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
|
|---|---|---|---|---|---|---|
Alireza1044/albert-base-v2-cola | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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
}
}
} | 32 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: deberta-v3-large-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.7193201130196331
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-cola
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5335
- Matthews Correlation: 0.7193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Alireza1044/albert-base-v2-rte | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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
}
}
} | 30 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/bart-large-iirc-gold"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Alireza1044/albert-base-v2-sst2 | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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
}
}
} | 52 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/bart-large-iirc-retrieved"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Alireza1044/albert-base-v2-stsb | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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
}
}
} | 37 | null | ---
language:
- vi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- vivos
model-index:
- name: Whisper Small Vietnamese ver1.1 - Son Huynh
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. -->
# Whisper Small Vietnamese ver1.1 - Son Huynh
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the vivos-train dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1925
- eval_wer: 15.6566
- eval_runtime: 498.9405
- eval_samples_per_second: 1.523
- eval_steps_per_second: 0.19
- epoch: 0.27
- step: 200
## 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: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 800
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Alireza1044/albert-base-v2-wnli | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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
}
}
} | 164 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/bart-large-numglue"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Alireza1044/bert_classification_lm | [
"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
}
}
} | 35 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/bart-large-tatqa"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Alireza1044/dwight_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"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
}
}
} | 14 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/t5-large-drop"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Alireza1044/michael_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"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
}
}
} | 10 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/t5-large-iirc-gold"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AlirezaBaneshi/testPersianQA | [
"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
}
}
} | 4 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/t5-large-iirc-retrieved"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Allybaby21/Allysai | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/t5-3b-drop"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Aloka/mbart50-ft-si-en | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"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 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/t5-3b-iirc-gold"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Amro-Kamal/gpt | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/preasm-large-tatqa"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"Who scored the first touchdown of the game?\n" +
"... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Anamika/autonlp-Feedback1-479512837 | [
"pytorch",
"xlm-roberta",
"text-classification",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 34 | 2022-12-03T07:24:05Z | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/poet-large-tatqa"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
Anamika/autonlp-fa-473312409 | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Anamika/autonlp-data-fa",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 35 | 2022-12-03T07:25:06Z | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-bart-large-drop"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 6 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- endpoints-template
inference: true
--- |
AnonymousSub/AR_specter | [
"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
}
}
} | 2 | null | ---
license: gpl-2.0
---
Gad'sImageModels_GUI Source_code(Tkinter)
```
import tkinter as tk
import torch,cv2
win=tk.Tk()
def clear(obj):
obj.place_forget()
def make_images():
v1_v=int(v1.get())
v2_v=int(v2.get())
v3_v=int(v3.get())
v4_v=int(v4.get())
v5_v=int(v5.get())
model=torch.load(model_name.get())
img=model(torch.Tensor([v1_v,v2_v,v3_v,v4_v,v5_v]))
img=img.detach().numpy().reshape(80,80,3)
img=cv2.resize(img,(800,800))
cv2.imshow("imgs.jpg",img)
def newimagesmakescene():
clear(make_images_button)
make_bu=tk.Button(text="+Make",width=95,height=2,background="#3c3c3c",font=(10),fg="#ffffff",command=make_images).place(x=20,y=500)
global model_name
model_name=tk.Entry(background="#3c3c3c",fg="#ffffff",)
model_name.insert(0,"testmodel_1.bin")
model_name.place(x=20,y=20)
global v1
global v2
global v3
global v4
global v5
scale_var = tk.DoubleVar()
v1 = tk.Scale(background="#3c3c3c",orient=tk.HORIZONTAL,length = 300,width = 20,sliderlength = 15,from_ = 0,to = 30, resolution=0.5,tickinterval=5,fg="#ffffff")
v1.place(x=20,y=50)
v2 = tk.Scale(background="#3c3c3c",orient=tk.HORIZONTAL,length = 300,width = 20,sliderlength = 15,from_ = 0,to = 30, resolution=0.5,tickinterval=5,fg="#ffffff")
v2.place(x=20,y=50*2)
v3 = tk.Scale(background="#3c3c3c", orient=tk.HORIZONTAL,length = 300,width = 20,sliderlength = 15,from_ = 0,to = 30, resolution=0.5,tickinterval=5,fg="#ffffff")
v3.place(x=20,y=50*3)
v4 = tk.Scale(background="#3c3c3c",orient=tk.HORIZONTAL,length = 300,width = 20,sliderlength = 15,from_ = 0,to = 30, resolution=0.5,tickinterval=5,fg="#ffffff")
v4.place(x=20,y=50*4)
v5 = tk.Scale(background="#3c3c3c",orient=tk.HORIZONTAL,length = 300,width = 20,sliderlength = 15,from_ = 0,to = 30, resolution=0.5,tickinterval=5,fg="#ffffff")
v5.place(x=20,y=50*5)
win.title("GadaiImage Maker")
win.geometry("1000x600")
win.resizable(0,0)
win.configure(background="#2f2f2f")
make_images_button=tk.Button(text="Image Make",width=95,height=2,background="#3c3c3c",font=(10),fg="#ffffff",command=newimagesmakescene)
make_images_button.place(x=20,y=20)
win.mainloop()
``` |
AnonymousSub/SR_consert | [
