |
|
Doohae/roberta
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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},
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| 3
| null |
# `vocabtrimmer/xlm-v-base-trimmed-pt-60000-tweet-sentiment-pt`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-pt-60000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-pt-60000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (portuguese).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(portuguese).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 63.91 | 63.91 | 63.91 | 62.99 | 63.91 | 63.5 | 63.91 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-pt-60000-tweet-sentiment-pt/raw/main/eval.json).
|
Doxophobia/DialoGPT-medium-celeste
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 11
| null |
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-fr-60000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-fr-60000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 132,185,186 |
| parameter_size_embedding | 692,451,072 | 46,081,536 |
| vocab_size | 901,629 | 60,002 |
| compression_rate_full | 100.0 | 16.96 |
| compression_rate_embedding | 100.0 | 6.65 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 60000 | 2 |
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
| 29
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en): `vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-30000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-en | vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-30000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 109,085,955 |
| parameter_size_embedding | 692,451,072 | 23,041,536 |
| vocab_size | 901,629 | 30,002 |
| compression_rate_full | 100.0 | 14.01 |
| compression_rate_embedding | 100.0 | 3.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 30000 | 2 |
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 29
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_6_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-nl6-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_6_1
type: common_voice_6_1
config: nl
split: test
args: nl
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-nl6-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_6_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0996
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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},
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}
| 29
| 2023-03-30T21:20:23Z
|
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
}
| 30
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en): `vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-60000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-en | vocabtrimmer/xlm-v-base-tweet-sentiment-en-trimmed-en-60000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 132,125,955 |
| parameter_size_embedding | 692,451,072 | 46,081,536 |
| vocab_size | 901,629 | 60,002 |
| compression_rate_full | 100.0 | 16.97 |
| compression_rate_embedding | 100.0 | 6.65 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 60000 | 2 |
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
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
}
}
}
| 37
| null |
# `vocabtrimmer/xlm-v-base-trimmed-fr-60000-tweet-sentiment-fr`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-fr-60000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-fr-60000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (french).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(french).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 71.95 | 71.95 | 71.95 | 71.7 | 71.95 | 71.94 | 71.95 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-fr-60000-tweet-sentiment-fr/raw/main/eval.json).
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
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
}
}
}
| 33
| null |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
DoyyingFace/bert-asian-hate-tweets-concat-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
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": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 25
| null |
---
license: creativeml-openrail-m
duplicated_from: rayray930126/BlueMix
---
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 38,156
| 2023-03-30T21:44:21Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 555.00 +/- 195.44
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rgres -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rgres -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rgres
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 4,785,283
| 2023-03-30T21:46:40Z
|
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-en`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-en |
|:---------------------------|:----------------------|:-------------------------------------|
| parameter_size_full | 779,396,349 | 458,814,091 |
| parameter_size_embedding | 692,451,072 | 372,285,696 |
| vocab_size | 901,629 | 484,747 |
| compression_rate_full | 100.0 | 58.87 |
| compression_rate_embedding | 100.0 | 53.76 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | | 2 |
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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
}
}
}
| 687
| 2023-03-30T21:49:13Z
|
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.71 +/- 0.29
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
bert-base-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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
}
}
}
| 8,621,271
| 2023-03-30T22:02:19Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 394.50 +/- 139.74
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PhilSad -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PhilSad -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PhilSad
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
bert-base-german-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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
}
}
}
| 175,983
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-uncased-guilt-detectionv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-guilt-detectionv2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7730
- Accuracy: 0.7876
- F1: 0.7876
- Precision: 0.7880
- Recall: 0.7876
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4529 | 1.0 | 2042 | 0.4393 | 0.7995 | 0.7995 | 0.7995 | 0.7995 |
| 0.3885 | 2.0 | 4084 | 0.4630 | 0.7990 | 0.7989 | 0.7991 | 0.7990 |
| 0.2709 | 3.0 | 6126 | 0.5564 | 0.7964 | 0.7963 | 0.7974 | 0.7964 |
| 0.1738 | 4.0 | 8168 | 0.6039 | 0.7889 | 0.7887 | 0.7897 | 0.7889 |
| 0.1208 | 5.0 | 10210 | 0.7918 | 0.7837 | 0.7831 | 0.7867 | 0.7837 |
| 0.0881 | 6.0 | 12252 | 0.7730 | 0.7876 | 0.7876 | 0.7880 | 0.7876 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
bert-base-german-dbmdz-cased
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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,
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},
"translation_en_to_ro": {
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}
}
}
| 1,814
| 2023-03-30T22:25:55Z
|
# `vocabtrimmer/xlm-v-base-trimmed-en-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 64.6 | 64.6 | 64.6 | 61.28 | 64.6 | 65.46 | 64.6 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-en-tweet-sentiment-en/raw/main/eval.json).
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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,
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},
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},
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},
"translation_en_to_fr": {
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}
}
}
| 68,305
| 2023-03-30T22:31:30Z
|
---
language:
- en
metrics:
- squad
---
# Model Card for rein5/bert-base-uncased-finetuned-spoken-squad
<!-- Provide a quick summary of what the model is/does. -->
Extractive Question-Answering model fine-tuned from bert-base-uncased on the SpokenSQuAD dataset.
Code for training and testing: https://github.com/rein5/spoken-squad-language-model
Spoken-SQuAD dataset: https://github.com/chiahsuan156/Spoken-SQuAD
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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
}
}
}
| 4,749,504
| 2023-03-30T22:40:07Z
|
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-en-5000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-en-5000 |
|:---------------------------|:----------------------|:------------------------------------------|
| parameter_size_full | 779,396,349 | 89,890,186 |
| parameter_size_embedding | 692,451,072 | 3,841,536 |
| vocab_size | 901,629 | 5,002 |
| compression_rate_full | 100.0 | 11.53 |
| compression_rate_embedding | 100.0 | 0.55 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 5000 | 2 |
|
bert-large-cased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_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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},
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"prefix": null
}
}
}
| 2,316
| 2023-03-30T22:51:52Z
|
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
bert-large-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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
}
}
}
| 388,769
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Yureeh/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bert-large-uncased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 480,510
| 2023-03-30T22:57:02Z
|
# `vocabtrimmer/xlm-v-base-trimmed-en-5000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en-5000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en-5000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 53.56 | 53.56 | 53.56 | 43.39 | 53.56 | 58.72 | 53.56 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-en-5000-tweet-sentiment-en/raw/main/eval.json).
|
bert-large-uncased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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,
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},
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}
}
}
| 76,685
| 2023-03-30T22:58:08Z
|
---
duplicated_from: ninsia/NO133_104
---
忌呪帯法
recommendation 自分が使う時の設定
sita,sita_nsfw
Clip skip:2
scale 7～10
VAE k1-f8 anime2
Sampling method:DDIM or DPM 2M Karras
Hires. fix:ON
sita_mix,sita_mix_nsfw
beautiful detailed eyes,big eyes常用
VAE NAIだったり好きなものを
HiresはUpscaler1.5 Latentのstrength0.56か
ESRGAN_4xのstrength0.5
Sampling method:DDIM or DPM 2M Karras STEP40
loli series
loli,beautiful detailed eyes,big eyes常用
Clip skip:2
scale 7～10
VAE:merge VAE(アップしたやつ)
HiresはUpscaler1.5 Latentのstrength0.56か
ESRGAN_4xのstrength0.5
Sampling method:DDIM or DPM++ 2M Karras STEP40
a デフォルト
b 少し太眉
c 少しメスガキ
d1 モデル破損ありでおかしな挙動するので微妙
d1は修正したものもありますが基本はaのみで良いと思います もし需要など増えたらアップします
|
bert-large-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
}
}
| 1,058,496
| 2023-03-30T23:00:36Z
|
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-en-10000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-en-10000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 93,735,186 |
| parameter_size_embedding | 692,451,072 | 7,681,536 |
| vocab_size | 901,629 | 10,002 |
| compression_rate_full | 100.0 | 12.03 |
| compression_rate_embedding | 100.0 | 1.11 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 10000 | 2 |
|
ctrl
|
[
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null |
{
"architectures": null,
"model_type": "ctrl",
"task_specific_params": {
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},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 17,007
| 2023-03-30T23:01:48Z
|
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 136.20 +/- 7.52
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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
distilbert-base-cased
|
[
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null |
{
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
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"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 574,859
| null |
Access to model arigot3/autotrain-moodify-45307113562 is restricted and you are not in the authorized list. Visit https://huggingface.co/arigot3/autotrain-moodify-45307113562 to ask for access.
|
distilbert-base-multilingual-cased
|
[
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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"DistilBertForMaskedLM"
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}
| 8,339,633
| 2023-03-30T23:09:00Z
|
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 47.70 +/- 34.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AJ/rick-ai
|
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}
| 0
| 2023-03-31T03:31:14Z
|
---
language: zh
---
## bert-chinese-homie
This is a Chinese pre-training model BERT, pre-trained on a large-scale corpus. It is suitable for fine-tuning on specific downstream tasks, or as a parameter initialization for pre-training, which can improve performance. Due to excessive alchemy, it is not suitable for Fill Mask directly, unless you have performed a small amount of pre-training.
I don't know what homie means, but someone calls me that. I believe this is an interesting natural language processing. I don't know what homie means, but I've been called that and I can feel the meaning.
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
---
|
Pinwheel/wav2vec2-large-xls-r-1b-hindi
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 4
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AdapterHub/bert-base-uncased-pf-emotion
|
[
"bert",
"en",
"dataset:emotion",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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}
| 165
| null |
---
license: apache-2.0
---
## Introduction
PanGu-Alpha-Evolution is an enhanced version of Pangu-Alpha, which can better understand and process tasks, and better follow your task description. More technical details will be updated continuously, please pay attention.
[[Technical report](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha/src/branch/master/PANGU-%ce%b1.pdf)]
### Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("superqing/pangu-evolution")
model = AutoModelForCausalLM.from_pretrained("superqing/pangu-evolution", trust_remote_code=True)
```
|
AdapterHub/roberta-base-pf-conll2003_pos
|
[
"roberta",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/conll2003"
] |
token-classification
|
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}
| 2
| 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: 253.98 +/- 27.16
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
...
```
|
AdapterHub/roberta-base-pf-ud_pos
|
[
"roberta",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/ud_ewt"
] |
token-classification
|
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}
}
}
| 8
| null |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
Ajay191191/autonlp-Test-530014983
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
}
| 34
| null |
---
library_name: stable-baselines3
tags:
- MiniGrid-DoorKey-5x5-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MiniGrid-DoorKey-5x5-v0
type: MiniGrid-DoorKey-5x5-v0
metrics:
- type: mean_reward
value: 0.97 +/- 0.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **MiniGrid-DoorKey-5x5-v0**
This is a trained model of a **PPO** agent playing **MiniGrid-DoorKey-5x5-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-DoorKey-5x5-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-DoorKey-5x5-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('clip_range', 0.2),
('ent_coef', 0.0),
('env_wrapper', 'gym_minigrid.wrappers.FlatObsWrapper'),
('gae_lambda', 0.95),
('gamma', 0.99),
('learning_rate', 0.00025),
('n_envs', 8),
('n_epochs', 10),
('n_steps', 128),
('n_timesteps', 100000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
Akashpb13/Hausa_xlsr
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
],
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}
| 31
| 2023-03-31T10:42:12Z
|
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-it`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-it |
|:---------------------------|:----------------------|:-------------------------------------|
| parameter_size_full | 779,396,349 | 228,094,097 |
| parameter_size_embedding | 692,451,072 | 141,865,728 |
| vocab_size | 901,629 | 184,721 |
| compression_rate_full | 100.0 | 29.27 |
| compression_rate_embedding | 100.0 | 20.49 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | | 2 |
|
Aklily/Lilys
|
[] | null |
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| 0
| null |
---
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_pipeline
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9191333537
license: cc-by-4.0
library_name: spacy
---
## de_STTS2_folk tagger
This is a spaCy language model trained to use the Stuttgart-Tübingen Tagset version 2.0, which was designed to tag transcripts of conversational speech in German.