"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
}
}
} | 2 | 2022-12-03T16:03:46Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-finetuned-NYT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-finetuned-NYT
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: 2.2519
- Rouge1: 45.692
- Rouge2: 32.1167
- Rougel: 44.3548
- Rougelsum: 44.3959
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 2516 | 2.4293 | 38.1445 | 25.9377 | 36.5758 | 36.6165 |
| No log | 2.0 | 5032 | 2.3661 | 40.356 | 27.7563 | 38.993 | 39.036 |
| No log | 3.0 | 7548 | 2.3225 | 43.6557 | 30.2246 | 42.2529 | 42.2804 |
| No log | 4.0 | 10064 | 2.2852 | 43.7584 | 30.4744 | 42.3437 | 42.3871 |
| No log | 5.0 | 12580 | 2.2677 | 45.6522 | 32.0921 | 44.311 | 44.3587 |
| No log | 6.0 | 15096 | 2.2598 | 45.4426 | 31.7845 | 44.1273 | 44.1394 |
| No log | 7.0 | 17612 | 2.2529 | 45.6841 | 32.1469 | 44.3355 | 44.37 |
| 3.2998 | 8.0 | 20128 | 2.2519 | 45.692 | 32.1167 | 44.3548 | 44.3959 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"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
}
}
} | 6 | null | ---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Czech CV11
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
split: test
config: cs
metrics:
- name: Wer
type: wer
value: 11.689339690370561
---
<!-- 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 Medium Czech CV11
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2537
- Wer: 11.6893
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0384 | 2.02 | 1000 | 0.2167 | 13.5467 |
| 0.0061 | 4.03 | 2000 | 0.2373 | 12.9172 |
| 0.0018 | 6.05 | 3000 | 0.2407 | 12.0409 |
| 0.0007 | 8.07 | 4000 | 0.2463 | 11.7685 |
| 0.0003 | 10.09 | 5000 | 0.2537 | 11.6893 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mtop
model-index:
- name: byt5-small-mtop
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. -->
# byt5-small-mtop
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the mtop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0697
- Exact Match: 0.7620
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 0.5431 | 6.65 | 200 | 0.1532 | 0.0940 |
| 0.073 | 13.33 | 400 | 0.0697 | 0.2823 |
| 0.0247 | 19.98 | 600 | 0.0721 | 0.3007 |
| 0.0128 | 26.65 | 800 | 0.0863 | 0.3038 |
| 0.0082 | 33.33 | 1000 | 0.0950 | 0.3078 |
| 0.0051 | 39.98 | 1200 | 0.0994 | 0.3029 |
| 0.0036 | 46.65 | 1400 | 0.1046 | 0.3074 |
| 0.0027 | 53.33 | 1600 | 0.1022 | 0.3087 |
| 0.0022 | 59.98 | 1800 | 0.1067 | 0.3096 |
| 0.0016 | 66.65 | 2000 | 0.1081 | 0.3101 |
| 0.0011 | 73.33 | 2200 | 0.1141 | 0.3105 |
| 0.0008 | 79.98 | 2400 | 0.1170 | 0.3074 |
| 0.0007 | 86.65 | 2600 | 0.1198 | 0.3083 |
| 0.0006 | 93.33 | 2800 | 0.1212 | 0.3083 |
| 0.0005 | 99.98 | 3000 | 0.1218 | 0.3087 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2.590324397585769e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/SR_rule_based_twostage_quadruplet_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,
"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
}
}
} | 3 | 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: 224.37 +/- 23.28
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/SR_specter | [
"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
}
}
} | 5 | 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(matteopilotto/sd-class-butterflies-64)
image = pipeline().images[0]
image
```
|
AnonymousSub/SciFive_pubmedqa_question_generation | [
"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": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 7 | null | DistilRoberta model trained on squad augmented dataset |
AnonymousSub/T5_pubmedqa_question_generation | [
"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": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | Access to model sd-concepts-library/polki-jewellery is restricted and you are not in the authorized list. Visit https://huggingface.co/sd-concepts-library/polki-jewellery to ask for access. |
AnonymousSub/cline-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 27 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_tweetqa
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/t5-small-tweetqa-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_tweetqa
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 23.73
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 49.86
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 27.89
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 92.19
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 74.57
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 56.12
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 38.49
---
# Model Card of `lmqg/t5-small-tweetqa-qa`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [t5-small](https://huggingface.co/t5-small)
- **Language:** en
- **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (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/t5-small-tweetqa-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/t5-small-tweetqa-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/t5-small-tweetqa-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-------------------------------------------------------------------|
| AnswerExactMatch | 38.49 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| AnswerF1Score | 56.12 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| BERTScore | 92.19 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_1 | 45.54 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_2 | 37.38 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_3 | 29.91 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_4 | 23.73 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| METEOR | 27.89 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| MoverScore | 74.57 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| ROUGE_L | 49.86 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_tweetqa
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: t5-small
- max_length: 512
- max_length_output: 32
- epoch: 7
- batch: 64
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 1
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-tweetqa-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/cline-emanuals-s10-SR | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_tweetqa
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/bart-base-tweetqa-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_tweetqa
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 33.57
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 58.37
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 32.39
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 93.84
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 78.67
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 64.79