The model may be useful for tagging ASR transcripts such as those collected in the [CoGS](https://cc.oulu.fi/~scoats/CoGS.html) corpus.
The model was trained using the tag annotations from the FOLK corpus at https://agd.ids-mannheim.de/folk-gold.shtml, employing an 80/20 training/test split. Tokens in the training data for the model were converted to lower case prior to traning to match the format used for automatic speech recognition transcripts on YouTube, as of early 2023.
Usage example:
```python
!pip install https://huggingface.co/stcoats/de_STTS2_folk/resolve/main/de_STTS2_folk-any-py3-none-any.whl
import spacy
import de_STTS2_folk
nlp = de_STTS2_folk.load()
doc = nlp("ach so meinst du wir sollen es jetzt tun")
for token in doc:
print(token.text, token.tag_)
```
### References
Coats, Steven. (In review).
Westpfahl, Swantje and Thomas Schmidt. (2016): [FOLK-Gold – A GOLD standard for Part-of-Speech-Tagging of Spoken German](https://aclanthology.org/L16-1237). In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia. Paris: European Language Resources Association (ELRA), pp. 1493-1499.
---
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_STTS2_folk
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9191333537
---
| Feature | Description |
| --- | --- |
| **Name** | `de_STTS2_folk` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger` |
| **Components** | `tok2vec`, `tagger` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | Swantje Westpfahl and Thomas Schmidt, FOLK-Gold, https://agd.ids-mannheim.de/folk-gold.shtml |
| **License** | CC-BY 4.0 |
| **Author** | Steven Coats |
### Label Scheme
<details>
<summary>View label scheme (62 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$.`, `AB`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NGAKW`, `NGHES`, `NGIRR`, `NGONO`, `NN`, `ORD`, `PDAT`, `PDS`, `PIAT`, `PIDAT`, `PIDS`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PTKA`, `PTKIFG`, `PTKMA`, `PTKMWL`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `SEDM`, `SEQU`, `SPELL`, `TRUNC`, `UI`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 91.91 |
| `TOK2VEC_LOSS` | 478891.28 |
| `TAGGER_LOSS` | 402526.03 |
|
AkshatSurolia/BEiT-FaceMask-Finetuned
|
[
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
image-classification
|
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| 239
| null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('JKSoon/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
AlanDev/DallEMiniButBetter
|
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| 0
| null |
# `vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-5000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-5000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 67.36 | 67.36 | 67.36 | 67.11 | 67.36 | 67.93 | 67.36 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it/raw/main/eval.json).
|
AlanDev/dall-e-better
|
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| 0
| null |
---
license: apache-2.0
datasets:
- lambdasec/cve-single-line-fixes
- lambdasec/gh-top-1000-projects-vulns
language:
- code
tags:
- code
programming_language:
- Java
- JavaScript
- Python
inference: false
model-index:
- name: SantaFixer
results:
- task:
type: text-generation
dataset:
type: openai/human-eval-infilling
name: HumanEval
metrics:
- name: single-line infilling pass@1
type: pass@1
value: 0.47
verified: false
- name: single-line infilling pass@10
type: pass@10
value: 0.74
verified: false
- task:
type: text-generation
dataset:
type: lambdasec/gh-top-1000-projects-vulns
name: GH Top 1000 Projects Vulnerabilities
metrics:
- name: pass@1 (Java)
type: pass@1
value: 0.26
verified: false
- name: pass@10 (Java)
type: pass@10
value: 0.48
verified: false
- name: pass@1 (Python)
type: pass@1
value: 0.31
verified: false
- name: pass@10 (Python)
type: pass@10
value: 0.56
verified: false
- name: pass@1 (JavaScript)
type: pass@1
value: 0.36
verified: false
- name: pass@10 (JavaScript)
type: pass@10
value: 0.62
verified: false
---
# Model Card for SantaFixer
<!-- Provide a quick summary of what the model is/does. -->
This is a LLM for code that is focussed on generating bug fixes using infilling.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [codelion](https://huggingface.co/codelion)
- **Model type:** GPT-2
- **Finetuned from model:** [bigcode/santacoder](https://huggingface.co/bigcode/santacoder)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "lambdasec/santafixer"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint,
trust_remote_code=True).to(device)
input_text = "<fim-prefix>def print_hello_world():\n
<fim-suffix>\n print('Hello world!')
<fim-middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
## Training Details
- **GPU:** Tesla P100
- **Time:** ~5 hrs
### Training Data
The model was fine-tuned on the [CVE single line fixes dataset](https://huggingface.co/datasets/lambdasec/cve-single-line-fixes)
### Training Procedure
Supervised Fine Tuning (SFT)
#### Training Hyperparameters
- **optim:** adafactor
- **gradient_accumulation_steps:** 4
- **gradient_checkpointing:** true
- **fp16:** false
## Evaluation
The model was tested with the [GitHub top 1000 projects vulnerabilities dataset](https://huggingface.co/datasets/lambdasec/gh-top-1000-projects-vulns)
|
AlanDev/test
|
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| 0
| 2023-03-31T11:30:08Z
|
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-it-10000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-it-10000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 93,735,186 |
| parameter_size_embedding | 692,451,072 | 7,681,536 |
| vocab_size | 901,629 | 10,002 |
| compression_rate_full | 100.0 | 12.03 |
| compression_rate_embedding | 100.0 | 1.11 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 10000 | 2 |
|
AlbertHSU/BertTEST
|
[
"pytorch"
] | null |
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| 8
| 2023-03-31T11:30:08Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fedcsis-slot_baseline-xlm_r-en
results: []
datasets:
- cartesinus/leyzer-fedcsis
language:
- en
---
<!-- 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. -->
# fedcsis-slot_baseline-xlm_r-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[leyzer-fedcsis](https://huggingface.co/cartesinus/leyzer-fedcsis) dataset.
Results on test set:
- Precision: 0.7767
- Recall: 0.7991
- F1: 0.7877
- Accuracy: 0.9425
It achieves the following results on the evaluation set:
- Loss: 0.1097
- Precision: 0.9705
- Recall: 0.9723
- F1: 0.9714
- Accuracy: 0.9859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2866 | 1.0 | 814 | 0.3188 | 0.8661 | 0.8672 | 0.8666 | 0.9250 |
| 0.1956 | 2.0 | 1628 | 0.1299 | 0.9409 | 0.9471 | 0.9440 | 0.9736 |
| 0.1063 | 3.0 | 2442 | 0.1196 | 0.9537 | 0.9607 | 0.9572 | 0.9810 |
| 0.0558 | 4.0 | 3256 | 0.0789 | 0.9661 | 0.9697 | 0.9679 | 0.9854 |
| 0.0367 | 5.0 | 4070 | 0.0824 | 0.9685 | 0.9690 | 0.9687 | 0.9848 |
| 0.031 | 6.0 | 4884 | 0.0887 | 0.9712 | 0.9728 | 0.9720 | 0.9859 |
| 0.0233 | 7.0 | 5698 | 0.0829 | 0.9736 | 0.9744 | 0.9740 | 0.9872 |
| 0.0139 | 8.0 | 6512 | 0.0879 | 0.9743 | 0.9747 | 0.9745 | 0.9876 |
| 0.007 | 9.0 | 7326 | 0.0978 | 0.9740 | 0.9734 | 0.9737 | 0.9870 |
| 0.0076 | 10.0 | 8140 | 0.1015 | 0.9723 | 0.9726 | 0.9725 | 0.9860 |
| 0.026 | 11.0 | 814 | 0.1264 | 0.9732 | 0.9620 | 0.9676 | 0.9829 |
| 0.0189 | 12.0 | 1628 | 0.0975 | 0.9732 | 0.9711 | 0.9722 | 0.9861 |
| 0.0099 | 13.0 | 2442 | 0.1080 | 0.9721 | 0.9715 | 0.9718 | 0.9866 |
| 0.0052 | 14.0 | 3256 | 0.1052 | 0.9706 | 0.9715 | 0.9710 | 0.9860 |
| 0.0031 | 15.0 | 4070 | 0.1097 | 0.9705 | 0.9723 | 0.9714 | 0.9859 |
### Per slot evaluation on test set
| slot_name | precision | recall | f1 | tc_size |
|-----------|-----------|--------|----|---------|
| album | 0.7000 | 0.8750 | 0.7778 | 8 |
| album_name | 0.9091 | 0.6250 | 0.7407 | 16 |
| album_type | 0.1842 | 0.5385 | 0.2745 | 13 |
| album_type_1a | 0.0000 | 0.0000 | 0.0000 | 10 |
| album_type_an | 0.0000 | 0.0000 | 0.0000 | 20 |
| all_lang | 0.5556 | 0.7143 | 0.6250 | 7 |
| artist | 0.7500 | 0.7857 | 0.7674 | 42 |
| av_alias | 0.8333 | 0.5263 | 0.6452 | 19 |
| caption | 0.8065 | 0.7576 | 0.7813 | 33 |
| category | 0.8571 | 1.0000 | 0.9231 | 18 |
| channel | 0.6786 | 0.8085 | 0.7379 | 47 |
| channel_id | 0.7826 | 0.9000 | 0.8372 | 20 |
| count | 0.5714 | 1.0000 | 0.7273 | 4 |
| date | 0.8333 | 0.7500 | 0.7895 | 40 |
| date_day | 1.0000 | 1.0000 | 1.0000 | 4 |
| date_month | 1.0000 | 1.0000 | 1.0000 | 8 |
| device_name | 0.8621 | 0.7576 | 0.8065 | 33 |
| email | 1.0000 | 1.0000 | 1.0000 | 16 |
| event_name | 0.5467 | 0.5325 | 0.5395 | 77 |
| file_name | 0.7333 | 0.7857 | 0.7586 | 14 |
| file_size | 1.0000 | 1.0000 | 1.0000 | 1 |
| filename | 0.7083 | 0.7391 | 0.7234 | 23 |
| filter | 0.8333 | 0.9375 | 0.8824 | 16 |
| from | 1.0000 | 1.0000 | 1.0000 | 33 |
| hashtag | 1.0000 | 0.6000 | 0.7500 | 10 |
| img_query | 0.9388 | 0.9246 | 0.9316 | 199 |
| label | 0.2500 | 1.0000 | 0.4000 | 1 |
| location | 0.8319 | 0.9171 | 0.8724 | 205 |
| mail | 1.0000 | 1.0000 | 1.0000 | 2 |
| massage | 0.0000 | 0.0000 | 0.0000 | 1 |
| mesage | 0.0000 | 0.0000 | 0.0000 | 1 |
| message | 0.5856 | 0.5285 | 0.5556 | 123 |
| mime_type | 0.6667 | 1.0000 | 0.8000 | 2 |
| name | 0.9412 | 0.8767 | 0.9078 | 73 |
| pathname | 0.7805 | 0.6809 | 0.7273 | 47 |
| percent | 1.0000 | 0.9583 | 0.9787 | 24 |
| phone_number | 1.0000 | 1.0000 | 1.0000 | 48 |
| phone_type | 1.0000 | 0.9375 | 0.9677 | 16 |
| picture_url | 1.0000 | 1.0000 | 1.0000 | 14 |
| playlist | 0.7219 | 0.8134 | 0.7649 | 134 |
| portal | 0.8108 | 0.7692 | 0.7895 | 39 |
| power | 1.0000 | 1.0000 | 1.0000 | 1 |
| priority | 0.6667 | 1.0000 | 0.8000 | 2 |
| purpose | 1.0000 | 1.0000 | 1.0000 | 8 |
| query | 0.6706 | 0.6064 | 0.6369 | 94 |
| rating | 0.9167 | 0.9167 | 0.9167 | 12 |
| review_count | 0.8750 | 0.7778 | 0.8235 | 9 |
| section | 0.9091 | 0.9091 | 0.9091 | 22 |
| seek_time | 0.6667 | 1.0000 | 0.8000 | 2 |
| sender | 0.6000 | 0.6000 | 0.6000 | 10 |
| sender_address | 0.6364 | 0.8750 | 0.7368 | 8 |
| song | 0.5476 | 0.6133 | 0.5786 | 75 |
| src_lang_de | 0.8765 | 0.9467 | 0.9103 | 75 |
| src_lang_en | 0.6604 | 0.6481 | 0.6542 | 54 |
| src_lang_es | 0.8132 | 0.9024 | 0.8555 | 82 |
| src_lang_fr | 0.8795 | 0.9125 | 0.8957 | 80 |
| src_lang_it | 0.8590 | 0.9437 | 0.8993 | 71 |
| src_lang_pl | 0.7910 | 0.8833 | 0.8346 | 60 |
| state | 1.0000 | 1.0000 | 1.0000 | 1 |
| status | 0.5455 | 0.5000 | 0.5217 | 12 |
| subject | 0.6154 | 0.5581 | 0.5854 | 86 |
| text_de | 0.9091 | 0.9091 | 0.9091 | 77 |
| text_en | 0.5909 | 0.5843 | 0.5876 | 89 |
| text_es | 0.7935 | 0.8111 | 0.8022 | 90 |
| text_esi | 0.0000 | 0.0000 | 0.0000 | 1 |
| text_fr | 0.9125 | 0.8588 | 0.8848 | 85 |
| text_it | 0.8205 | 0.9014 | 0.8591 | 71 |
| text_multi | 0.3333 | 1.0000 | 0.5000 | 1 |
| text_pl | 0.8167 | 0.7656 | 0.7903 | 64 |
| time | 0.8750 | 1.0000 | 0.9333 | 7 |
| to | 0.8927 | 0.9186 | 0.9054 | 172 |
| topic | 0.4000 | 0.6667 | 0.5000 | 3 |
| translator | 0.7991 | 0.9777 | 0.8794 | 179 |
| trg_lang_de | 0.8116 | 0.8615 | 0.8358 | 65 |
| trg_lang_en | 0.8841 | 0.8841 | 0.8841 | 69 |
| trg_lang_es | 0.8906 | 0.8769 | 0.8837 | 65 |
| trg_lang_fr | 0.8676 | 0.9365 | 0.9008 | 63 |
| trg_lang_general | 0.8235 | 0.7368 | 0.7778 | 19 |
| trg_lang_it | 0.8254 | 0.8667 | 0.8455 | 60 |
| trg_lang_pl | 0.8077 | 0.8630 | 0.8344 | 73 |
| txt_query | 0.5714 | 0.7059 | 0.6316 | 17 |
| username | 0.6875 | 0.7333 | 0.7097 | 15 |
| value | 0.7500 | 0.8571 | 0.8000 | 14 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AlbertHSU/ChineseFoodBert
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 15
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Yepes_5e-05_30_03_recall
results: []
datasets:
- Brizape/amia_ibo2
---
<!-- 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. -->
# Yepes_5e-05_30_03
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
## Model description
trained on amia_ibo2; metric for best model: eval/recall
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Aleksandar/bert-srb-ner-setimes
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
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| 8
| null |
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-it-15000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-it-15000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 97,580,186 |
| parameter_size_embedding | 692,451,072 | 11,521,536 |
| vocab_size | 901,629 | 15,002 |
| compression_rate_full | 100.0 | 12.52 |
| compression_rate_embedding | 100.0 | 1.66 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 15000 | 2 |
|
Aleksandar/distilbert-srb-ner-setimes-lr
|
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| 0
| null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {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)
```
## 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 449 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 898,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Aleksandar1932/gpt2-pop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 8
| null |
# `vocabtrimmer/xlm-v-base-trimmed-it-15000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-15000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-15000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 66.55 | 66.55 | 66.55 | 66.12 | 66.55 | 69.61 | 66.55 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-it-15000-tweet-sentiment-it/raw/main/eval.json).
|
Aleksandra/distilbert-base-uncased-finetuned-squad
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Yepes_5e-05_31_03
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. -->
# Yepes_5e-05_31_03
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1043
- Precision: 0.7017
- Recall: 0.5476
- F1: 0.6152
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4466 | 1.92 | 25 | 0.1761 | 0.0 | 0.0 | 0.0 | 0.9704 |
| 0.1328 | 3.85 | 50 | 0.1094 | 0.2957 | 0.1799 | 0.2237 | 0.9747 |
| 0.0932 | 5.77 | 75 | 0.0997 | 0.5656 | 0.3307 | 0.4174 | 0.9793 |
| 0.0627 | 7.69 | 100 | 0.0822 | 0.6353 | 0.4471 | 0.5248 | 0.9822 |
| 0.0455 | 9.62 | 125 | 0.0815 | 0.7116 | 0.5026 | 0.5891 | 0.9838 |
| 0.0332 | 11.54 | 150 | 0.0929 | 0.7302 | 0.4868 | 0.5841 | 0.9829 |
| 0.0246 | 13.46 | 175 | 0.0912 | 0.6894 | 0.5344 | 0.6021 | 0.9826 |
| 0.0195 | 15.38 | 200 | 0.1108 | 0.708 | 0.4683 | 0.5637 | 0.9828 |
| 0.0179 | 17.31 | 225 | 0.0881 | 0.7021 | 0.5423 | 0.6119 | 0.9837 |
| 0.0132 | 19.23 | 250 | 0.1011 | 0.7053 | 0.5635 | 0.6265 | 0.9837 |
| 0.0104 | 21.15 | 275 | 0.1043 | 0.7017 | 0.5476 | 0.6152 | 0.9834 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Aleksandra/herbert-base-cased-finetuned-squad
|
[
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible"
] |
question-answering
|
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"BertForQuestionAnswering"
],
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| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-distilbert-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-distilbert-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7943
- Accuracy: 0.6515
- F1: 0.6222
## 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
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AlekseyKulnevich/Pegasus-QuestionGeneration
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
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| 17
| 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: 465.60 +/- 103.20
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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AlekseyKulnevich/Pegasus-Summarization
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
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| 7
| null |
# `vocabtrimmer/xlm-v-base-trimmed-it-30000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-30000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-30000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 60.46 | 60.46 | 60.46 | 60.04 | 60.46 | 65.07 | 60.46 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-it-30000-tweet-sentiment-it/raw/main/eval.json).
|
Alexandru/creative_copilot
|
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| 0
| null |
# GPT4 x Alpaca
As a base model we used: https://huggingface.co/chavinlo/alpaca-13b
Finetuned on GPT4's responses, for 3 epochs.
NO LORA
Please do note that the configurations files maybe messed up, this is because of the trainer I used. I WILL NOT EDIT THEM because there are repos hat automatically fix this, changing it might break it. Generally you just need to change anything that's under the name of "LLaMa" to "Llama" NOTE THE UPPER AND LOWER CASE!!!!
|
AliPotter24/a
|
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| 0
| null |
---
license: openrail
language:
- as
library_name: open_clip
tags:
- code
---
|
AliReza/distilbert-emotion
|
[] | null |
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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 playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pelinbalci/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"])
```
|
Alicanke/Wyau
|
[] | null |
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| 0
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.66 +/- 0.28
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Alifarsi/t5-small-finetuned-xsum
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ikitracks_netzero
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. -->
# ikitracks_netzero
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5963
- F1: 0.8424
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5967 | 1.0 | 109 | 0.6004 | 0.7168 |
| 0.3709 | 2.0 | 218 | 0.6017 | 0.8215 |
| 0.1412 | 3.0 | 327 | 0.5071 | 0.8851 |
| 0.0604 | 4.0 | 436 | 0.5599 | 0.8851 |
| 0.0365 | 5.0 | 545 | 0.5963 | 0.8424 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Aliraza47/BERT
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1531
- Rouge1: 0.1799
- Rouge2: 0.1086
- Rougel: 0.1599
- Rougelsum: 0.1598
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2365 | 1.0 | 1635 | 0.1723 | 0.1782 | 0.1055 | 0.1575 | 0.1575 | 19.0 |
| 0.209 | 2.0 | 3270 | 0.1596 | 0.1787 | 0.1067 | 0.1584 | 0.1584 | 19.0 |
| 0.1986 | 3.0 | 4905 | 0.1545 | 0.1794 | 0.1079 | 0.1593 | 0.1593 | 19.0 |
| 0.1917 | 4.0 | 6540 | 0.1531 | 0.1799 | 0.1086 | 0.1599 | 0.1598 | 19.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Alireza-rw/testbot
|
[] | null |
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| 0
| 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: 500.00 +/- 0.00
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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Alireza1044/albert-base-v2-cola
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
| 32
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.20 +/- 15.73
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Alireza1044/albert-base-v2-qnli
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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}
| 41
| null |
---
license: apache-2.0
tags:
- text generation
- conversational
- gptq
- 4bit
inference: false
language:
- en
pipeline_tag: text-generation
---
GPTQ quantization of https://huggingface.co/KoboldAI/PPO_Pygway-6b-Mix
Using this repository: https://github.com/mayaeary/GPTQ-for-LLaMa/tree/gptj-v2
Command:
```
python3 gptj.py models/PPO_Pygway-6b-Mix c4 --wbits 4 --groupsize 128 --save_safetensors models/PPO_Pygway-6b-Mix-4bit-128g.safetensors
```
|
Alireza1044/albert-base-v2-qqp
|
[
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
],
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}
| 37
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5361146089547957
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8228
- Matthews Correlation: 0.5361
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5241 | 1.0 | 535 | 0.5480 | 0.4006 |
| 0.3496 | 2.0 | 1070 | 0.5164 | 0.4819 |
| 0.2387 | 3.0 | 1605 | 0.6022 | 0.5138 |
| 0.1779 | 4.0 | 2140 | 0.7458 | 0.5280 |
| 0.127 | 5.0 | 2675 | 0.8228 | 0.5361 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Alireza1044/albert-base-v2-rte
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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"AlbertForSequenceClassification"
],
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}
| 30
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pelinbalci/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"])
```
|
Alireza1044/albert-base-v2-sst2
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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"AlbertForSequenceClassification"
],
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}
| 52
| 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: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9280261795203244
---
<!-- 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: 0.2130
- Accuracy: 0.928
- F1: 0.9280
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8337 | 1.0 | 250 | 0.3003 | 0.909 | 0.9063 |
| 0.2437 | 2.0 | 500 | 0.2130 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Alireza1044/albert-base-v2-stsb
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
| 37
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Corianas/poca-SoccerTwos2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Aliskin/xlm-roberta-base-finetuned-marc
|
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| 0
| 2023-03-31T13:55:58Z
|
---
license: gpl-3.0
tags:
- DocVQA
- Document Question Answering
- Document Visual Question Answering
datasets:
- rubentito/mp-docvqa
language:
- en
---
# Hi-VT5 base fine-tuned on MP-DocVQA
This is Hierarchical Visual T5 (Hi-VT5) base fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
This model was proposed in [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
- Results on the MP-DocVQA dataset are reported in Table 2.
- Training hyperparameters can be found in Table 8 of Appendix D.
<b style="color: #ff0000">Disclaimer</b>: Due to some issues, this model does not achieve as good results as the reported ones in the paper. Please refer to the [project Github](https://github.com/rubenpt91/MP-DocVQA-Framework) for more details.
## How to use
Hi-VT5 is not integrated into HF yet. Please download the code from [Github repository](https://github.com/rubenpt91/MP-DocVQA-Framework) and follow the instructions.
## Metrics
**Average Normalized Levenshtein Similarity (ANLS)**
The standard metric for text-based VQA tasks (ST-VQA and DocVQA). It evaluates the method's reasoning capabilities while smoothly penalizes OCR recognition errors.
Check [Scene Text Visual Question Answering](https://arxiv.org/abs/1905.13648) for detailed information.
**Answer Page Prediction Accuracy (APPA)**
In the MP-DocVQA task, the models can provide the index of the page where the information required to answer the question is located. For this subtask accuracy is used to evaluate the predictions: i.e. if the predicted page is correct or not.
Check [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/abs/2212.05935) for detailed information.
## Model results
Extended experimentation can be found in Table 2 of [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
You can also check the live leaderboard at the [RRC Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4).
| Model | HF name | Parameters | ANLS | APPA |
|-----------------------------------------------------------------------------------|:--------------------------------------|:-------------:|:-------------:|:---------:|
| [Bert large](https://huggingface.co/rubentito/bert-large-mpdocvqa) | rubentito/bert-large-mpdocvqa | 334M | 0.4183 | 51.6177 |
| [Longformer base](https://huggingface.co/rubentito/longformer-base-mpdocvqa) | rubentito/longformer-base-mpdocvqa | 148M | 0.5287 | 71.1696 |
| [BigBird ITC base](https://huggingface.co/rubentito/bigbird-base-itc-mpdocvqa) | rubentito/bigbird-base-itc-mpdocvqa | 131M | 0.4929 | 67.5433 |
| [LayoutLMv3 base](https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa) | rubentito/layoutlmv3-base-mpdocvqa | 125M | 0.4538 | 51.9426 |
| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa) | rubentito/t5-base-mpdocvqa | 223M | 0.5050 | 0.0000 |
| [**Hi-VT5**](https://huggingface.co/rubentito/hivt5-base-mpdocvqa) | rubentito/hivt5-base-mpdocvqa | 316M | 0.6201 | 79.23 |
## Citation Information
```tex
@article{tito2022hierarchical,
title={Hierarchical multimodal transformers for Multi-Page DocVQA},
author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest},
journal={arXiv preprint arXiv:2212.05935},
year={2022}
}
```
|
Amalq/roberta-base-finetuned-schizophreniaReddit2
|
[
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
}
| 5
| null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# argilla/alpaca-gigo-detector
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("argilla/alpaca-gigo-detector")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Amba/wav2vec2-large-xls-r-300m-tr-colab
|
[] | null |
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},
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}
}
| 0
| null |
---
tags:
- generated_from_trainer
datasets:
- afrispeech-200
metrics:
- wer
model-index:
- name: whisper-small-hi-2400_500_132
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: afrispeech-200
type: afrispeech-200
config: hausa
split: train
args: hausa
metrics:
- name: Wer
type: wer
value: 0.3433857983900036
---
<!-- 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. -->
# whisper-small-hi-2400_500_132
This model is a fine-tuned version of [saif-daoud/whisper-small-hi-2400_500_131](https://huggingface.co/saif-daoud/whisper-small-hi-2400_500_131) on the afrispeech-200 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8127
- Wer: 0.3434
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0318 | 0.5 | 900 | 0.8252 | 0.3442 |
| 0.9844 | 1.5 | 1800 | 0.8127 | 0.3434 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Amrrs/south-indian-foods
|
[
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] |
image-classification
|
{
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
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},
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},
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},
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}
}
}
| 21
| null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1749.05 +/- 115.55
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
AndreLiu1225/t5-news-summarizer
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
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},
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"prefix": null
}
}
}
| 10
| null |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Garfieldgx/autotrain-data-severe-js100-sentiment
co2_eq_emissions:
emissions: 0.9273951637568196
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274
## Validation Metrics
- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Garfieldgx/autotrain-severe-js100-sentiment-45485113858
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Garfieldgx/autotrain-severe-js100-sentiment-45485113858", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Garfieldgx/autotrain-severe-js100-sentiment-45485113858", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 4
| null |
Access to model zhixuan2/warp2image-psc is restricted and you are not in the authorized list. Visit https://huggingface.co/zhixuan2/warp2image-psc to ask for access.
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: amr-model-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. -->
# amr-model-2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1594 | 17.24 | 500 | 1.1475 |
| 1.0059 | 34.48 | 1000 | 1.3365 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
| 4
| null |
---
tags:
- generated_from_keras_callback
model-index:
- name: ru_propaganda_model_without_foreign_agent_mask_large
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. -->
# ru_propaganda_model_without_foreign_agent_mask_large
This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0094
- Validation Loss: 0.0965
- Train Accuracy: 0.9786
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2205, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2574 | 0.1273 | 0.9286 | 0 |
| 0.1036 | 0.0830 | 0.9643 | 1 |
| 0.0358 | 0.0711 | 0.9786 | 2 |
| 0.0179 | 0.0778 | 0.9714 | 3 |
| 0.0094 | 0.0965 | 0.9786 | 4 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
| 8
| null |
tokenizer="roberta-base"
model="roberta-base"
epochs=3
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
| 4
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.14 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Ibnout/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"])
```
|
AnonymousSub/SR_rule_based_twostagetriplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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},
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}
| 2
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: lmqg/mbart-large-cc25-squad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 56.23
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 74.73
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 43.17
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 92.7
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 84.01
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 76.98
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 62.63
---
# Model Card of `lmqg/mbart-large-cc25-squad-qa`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question answering task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/mbart-large-cc25-squad-qa")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 62.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 76.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 92.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 69.46 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 64.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 60.19 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 56.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 43.17 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 84.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 74.73 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 16
- batch: 16
- lr: 6e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 4
| null |
Access to model Tophe/kjvmodel is restricted and you are not in the authorized list. Visit https://huggingface.co/Tophe/kjvmodel to ask for access.
|
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": {
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},
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}
}
}
| 1
| null |
---
license: cc-by-nc-4.0
datasets:
- tatsu-lab/alpaca
library_name: transformers
pipeline_tag: text-generation
tags:
- galactica
- alpaca
- opt
inference: false
---
# GALPACA 6.7B (standard)
GALACTICA 6.7B fine-tuned on the Alpaca dataset.
The model card from the original Galactica repo can be found [here](https://github.com/paperswithcode/galai/blob/main/docs/model_card.md), and the original paper [here](https://galactica.org/paper.pdf).
The dataset card for Alpaca can be found [here](https://huggingface.co/datasets/tatsu-lab/alpaca/blob/main/README.md), and the project homepage [here](https://crfm.stanford.edu/2023/03/13/alpaca.html).
The Alpaca dataset was collected with a modified version of the [Self-Instruct Framework](https://github.com/yizhongw/self-instruct), and was built using OpenAI's `text-davinci-003` model. As such it is subject to OpenAI's terms of service.
## Model Details
The GALACTICA models are trained on a large-scale scientific corpus and are designed to perform scientific tasks.
The Alpaca dataset is a set of 52k instruct-response pairs designed to enhace the instruction following capabilites of pre-trained language models.
## Model Use
The GALACTICA model card specifies that the primary indended users of the GALACTICA models are researchers studying language models applied to the scientific domain, and it cautions against production use of GALACTICA without safeguards due to the potential for the model to produce inaccurate information.
The original GALACTICA models are available under a non-commercial CC BY-NC 4.0 license, and the GALPACA model is additionally subject to the [OpenAI Terms of Service](https://openai.com/policies/terms-of-use).
## Training Data
The GALPACA models are trained by fine-tuning pre-trained GALACTICA models on the Alpaca dataset. GALACTICA models were trained on 106 billion tokens of open-access scientific text and data, including papers, textbooks, scientific websites, encyclopedias, and more.
Fine-tuning the base GALACTICA models on the 52k instruction-response pairs in the Alpaca dataset allows users to query the GALPACA models in an instruct-response fashion.
## How to Use
The GALPACA weights are made available for use with the `transformers` library.
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("GeorgiaTechResearchInstitute/galpaca-6.7b")
model = OPTForCausalLM.from_pretrained("GeorgiaTechResearchInstitute/galpaca-6.7b", device_map="auto", torch_dtype=torch.float16)
# see the original Alpaca repo for more information about the prompt templates
no_input_prompt_template = ("Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:")
prompt = "Write out Maxwell's equations and explain the meaning of each one."
formatted_prompt = no_input_prompt_template.format_map({'instruction': prompt})
tokenized_prompt = tokenizer(formatted_prompt, return_tensors="pt").input_ids.to(model.device)
out_tokens = model.generate(tokenized_prompt)
print(tokenizer.batch_decode(out_tokens, skip_special_tokens=False, clean_up_tokenization_spaces=False))
```
</details>
## Training Resources
GALPACA 6.7B was fine-tuned in about 2 hours using 4 A100 80GB GPUS, 16-bit mixed-precision, an effective batch-size of 128, and with a maximum context window of 512 tokens. This model was trained using full-shard data parallelism.
## Performance and Limitations
Qualitative evaluation suggests that Galpaca frequently outperforms LLaMA-based Alpaca models on tasks related to technical knowledge and programming, while it underperforms on natural langauge tasks such as generating prose. More information about the performance and limitations of the GALACTICA family of models can be found on the original GALACTICA model card.
## Works Cited
```bibtex
@inproceedings{GALACTICA,
title={GALACTICA: A Large Language Model for Science},
author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic},
year={2022}
}
```
```bibtex
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
}
| 6
| null |
tokenizer="roberta-base"
model="roberta-base"
epochs=5
|
AnonymousSub/cline-papers-roberta-0.585
|
[
"pytorch",
"roberta",
"transformers"
] | null |
{
"architectures": [
"LecbertForPreTraining"
],
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}
}
| 1
| 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 +/- 2.63
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Ibnout/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"])
```
|
AnonymousSub/cline-s10-SR
|
[] | null |
{
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}
}
| 0
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.04 +/- 6.09
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Nazzyk/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/consert-techqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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}
}
| 4
| null |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
duplicated_from: gsdf/Replicant
---
# Please enable hires. fix when using it.
Replicant is built by merging several models with fine-tuning WD1.4 and photorealistic SD2.0 models that works with danbooru tags.I trained 4 models to merge and prepared several LoRa models for tuning.As with SD1.x, merging individually trained models is better quality than training many concepts at once.This model is a workflow test and is not good enough. WD1.4 seems to vary greatly in quality with/without Hires. fix.In Replicant, the difference in quality is more noticeable because of the detailed drawings.So I recommend enabling Hires.fix for use.
# Example
Denoising strength 0.6 is a bit large. I like 0.57 better.
The optimal CFG Scale value should also be examined.
Hands often multiply. When this happens, increase the value of "extra hands".
((masterpiece, best quality)), 1girl, flower, solo, dress, holding, sky, cloud, hat, outdoors, bangs, bouquet, rose, expressionless, blush, pink hair, flower field, red flower, pink eyes, white dress, looking at viewer, midium hair, holding flower, small breasts, red rose, holding bouquet, sun hat, white headwear, depth of field
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, skirt, shoes, solo, jacket, holding, alley, sitting, can, sneakers, hood, bag, hoodie, squatting, bangs, shirt, black hair, black skirt, short hair, white jacket, looking away, white footwear, full body, red eyes, long sleeves, open jacket, open clothes, holding can,
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra legs, extra hands, fewer digits , long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes,drinking
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, blood, solo, wings, halo, dress, socks, angel, long hair, shoes, standing, ribbon, long hair, blue eyes, angel wings, blood on clothes, white hair, full body, white wings, black footwear, white dress, feathered wings, white sock, white background, long sleeves, simple background,
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra legs, extra hands, fewer digits , long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 384x576, Denoising strength: 0.57, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, car, solo, shorts, jacket, bangs, sitting, shirt, shoes, hairclip, socks, sneakers, denim, sidelocks, motor vehicle, long hair, ground vehicle,brown hair, looking at viewer, white shirt, black jacket, long sleeves, sports car, vehicle focus, aqua eyes, white socks, blue shorts, open clothes, black footwear, denim shorts, open jacket
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 384x576, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, solo, twintails, lollipop, smile, ahoge, hairclip, bow, holding, ribbon, frills, blush, shirt, :d, stuffed toy, pink hair, stuffed animal, red nails, hair ornament, open mouth, looking at viewer, stuffed bunny, nail polish, short sleeves, object hug, puffy sleeves, hair between eyes, upper body, light blue eyes, puffy short sleeves, holding stuffed toy, hair bow, white bow, doll hug, hair ribbon, streaked hair, white shirt
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 512x512, Denoising strength: 0.57, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, solo, tail, barefoot, skirt, sleeping, lying, grass, shirt, outdoors, socks, flower, long hair, on side, animal ears, blonde hair, cat tail, closed eyes, blue skirt, white shirt, cat ears, school uniform, dappled sunlight, short sleeves, bare legs, closed mouth, full body, pleated skirt
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent
((masterpiece, best quality)), 1girl, car, building, gun, weapon, outdoors, solo, military, day, city, standing, serious, pants, rifle, holding, jacket, motor vehicle, ground vehicle, brown hair, assault rifle, long hair, vehicle focus, holding gun, holding weapon, black footwear, military vehicle, full body, depth of field,
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent
|
AnonymousSub/declutr-biomed-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
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}
}
| 7
| null |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.34 +/- 0.47
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pinaggle/q-FrozenLake-v1-8x8-Slippery", 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"])
```
|
AnonymousSub/declutr-emanuals-s10-SR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 28
| null |
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: English2AkuapemTwi
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. -->
# English2AkuapemTwi
This model is a fine-tuned version of [allenai/unifiedqa-t5-small](https://huggingface.co/allenai/unifiedqa-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3098
- Bleu: 8.8875
- Gen Len: 17.5114
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 2.8304 | 1.0 | 2542 | 2.5363 | 0.2404 | 18.9072 |
| 2.5079 | 2.0 | 5084 | 2.2533 | 0.5763 | 18.6814 |
| 2.3362 | 3.0 | 7626 | 2.0838 | 0.8836 | 18.4972 |
| 2.2155 | 4.0 | 10168 | 1.9649 | 1.3748 | 18.2368 |
| 2.1066 | 5.0 | 12710 | 1.8767 | 2.0954 | 18.0588 |
| 2.0368 | 6.0 | 15252 | 1.8057 | 2.6294 | 17.9451 |
| 1.9641 | 7.0 | 17794 | 1.7436 | 3.0267 | 17.8342 |
| 1.9194 | 8.0 | 20336 | 1.6907 | 3.4947 | 18.0199 |
| 1.8474 | 9.0 | 22878 | 1.6438 | 4.0431 | 17.7758 |
| 1.8261 | 10.0 | 25420 | 1.6008 | 4.4608 | 17.7919 |
| 1.7833 | 11.0 | 27962 | 1.5642 | 4.9271 | 17.7868 |
| 1.7255 | 12.0 | 30504 | 1.5322 | 5.3659 | 17.7962 |
| 1.6913 | 13.0 | 33046 | 1.5020 | 5.9051 | 17.7301 |
| 1.6616 | 14.0 | 35588 | 1.4780 | 6.0824 | 17.7217 |
| 1.6522 | 15.0 | 38130 | 1.4550 | 6.6326 | 17.6227 |
| 1.6039 | 16.0 | 40672 | 1.4327 | 6.7955 | 17.6564 |
| 1.5926 | 17.0 | 43214 | 1.4119 | 7.0477 | 17.6876 |
| 1.5802 | 18.0 | 45756 | 1.3960 | 7.4328 | 17.6233 |
| 1.5438 | 19.0 | 48298 | 1.3816 | 7.4937 | 17.6359 |
| 1.5452 | 20.0 | 50840 | 1.3695 | 7.7696 | 17.5515 |
| 1.5423 | 21.0 | 53382 | 1.3569 | 7.9677 | 17.5596 |
| 1.5076 | 22.0 | 55924 | 1.3465 | 8.2829 | 17.5193 |
| 1.5183 | 23.0 | 58466 | 1.3386 | 8.4304 | 17.5486 |
| 1.4919 | 24.0 | 61008 | 1.3296 | 8.536 | 17.5714 |
| 1.4923 | 25.0 | 63550 | 1.3242 | 8.6446 | 17.4343 |
| 1.488 | 26.0 | 66092 | 1.3177 | 8.7099 | 17.5126 |
| 1.477 | 27.0 | 68634 | 1.3144 | 8.8431 | 17.5035 |
| 1.4724 | 28.0 | 71176 | 1.3116 | 8.8588 | 17.5277 |
| 1.4488 | 29.0 | 73718 | 1.3105 | 8.8989 | 17.5024 |
| 1.4837 | 30.0 | 76260 | 1.3098 | 8.8875 | 17.5114 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/declutr-emanuals-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
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}
}
}
| 4
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-Bengali
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: bn
split: train+validation
args: bn
metrics:
- name: Wer
type: wer
value: 0.5156123435499465
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-Bengali
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4707
- Wer: 0.5156
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5484 | 2.85 | 500 | 1.6415 | 1.0493 |
| 0.8446 | 5.71 | 1000 | 0.5394 | 0.6886 |
| 0.4604 | 8.57 | 1500 | 0.4741 | 0.6090 |
| 0.3466 | 11.43 | 2000 | 0.4685 | 0.5788 |
| 0.2707 | 14.28 | 2500 | 0.4741 | 0.5449 |
| 0.2317 | 17.14 | 3000 | 0.4661 | 0.5157 |
| 0.2024 | 20.0 | 3500 | 0.4707 | 0.5156 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/declutr-model
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 4
| null |
---
duplicated_from: Defpoint/Defmix-v2.0
---
<br>
# ■*Defmix-v2.0*
◎<strong>*Defmix-v2.0*</strong>は、下記のモデルをMBWによって*U-Net*の階層ごとに重みを変化させてマージしたモデルです。<br>
<strong>*Defmix-v2.0*</strong> is a model that merges the following models by adjusting the weights of each layer in *U-Net*.<br>
- <strong>*Counterfeit v2.5*</strong>
- <strong>*Basil Mix*</strong>
- <strong>*Abyss Orange Mix v3.0 A2*</strong>
◎*Vae*ファイルは好みのものを使用してください。<br>
Please use the *Vae* file of your preference.<br>
<br>
# ■*Examples*
◎*ControlNet*が登場したことから、このモデルは*Defmix-v1.0*と異なり、構図や人物と背景のバランスよりも全体の描画力や質感を重視しています。<br>
With the introduction of *ControlNet*, this model, unlike *Defmix-v1.0*, emphasizes overall drawing power and texture rather than composition and balance between characters and backgrounds.<br>
◎現在広く使われている<strong>クオリティタグ(best qualityやmasterpieceなど)を使用してなくても</strong>、高品質な画像が出力されるように調整しています。<br>
I have adjusted the output to ensure high-quality images are produced, <strong>even without using commonly used Quality Tags</strong> such as 'best quality' or 'masterpiece'.<br>
<br>
- *Sampler: DPM++ 2M Karras*
- *Steps: 28*
- *CFG Scale: 8*
- *Clip Skip: 2*
- *Upscaler: Latent(nearest)*
- *Highres Step: 0*
- *Denoising strength: 0.6*
<br>
Positive: beautiful girl, gothic<br>
Negative: EasyNegative
<br>
<img src="https://i.imgur.com/a25fE5f.jpeg" width="768" height="768">
<br>
# ■*Important Reminders*
◎画風をかなり現実的にすることができるため、<strong>このモデルによって出力したR-18のNSFW画像をSNSサイト等で公開することはご遠慮頂きますよう</strong>、よろしくお願い致します。<br>
As this model can make the style of images quite realistic, <strong>I kindly request that you refrain from posting R-18 NSFW images generated by this model on social media or other websites.</strong> <br>
Thank you for your understanding and cooperation.
<br>
|
AnonymousSub/declutr-model_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 26
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Ashwin0/mt5-small-finetuned-amazon-en-es
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. -->
# Ashwin0/mt5-small-finetuned-amazon-en-es
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:
- Train Loss: 6.5637
- Validation Loss: 4.4146
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 2418, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.6408 | 4.8732 | 0 |
| 6.7679 | 4.4146 | 1 |
| 6.5518 | 4.4146 | 2 |
| 6.5538 | 4.4146 | 3 |
| 6.5289 | 4.4146 | 4 |
| 6.5397 | 4.4146 | 5 |
| 6.5474 | 4.4146 | 6 |
| 6.5637 | 4.4146 | 7 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/declutr-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
| 4
| null |
---
license: cc0-1.0
tags:
- stable-diffusion
- text-to-image
duplicated_from: thiros/YuzuLemonTea
---
# YuzuLemonTea Mix models ☕
List of my experimental merge models
- [Recommended Settings](#recommended-setteings)
- [YuzuLemonMilk](#yuzulemonmilk)
- [YuzuLemonChaiLatte](#yuzulemonchailatte)
- [YuzuGinger](#yuzuginger)
# important notice(Jan 15/23)
According to bbc-mc's note, there is a possibility of bug that some token(prompt) can be ignored, when merge with "add difference" option.
Milk and ChaiLatte models are now replaced with bug-fix ver.
https://note.com/bbcmc/n/n12c05bf109cc
# Recommended Setteings
VAE: "kl-f8-anime2" and "vae-ft-mse-840000-ema-pruned" are suitable
Steps: 20-30, Sampler: DPM++ SDE Karras or DPM++ 2M Karras, CFG scale: 8, Clip skip: 2, ENSD: 31377, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased),Denoising strength: 0.54~0.7
Negataive Prompt: (worst quality:2), (low quality:2),inaccurate limb,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name
- (worst quality), (low quality) are adjustable between 1.4~2.0
- If you don't want 3DCG-ish paint, you can add (3d:0.8)~1.0 in Negative Prompt
# Sample prompt
4girls,(a 3d reader of:0.8) (teenage loli children:1.2), (wearing intricate casual camisole, cute hair ornament,crop jacket,hot pants, tighhigh:1.1),
shiny brown skin,
looking at viewer, (alluring smug:1.2),
dynamic angle,
(onomichi street:1.2),fisheye
<img src="https://i.imgur.com/2JiZwFU.jpg" width="" height="1000">
# YuzuLemonMilk
Block merged model of Anything v3 and some real models.
Rather photo realistic.
Works fine with positive (realistic) and (photo realistic).
<img src="https://i.imgur.com/qYK8DKn.jpg" width="" height="1000">
# YuzuLemonChaiLatte
Combination of a weight merge of ACertainModel and Anything-V3.0, and a block merge of several realistic models.
Rather anime-ish style with realistic background.
- v3.5
<img src="https://i.imgur.com/WLKr3pj.jpg" width="" height="1000">
- v9.5
<img src="https://i.imgur.com/Ufh3JK2.jpg" width="" height="1000">
# YuzuGinger
Add more anime models to YuzuLemonChaiLatte. Can be very anime looks.
- v1
<img src="https://i.imgur.com/4vc4HSL.jpg" width="" height="1000">
- v4
<img src="https://i.imgur.com/M6q6hYp.jpg" width="" height="1000">
|
AnonymousSub/dummy_2_parent
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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| 3
| null |
---
library_name: keras
datasets:
- moizsajid/dreambooth-markhor
pipeline_tag: text-to-image
---
## Model description
This is a Stable Diffusion model fine-tuned using Dreambooth on markhor images.
## Intended uses & limitations
Submission for keras-dreambooth sprint
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | RMSprop |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | 100 |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| rho | 0.9 |
| momentum | 0.0 |
| epsilon | 1e-07 |
| centered | False |
| training_precision | float32 |
|
AnonymousSub/hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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}
| 8
| null |
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
pipeline_tag: text-to-image
duplicated_from: les-chien/X-mix
---
# X-mix
**Civitai**: [X-mix | Stable Diffusion Checkpoint | Civitai](https://civitai.com/models/13069/x-mix)
X-mix is a merging model used to generate anime images. My English is not very good, so there may be some parts of this article that are unclear.
## V2.0
V2.0 is a merged model based on V1.0. This model supports nsfw.
### Difference from V1.0
- The performance of V2.0 is not better than that of V1.0, but the generated images now exhibit a different artistic style.
- V2.0 offers better support for nsfw than V1.0, but the drawback is that even when you do not intend to generate an nsfw image, there is still a possibility of generating one. If you are more interested in the sfw model, I will provide a detailed explanation in the recipe section.
- In my opinion, V2.0 is not as user-friendly as V1.0, and it appears to be more challenging to generate an excellent image.
### Recommended Settings
- Sampler: DPM++ SDE Karras (sfw), DDIM (nsfw)
- Steps: 20 (DDIM may require more steps)
- CFG Scale: 5
- Hires upscale: Latent (bicubic antialiased), Latent (nearest-exact), Denoising strength: 0.4~0.7
- vae: NAI.vae
- Clip skip: 2
- ENSD: 31337
- Eta: 0.67
### Example
```
masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 4291846267, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (nearest-exact), Eta: 0.67
```
```
Indoor, bright, 1Girl, gray hair, amber eyes, smile, black dress, barefoot, sitting posture,
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2118045521, Size: 600x400, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
%2C%20white%20t.png)
```
landscape, in spring, cherry blossoms, cloudy sky, 1girl, solo, long blue hair, smirk, pink eyes, (school uniform:1.05), white thighhighs,
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 3093571233, Size: 400x600, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
1girl, on bed, wet, see-through shirt, thighhighs, cleavage, collarbone, full body,
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 986400693, Size: 512x512, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
%2C%20solo%2C%20Flowery%20meadow%2C%20cloudy%20sky%2C%20aqua%20eyes%2C%20white%20pantyhose%2C%20blonde%20hair%2C.png)
```
Alice \(Alice in wonderland\), solo, Flowery meadow, cloudy sky, aqua eyes, white pantyhose, blonde hair,
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 273840053, Size: 512x512, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
masterpiece, best quality, ultra-detailed, illustration, portrait, hakurei reimu, 1girl, throne room, dimly lit
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2212365348, Size: 512x512, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (nearest-exact), Eta: 0.67
```
```
masterpiece, best quality, ultra-detailed, illustration, 1girl, witch hat, purple eyes, blonde hair, wielding a purple staff blasting purple energy, purple beam, purple effects, dragons, chaos
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DDIM, CFG scale: 5, Seed: 293615512, Size: 512x512, Model hash: 7961a4960e, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (nearest-exact)
```
```
1girl, solo, black skirt, blue eyes, electric guitar, guitar, headphones, holding, holding plectrum, instrument, long hair, , music, one side up, pink hair, playing guitar, pleated skirt, black shirt, indoors
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2, DeepNegative-V1-75T
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 3442031040, Size: 512x512, Model hash: 7961a4960e, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (nearest-exact), Eta: 0.67
```
### Recipe
**Step 1:** animefull-latest (model) + pastelmix-lora (lora) + ligneClaireStyleCogecha (lora) = pastel-Cogecha
You can try replacing animefull-latest with Anything-V3.0 or your preferred model. However, I cannot confirm if this will yield better results and it requires you to experiment with it on your own.
**Step 2:** MBW: Chilloutmix + X-mix-V1.0
| Model A | Model B | base_alpha | Weight | Merge Name |
| ----------- | ---------- | ---------- | ------------------------------------------------- | --------------- |
| Chilloutmix | X-mix-V1.0 | 1 | 1,1,1,1,1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,1,1,1,1,1,1 | X-mix-V2.0-base |
This is the step of the sfw version. The steps for the nsfw version are as follows: I merged several LoRAs into Chilloutmix to obtain Chilloutmix-nsfw. Then I merged Chilloutmix-nsfw and X-mix-V1.0 to get X-mix-V2.0-nsfwBase1. Finally, I merged several LoRAs into X-mix-V2.0-nsfwBase1 to get X-mix-V2.0-nsfwBase2.
LoRAs related to real people should be merged into Chilloutmix or other photo-realistic models that you like, while LoRAs related to anime should be merged into X-mix-V2.0-base. Which LoRAs to use depends on your preference.
**Step 3:** MBW: pastel-Cogecha + X-mix-V2.0-base
| Model A | Model B | base_alpha | Weight | Merge Name |
| -------------- | --------------- | ---------- | ------------------------------------------------------- | -------------- |
| pastel-Cogecha | X-mix-V2.0-base | 0 | 1,1,1,1,1,0.3,0,0,0,1,0.1,1,1,1,1,1,0,1,0,1,1,0.2,1,1,1 | X-mix-V2.0-sfw |
In fact, I never tried to obtain the sfw version because I didn't plan on using it from the beginning. So this process is for reference only, and I am not sure about the actual effect of the sfw model.
## V1.0
I have forgotten the recipe for X-mix-V1.0, as too many models were used for merging. This model supports nsfw, but the effect may not be very good.
### Recommended Settings
- Sampler: DPM++ SDE Karras
- Steps: 20
- CFG Scale: 5
- Hires upscaler: Latent (bicubic antialiased), Denoising strength: 0.5~0.6
- vae: NAI.vae
- Clip skip: 2
- ENSD: 31337
- Eta: 0.67
### Examples
```
masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 1906918205, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
Indoor, bright, 1girl, gray hair, amber eyes, smile, black dress, barefoot, sitting posture,
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2118045521, Size: 600x400, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
landscape, in spring, cherry blossoms, cloudy sky, 1girl, solo, long blue hair, smirk, pink eyes, (school uniform:1.05), white thighhighs,
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 3093571233, Size: 400x600, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
1girl, on bed, wet, see-through shirt, thighhighs, cleavage, collarbone, full body,
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 1666118295, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
Alice \(Alice in wonderland\), solo, Flowery meadow, cloudy sky, aqua eyes, white pantyhose, blonde hair,
Negative prompt: EasyNegative, sketch by bad-artist
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 807449917, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.5, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (nearest-exact), Eta: 0.67
```
```
masterpiece, best quality, ultra-detailed, illustration, portrait, hakurei reimu, 1girl, throne room, dimly lit
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 116927034, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
masterpiece, best quality, ultra-detailed, illustration, 1girl, witch hat, purple eyes, blonde hair, wielding a purple staff blasting purple energy, purple beam, purple effects, dragons, chaos
Negative prompt: EasyNegative, photograph by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 1705759664, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
```
1girl, solo, black skirt, blue eyes, electric guitar, guitar, headphones, holding, holding plectrum, instrument, long hair, , music, one side up, pink hair, playing guitar, pleated skirt, black shirt, indoors
Negative prompt: EasyNegative, by bad-artist, bad_prompt_version2
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2548407675, Size: 512x512, Model hash: 7bc4c05c90, Denoising strength: 0.55, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased), Eta: 0.67
```
## Embedding
If you need the embedding used in examples, click them:
- **EasyNegative:** [embed/EasyNegative · Hugging Face](https://huggingface.co/embed/EasyNegative)
- **bad-artist:** [nick-x-hacker/bad-artist · Hugging Face](https://huggingface.co/nick-x-hacker/bad-artist)
- **bad_prompt_version2:** [embed/bad_prompt · Hugging Face](https://huggingface.co/embed/bad_prompt)
- **Deep Negative V1.x:** [Deep Negative V1.x | Stable Diffusion TextualInversion | Civitai](https://civitai.com/models/4629/deep-negative-v1x)
You can consider whether to use them according to your preferences.
## More
1. Since my prompts are usually brief, I'm not sure if this model will be able to meet all of your requirements if you need to use a large number of prompts.
2. Using low resolution is **not recommended** for generating pictures.
3. I did my best, but the hands are not perfect.
4. The above settings may not necessarily be perfect.
5. Due to my computer's performance, it's difficult for me to comprehensively test this model. I'm looking forward to your feedback.
|
AnonymousSub/hier_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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| 8
| null |
---
language: is
datasets:
- language-and-voice-lab/samromur_asr
- language-and-voice-lab/samromur_children
- language-and-voice-lab/malromur_asr
- language-and-voice-lab/althingi_asr
tags:
- audio
- automatic-speech-recognition
- icelandic
- whisper
- whisper-large
- iceland
- reykjavik
- samromur
license: cc-by-4.0
widget:
model-index:
- name: whisper-large-icelandic-10k-steps-1000h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Samrómur (Test)
type: language-and-voice-lab/samromur_asr
split: test
args:
language: is
metrics:
- name: WER
type: wer
value: 11.879
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Samrómur (Dev)
type: language-and-voice-lab/samromur_asr
split: validation
args:
language: is
metrics:
- name: WER
type: wer
value: 10.849
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Samrómur Children (Test)
type: language-and-voice-lab/samromur_children
split: test
args:
language: is
metrics:
- name: WER
type: wer
value: 12.325
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Samrómur Children (Dev)
type: language-and-voice-lab/samromur_children
split: validation
args:
language: is
metrics:
- name: WER
type: wer
value: 8.078
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Malrómur (Test)
type: language-and-voice-lab/malromur_asr
split: test
args:
language: is
metrics:
- name: WER
type: wer
value: 10.132
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Malrómur (Dev)
type: language-and-voice-lab/malromur_asr
split: validation
args:
language: is
metrics:
- name: WER
type: wer
value: 10.157
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Althingi (Test)
type: language-and-voice-lab/althingi_asr
split: test
args:
language: is
metrics:
- name: WER
type: wer
value: 11.750
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Althingi (Dev)
type: language-and-voice-lab/althingi_asr
split: validation
args:
language: is
metrics:
- name: WER
type: wer
value: 11.141
---
# whisper-large-icelandic-10k-steps-1000h
The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" with around 1000 hours of Icelandic data developed by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab). Most of the data is available at public repositories such as [LDC](https://www.ldc.upenn.edu/), [OpenSLR](https://openslr.org/) or [Clarin.is](https://clarin.is/)
The specific list of corpora used to fine-tune the model is:
- [Samrómur 21.05 (114h34m)](http://www.openslr.org/112/)
- [Samrómur Children (127h25m)](https://catalog.ldc.upenn.edu/LDC2022S11)
- [Malrómur (119hh03m)](https://clarin.is/en/resources/malromur/)
- [Althingi Parliamentary Speech (514h29m)](https://catalog.ldc.upenn.edu/LDC2021S01)
- L2-Speakers Data (125h55m) **Unpublished material**
The fine-tuning process was performed during March (2023) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
# Evaluation
```python
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/whisper-large-icelandic-10k-steps-1000h"
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda")
#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("language-and-voice-lab/samromur_children",split='test')
#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
#Process the dataset
def map_to_pred(batch):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
batch["reference"] = processor.tokenizer._normalize(batch['normalized_text'])
with torch.no_grad():
predicted_ids = model.generate(input_features.to("cuda"))[0]
transcription = processor.decode(predicted_ids)
batch["prediction"] = processor.tokenizer._normalize(transcription)
return batch
#Do the evaluation
result = ds.map(map_to_pred)
#Compute the overall WER now.
from evaluate import load
wer = load("wer")
WER=100 * wer.compute(references=result["reference"], predictions=result["prediction"])
print(WER)
```
**Test Result**: 12.325364793542379
# BibTeX entry and citation info
*When publishing results based on these models please refer to:*
```bibtex
@misc{mena2023whisperlarge10kicelandic,
title={Acoustic Model in Icelandic: whisper-large-icelandic-10k-steps-1000h.},
author={Hernandez Mena, Carlos Daniel},
year={2023},
url={https://huggingface.co/carlosdanielhernandezmena/whisper-large-icelandic-10k-steps-1000h},
}
```
# Acknowledgements
Thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.
|
AnonymousSub/roberta-base_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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}
| 25
| 2023-03-31T21:37:09Z
|
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: robotics
---
# Model Card: VC-1 (Visual Cortex)
Last updated: 2023-03-28
Version: 1.0
- Code: https://github.com/facebookresearch/eai-vc
- Other Links:
[VC-1 Website](https://eai-vc.github.io/),
[VC-1 Blogpost](https://ai.facebook.com/blog/robots-learning-video-simulation-artificial-visual-cortex-vc-1),
[VC-1 Paper](https://ai.facebook.com/research/publications/where-are-we-in-the-search-for-an-artificial-visual-cortex-for-embodied-intelligence/),
[VC-1 Demo](https://github.com/facebookresearch/eai-vc/blob/main/tutorial/tutorial_vc.ipynb)
The VC-1 model is a vision transformer (ViT) pre-trained on over 4,000 hours of egocentric videos from 7 different sources, together with ImageNet. The model is trained using Masked Auto-Encoding (MAE) and is available in two sizes: ViT-B and ViT-L. The model is intended for use for EmbodiedAI tasks, such as object manipulation and indoor navigation.
## Model Details
- Model Name: VC-1 (Vision Transformer-based model)
- Architecture:
- Patch size: 16x16
- Embedding dimension: 768
- Number of layers: 12
- Number of heads: 12
- MLP ratio: 4
- QKV bias: True
- Layer normalization: eps=1e-6
- Inputs: Images presented in 224x224x3.
- Outputs: 768x1 embedding.
- Image Size: 224
- Use of Classification Token: True
- Dropout Rate: 0.0
- Algorithm: MAE
- Epochs trained: 182
- Model authors: Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, and Franziska Meier.
- Person of Contact: Oleksandr Maksymets (FAIR)
## Citation
If you use this model, please cite:
```bibtex
@inproceedings{majumdar2023vc1,
title = {Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?},
author = {Arjun Majumdar and Karmesh Yadav and Sergio Arnaud and Yecheng Jason Ma and Claire Chen and Sneha Silwal and Aryan Jain and Vincent-Pierre Berges and Pieter Abbeel and Jitendra Malik and Dhruv Batra and Yixin Lin and Oleksandr Maksymets and Aravind Rajeswaran and Franziska Meier},
publisher = {arXiv},
year = {2023}
}
```
## Model Data
Training data:
The VC-1 model was trained on a large-scale dataset of egocentric videos, consisting of over 5.6 million frames. The dataset includes three modalities: manipulation, navigation, and object recognition. The manipulation modality includes videos of people performing various manipulations, such as cooking, cleaning, and tool use. The navigation modality includes videos of people moving around in indoor environments, such as homes and offices. The object recognition modality includes images from the ImageNet dataset, which contains over 1.2 million images of objects in various categories.
This table provides an overview of the assembled datasets used for scaling hypothesis experiments, including the total number of frames and the frames used for each dataset:
| Dataset | Contains | Total Frames | Frames used |
| ----------------------|:-----------:|:-------------:|:-----------:|
| Ego4D | Ego4D | 418,578,043 | 2,790,520 |
| | | | |
| EgoM (Manipulation) | Ego4D | 418,578,043 | 2,790,520 |
| | 100DOH | 99,899 | 99,899 |
| | SS-v2 | 25,209,271 | 315,115 |
| | Epic Kitchens| 19,965,439 | 332,757 |
| | | Total | 3,538,291 |
| | | | |
| EgoO (OpenHouse24) | Ego4D | 418,578,043 | 2,790,520 |
| | OpenHouse24 | 27,806,971 | 499,442 |
| | | Total | 3,289,962 |
| | | | |
| EgoN (Navigation) | Ego4D | 418,578,043 | 2,790,520 |
| | OpenHouse24 | 27,806,971 | 499,442 |
| | RealEstate10K| 10,000,000 | 303,087 |
| | | Total | 3,289,962 |
| | | | |
| EgoMN (Manipulation, Navigation) | Ego4D+M | 3,538,291 | 3,538,291 |
| | OpenHouse24 | 27,806,971 | 499,442 |
| | RealEstate10K| 10,000,000 | 303,087 |
| | | Total | 4,340,820 |
| | | | |
| EgoMNI (Manipulation, Navigation, ImageNet) | Ego4D+MN | 4,340,820 | 4,340,820 |
| | ImageNet | 1,281,167 | 1,281,167 |
| | | Total | 5,621,987 |
The VC-1 models were trained on EgoMNI (Manipulation, Navigation, ImageNet) assembled dataset.
Evaluation data (see also section [Evaluation Results](#performance)
below):
The mode was evaluated on CortexBench that includes 17 tasks from 7 benchmarks and described below:
| Benchmark | Tasks |
|-----------|-------|
| Adroit | Relocate, Reorient-Pen |
| MetaWorld | Assembly, Bin-Picking, Button-Press, Drawer-Open, Hammer |
| DeepMind Control | Finger-Spin, Reacher-Hard, Cheetah-Run, Walker-Stand, Walker-Walk |
| TriFinger | Reach-Cube, Push-Cube |
| Habitat | Image-Goal Navigation (ImageNav), Object-Goal Navigation (ObjectNav) |
| Habitat 2.0 | Mobile Pick |
## Model Creation & Maintenance
The VC-1 model was created by pre-training ViT-B and ViT-L on a combination of egocentric videos and ImageNet using Masked Auto-Encoding (MAE). The model is maintained by the authors and is available for open-source use.
## Model Usage
The VC-1 model is intended for EmbodiedAI tasks, such as object manipulation and indoor navigation.. The model outputs embeddings for image frame, which can be used as features for downstream tasks:
```
from vc_models.models.vit import model_utils
model,embd_size,model_transforms,model_info = model_utils.load_model(model_utils.VC1_BASE_NAME)
#the img loaded should be Bx3x250x250
img = your_function_here ...
#output will be of size Bx3x224x224
transformed_img = model_transforms(img)
#img will be 1x768
embedding = model(transformed_img)
```
## Performance
The performance of the models on the CortexBench:
| Model | Adroit | Meta-World | DMControl | Trifinger | ObjectNav | ImageNav | Mobile Pick | Mean Rank | Mean Success |
| ------------------------ | ------------ | ---------------------- | ------------------- | --------------------- | ------------ | ------------ | ----------------- | --------- | -------------- |
| Ego4D (VIT-B) | 48.7 ± 1.3 | 86.1 ± 2.1 | 64.1 ± 2.3 | 68.3 ± 1.1 | 46.8 ± 1.1 | 64.0 ± 0.7 | 57.4 ± 2.2 | 8.6 | 62.2 |
| Ego4D (VIT-L) | 50.0 ± 1.2 | 92.9 ± 2.4 | 60.8 ± 3.3 | 69.7 ± 0.5 | 47.6 ± 1.1 | 55.8 ± 0.8 | 67.6 ± 2.1 | 5.9 | 63.5 |
| Ego4D+N (VIT-B) | 50.0 ± 2.4 | 86.4 ± 2.9 | 59.5 ± 2.4 | 67.8 ± 1.3 | 54.7 ± 1.1 | 68.7 ± 0.7 | 59.4 ± 2.2 | 7.2 | 63.8 |
| Ego4D+N (VIT-L) | 54.0 ± 1.2 | 89.1 ± 2.9 | 66.4 ± 1.7 | 66.9 ± 0.4 | 57.4 ± 1.1 | 70.5 ± 0.7 | 65.2 ± 2.1 | 3.5 | 67.1 |
| Ego4D+M (VIT-B) | 51.3 ± 2.4 | 83.5 ± 2.6 | 64.3 ± 1.8 | 69.1 ± 0.4 | 47.3 ± 1.1 | 65.8 ± 0.7 | 59.8 ± 2.2 | 7.0 | 63.0 |
| Ego4D+M (VIT-L) | 52.0 ± 1.3 | 88.3 ± 3.2 | 64.7 ± 2.4 | 64.7 ± 0.9 | 47.3 ± 1.1 | 65.5 ± 0.7 | 68.6 ± 2.1 | 6.0 | 64.4 |
| VC-1: Ego4D+MN (VIT-B) | 48.7 ± 2.4 | 85.3 ± 5.2 | 64.2 ± 1.9 | 70.3 ± 0.5 | 52.8 ± 1.1 | 68.9 ± 0.7 | 58.6 ± 2.2 | 6.9 | 64.1 |
| VC-1: Ego4D + MNI (VIT-L) | 59.3 ± 5.2 | 88.8 ± 2.2 | 66.9 ± 1.4 | 71.7 ± 0.4 | 60.3 ± 1.1 | 70.3 ± 0.7 | 63.2 ± 2.2 | 2.4 | 68.7 |
## Limitations
The VC-1 model has been evaluated on a limited set of benchmarks and may not perform as well on other tasks. While we have focused on masked auto-encoders as the pre-training objective and ViT as the architecture in our study, there may be other SSL algorithms that exhibit different scaling behaviors or superior performance on the proposed datasets in our benchmark.
Additionally, the VC-1 model is computationally expensive to train and may not be practical for all use cases. The large size of the model may also pose challenges for deployment on resource-constrained devices.
It is important to note that although we utilize real-world images and videos for pre-training our visual representation models (PVRs), the evaluation benchmarks used in this study serve as proxies for actual robotic tasks. Therefore, the performance of the PVR models on real robots may differ from the rankings established in this study. Further research is necessary to fully evaluate the effectiveness of these models in real-world scenarios.
Finally, while we have made efforts to ensure fairness and avoid bias in our benchmark selection, it is possible that certain demographics or use cases may not be adequately represented in our evaluation tasks. Future work could explore additional benchmarks that address a wider range of scenarios and demographics.
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 8
| null |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
duplicated_from: dosukebewitch/WhiteMixs
---
## Examples
```
(masterpiece, best quality:1.2), 1girl,
NP: (worst quality, low quality, medium quality:1.4), (depth of field, blurry:1.2),
Steps: 35, Sampler: DPM++ SDE Karras, CFG scale: 7,
Seed: 2405394752, Size: 512x832, Model hash: 3dd41b2474,
Denoising strength: 0.3, Clip skip: 2, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
```
(masterpiece:1.2), 1girl, undertaker,
NP: (worst quality, low quality, medium quality:1.4), (depth of field, blurry:1.2), bad anatomy, bad hands, text, missing fingers, extra digit, fewer digits,
Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7,
Seed: 3862355089, Size: 512x832, Model hash: 3dd41b2474,
Denoising strength: 0.3, Clip skip: 2, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
```
(masterpiece:1.2), 1girl, off-shoulder sweater,
NP: (worst quality, low quality, medium quality:1.4), (depth of field, blurry:1.2), bad anatomy, bad hands, text, missing fingers, extra digit, fewer digits,
Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7,
Seed: 3862355089, Size: 512x832, Model hash: 3dd41b2474,
Denoising strength: 0.3, Clip skip: 2, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 4
| null |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- aiartchan
duplicated_from: AIARTCHAN/7pa
---
# 7pa
[원본글](https://arca.live/b/aiart/70729603)
[civitai](https://civitai.com/models/13468)
# Download
- [original 4.27GB](https://civitai.com/api/download/models/15869)
- [fp16 2.13GB](https://huggingface.co/AIARTCHAN/7pa/blob/main/7pa-fp16.safetensors)
7th anime v3 + 파스텔 + 어비스오렌지2(sfw)
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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| 3
| null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tateandlyle_1.0
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
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
---
<!-- 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. -->
# tateandlyle_1.0
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0100
- 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
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.85 | 100 | 0.4898 | 0.4570 | 0.2717 | 0.3407 | 0.8833 |
| No log | 3.7 | 200 | 0.1878 | 0.8613 | 0.8071 | 0.8333 | 0.9670 |
| No log | 5.56 | 300 | 0.0765 | 0.9518 | 0.9331 | 0.9423 | 0.9914 |
| No log | 7.41 | 400 | 0.0359 | 0.9961 | 0.9961 | 0.9961 | 0.9987 |
| 0.3458 | 9.26 | 500 | 0.0216 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3458 | 11.11 | 600 | 0.0156 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3458 | 12.96 | 700 | 0.0127 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3458 | 14.81 | 800 | 0.0111 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3458 | 16.67 | 900 | 0.0103 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0241 | 18.52 | 1000 | 0.0100 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
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feature-extraction
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| 4
| null |
---
license: creativeml-openrail-m
thumbnail: >-
https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/thumbnail.webp
tags:
- stable-diffusion
- text-to-image
- safetensors
- diffusers
inference: true
language:
- en
widget:
- text: >-
masterpiece, masterpiece, masterpiece, best quality, ultra-detailed, 1girl,
hatsune miku, blue hair, upper body, looking at viewer, ?, negative space,
bioluminescence, bioluminescence, bioluminescence, darkness, wind,
butterfly, black background, portrait, ice
example_title: example
library_name: diffusers
duplicated_from: NoCrypt/SomethingV2_2
---
<center>
<img src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/Artboard%201.png"/>
<h1 style="font-size:1.6rem;">
<b>
SomethingV2.2
</b>
</h1>
<p>
Welcome to SomethingV2.2 - an improved anime latent diffusion model from <a href="https://huggingface.co/NoCrypt/SomethingV2">SomethingV2</a>
A lot of things are being discovered lately, such as a way to merge model using mbw automatically, offset noise to get much darker result, and even VAE tuning. This model is intended to use all of those features as the improvements, here's some improvements that have been made:
</p>
<img src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/Artboard%202.png"/>
<h2>Can't trust the numbers? Here's some proof</h2>
</center>
%2C%20dark.png)
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/00019-1829045217-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20hands%20on%20ow.png" width="32%"/>
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/00018-1769428138-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20hands%20on%20ow.png" width="32%"/>
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/00020-3514023396-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20cafe%2C%20angry.png" width="32%"/>
<details><summary><big><b>Prompts</b></big></summary>
```yaml
masterpiece, best quality, ultra-detailed, 2girls, upper body, looking at viewer, ?, negative space, (bioluminescence:1.2), darkness, wind, butterfly, black background, glowing,
AND masterpiece, best quality, ultra-detailed, 2girls, hatsune miku, upper body, looking at viewer, ?, negative space, (bioluminescence:1.2), darkness, wind, butterfly, black background, glowing, (blue theme:1.2)
AND masterpiece, best quality, ultra-detailed, 2girls, hakurei reimu, (brown hair:1.1), upper body, looking at viewer, ?, negative space, (bioluminescence:1.2), darkness, wind, butterfly, black background, glowing, (red theme:1.2)
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3452449180, Size: 816x504, Model: somethingv2_1, Denoising strength: 0.58, Clip skip: 2, ENSD: 31337, Latent Couple: "divisions=1:1,1:2,1:2 positions=0:0,0:0,0:1 weights=0.2,0.8,0.8 end at step=13", Hires upscale: 1.9, Hires steps: 12, Hires upscaler: Latent (nearest-exact)
```
```yaml
masterpiece, best quality, hatsune miku, white shirt, darkness, dark background
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 72332473, Size: 504x600, Model: somethingv2_1, Denoising strength: 0.58, Clip skip: 2, ENSD: 31337, Hires upscale: 1.85, Hires steps: 12, Hires upscaler: Latent (nearest-exact)
```
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, hands on own cheeks, open mouth, one eye closed, clenched teeth, smile
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1829045217, Size: 456x592, Model: SomethingV2_2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, hands on own cheeks, open mouth, eyez closed, clenched teeth, smile, arms behind back,
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1769428138, Size: 456x592, Model: SomethingV2_2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, cafe, angry, crossed arms, detached sleeves, light particles,
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3514023396, Size: 456x592, Model: SomethingV2_2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
</details>
## Non-miku examples
<img style="display:inline;margin:0;padding:0;" width="49%" src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/00021-4018636341-masterpiece%2C%20best%20quality%2C%201girl%2C%20aqua%20eyes%2C%20baseball%20cap%2C%20blonde%20hair%2C%20closed%20mouth%2C%20earrings%2C%20green%20background%2C%20hat%2C%20hoop%20earr.png"/>
<img style="display:inline;margin:0;padding:0;" width="49%" src="https://huggingface.co/NoCrypt/SomethingV2_2/resolve/main/images/00022-1334620477-masterpiece%2C%20best%20quality%2C%20landscape.png"/>
<details><summary><big><b>Prompts</b></big></summary>
```yaml
masterpiece, best quality, 1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 4018636341, Size: 440x592, Model: SomethingV2_2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 13, Hires upscaler: Latent (nearest-exact)
```
```yaml
masterpiece, best quality, landscape
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1334620477, Size: 440x592, Model: SomethingV2_2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 13, Hires upscaler: Latent (nearest-exact)
```
</details>
## Recommended settings
- VAE: None (Baked in model, [blessed2](https://huggingface.co/NoCrypt/blessed_vae/blob/main/blessed2.vae.pt))
- Clip Skip: 2
- Sampler: DPM++ 2M Karras
- CFG Scale: 7 ± 5
- Recommended Positive Prompt: masterpiece, best quality, negative space, (bioluminescence:1.2), darkness, dark background
- Recommended Negative Prompt: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative)
- For better results, using hires fix is a must.
- Hires upscaler: Latent (any variant, such as nearest-exact)
## Recipe
*Due to [SD-Silicon's Terms of use](https://huggingface.co/Xynon/SD-Silicon#terms-of-use). I must specify how the model was made*
|Model A | Model B | Interpolation Method | Weight | Name |
|---|---|---|---|---|
|[dpepmkmp](https://huggingface.co/closertodeath/dpepmkmp/blob/main/dpepmkmp.safetensors)|[silicon29-dark](https://huggingface.co/Xynon/SD-Silicon/blob/main/Silicon29/Silicon29-dark.safetensors)|MBW|Reverse Cosine|[dpepsili](https://huggingface.co/un1xx/model_dump/blob/main/bw-merge-dpepmkmp-Silicon29-dark-0.ckpt)|
|[somethingV2_1](https://huggingface.co/NoCrypt/SomethingV2/blob/main/somethingv2_1.safetensors)|[dpepsili](https://huggingface.co/un1xx/model_dump/blob/main/bw-merge-dpepmkmp-Silicon29-dark-0.ckpt)|MBW|Cosine|SomethingV2_2 raw|
|SomethingV2_2 raw|[Blessed2 VAE](https://huggingface.co/NoCrypt/blessed_vae/blob/main/blessed2.vae.pt)|Bake VAE|-|**[SomethingV2_2](https://huggingface.co/NoCrypt/SomethingV2_2/blob/main/SomethingV2_2.safetensors)**|
## Why not call it SomethingV4?
Since this model was based on SomethingV2 and there's not THAT much of improvements in some condition. Calling it V4 is just not right at the moment 😅
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"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
}
}
}
| 2
| null |
---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
---
# Model card for convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384
A ConvNeXt image-text trained feature representation model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION by Ross Wightman.
Please see related OpenCLIP model cards for more details on pretrain:
* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 216.8
- GMACs: 101.1
- Activations (M): 126.8
- Image size: 384 x 384
- **Papers:**
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- **Original:** https://github.com/mlfoundations/open_clip
- **Pretrain Dataset:** LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 192, 96, 96])
# torch.Size([1, 384, 48, 48])
# torch.Size([1, 768, 24, 24])
# torch.Size([1, 1536, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
AnonymousSub/rule_based_hier_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": {
"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
}
}
}
| 8
| null |
---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
---
# Model card for convnext_large_mlp.clip_laion2b_soup_ft_in12k_320
A ConvNeXt image-text trained feature representation model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION by Ross Wightman.
Please see related OpenCLIP model cards for more details on pretrain:
* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 216.8
- GMACs: 70.2
- Activations (M): 88.0
- Image size: 320 x 320
- **Papers:**
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- **Original:** https://github.com/mlfoundations/open_clip
- **Pretrain Dataset:** LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_large_mlp.clip_laion2b_soup_ft_in12k_320', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_320',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 192, 80, 80])
# torch.Size([1, 384, 40, 40])
# torch.Size([1, 768, 20, 20])
# torch.Size([1, 1536, 10, 10])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_320',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 10, 10) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"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,
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 2
| 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: 249.08 +/- 34.67
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
...
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
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
}
}
}
| 28
| null |
---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
---
# Model card for convnext_large_mlp.clip_laion2b_soup_ft_in12k_384
A ConvNeXt image-text trained feature representation model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION by Ross Wightman.
Please see related OpenCLIP model cards for more details on pretrain:
* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 216.8
- GMACs: 101.1
- Activations (M): 126.8
- Image size: 384 x 384
- **Papers:**
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- **Original:** https://github.com/mlfoundations/open_clip
- **Pretrain Dataset:** LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_large_mlp.clip_laion2b_soup_ft_in12k_384', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_384',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 192, 96, 96])
# torch.Size([1, 384, 48, 48])
# torch.Size([1, 768, 24, 24])
# torch.Size([1, 1536, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_384',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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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,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 1
| null |
---
tags:
- generated_from_trainer
datasets:
- afrispeech-200
metrics:
- wer
model-index:
- name: whisper-small-hi-2400_500_133
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: afrispeech-200
type: afrispeech-200
config: hausa
split: train
args: hausa
metrics:
- name: Wer
type: wer
value: 0.32728583443469905
---
<!-- 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. -->
# whisper-small-hi-2400_500_133
This model is a fine-tuned version of [saif-daoud/whisper-small-hi-2400_500_132](https://huggingface.co/saif-daoud/whisper-small-hi-2400_500_132) on the afrispeech-200 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7843
- Wer: 0.3273
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 540
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9568 | 0.5 | 270 | 0.7916 | 0.3298 |
| 0.9337 | 1.5 | 540 | 0.7843 | 0.3273 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"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,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 4
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 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: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
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