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 48.38
---
# Model Card of `lmqg/bart-base-tweetqa-qa`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base)
- **Language:** en
- **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (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/bart-base-tweetqa-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/bart-base-tweetqa-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/bart-base-tweetqa-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-------------------------------------------------------------------|
| AnswerExactMatch | 48.38 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| AnswerF1Score | 64.79 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| BERTScore | 93.84 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_1 | 54.68 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_2 | 46.42 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_3 | 38.97 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_4 | 33.57 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| METEOR | 32.39 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| MoverScore | 78.67 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| ROUGE_L | 58.37 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_tweetqa
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: facebook/bart-base
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 32
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-tweetqa-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/cline-papers-biomed-0.618 | [
"pytorch",
"roberta",
"transformers"
]
| null | {
"architectures": [
"LecbertForPreTraining"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | ---
thumbnail: >-
https://repository-images.githubusercontent.com/523487884/fdb03a69-8353-4387-b5fc-0d85f888a63f
datasets:
- ChristophSchuhmann/improved_aesthetics_6plus
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
duplicated_from: lambdalabs/sd-image-variations-diffusers
---
# Stable Diffusion Image Variations Model Card
This version of Stable Diffusion has been fine tuned from [CompVis/stable-diffusion-v1-3-original](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original) to accept CLIP image embedding rather than text embeddings. This allows the creation of "image variations" similar to DALLE-2 using Stable Diffusion. This version of the weights has been ported to huggingface Diffusers, to use this with the Diffusers library requires the [Lambda Diffusers repo](https://github.com/LambdaLabsML/lambda-diffusers).
## Example
First clone [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers) and install any requirements (in a virtual environment in the example below):
```bash
git clone https://github.com/LambdaLabsML/lambda-diffusers.git
cd lambda-diffusers
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
Then run the following python code:
```python
from pathlib import Path
from lambda_diffusers import StableDiffusionImageEmbedPipeline
from PIL import Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionImageEmbedPipeline.from_pretrained("lambdalabs/sd-image-variations-diffusers")
pipe = pipe.to(device)
im = Image.open("your/input/image/here.jpg")
num_samples = 4
image = pipe(num_samples*[im], guidance_scale=3.0)
image = image["sample"]
base_path = Path("outputs/im2im")
base_path.mkdir(exist_ok=True, parents=True)
for idx, im in enumerate(image):
im.save(base_path/f"{idx:06}.jpg")
```
# Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
This model is fine tuned from Stable Diffusion v1-3 where the text encoder has been replaced with an image encoder. The training procedure is the same as for Stable Diffusion except for the fact that images are encoded through a ViT-L/14 image-encoder including the final projection layer to the CLIP shared embedding space.
- **Hardware:** 4 x A6000 GPUs (provided by [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud))
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Steps**: 87,000
- **Batch:** 6 x 4 = 24
- **Learning rate:** warmup to 0.0001 for 1,000 steps and then kept constant
Training was done using a [modified version of the original Stable Diffusion training code]((https://github.com/justinpinkney/stable-diffusion), the original version of the weights is [here](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).
# Uses
_The following section is adapted from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4)_
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
*This model card was written by: Justin Pinkney and is based on the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).* |
AnonymousSub/cline-papers-roberta-0.585 | [
"pytorch",
"roberta",
"transformers"
]
| null | {
"architectures": [
"LecbertForPreTraining"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small sv-SE - irena
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. -->
# Whisper Small sv-SE - irena
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AnonymousSub/cline-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AnonymousSub/cline-s10-SR | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
example_title: "Answering Extraction Example 1"
- text: "extract answers: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
example_title: "Answering Extraction Example 2"
model-index:
- name: lmqg/t5-small-squad-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 39.23
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 67.58
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 42.5
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 91.2
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 80.92
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 68.06
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 56.15
---
# Model Card of `lmqg/t5-small-squad-ae`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for answer extraction 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:** [t5-small](https://huggingface.co/t5-small)
- **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/t5-small-squad-ae")
# model prediction
answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-ae")
output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 56.15 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 68.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.2 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 52.42 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 47.81 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 43.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 39.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 42.5 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 80.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 67.58 | 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_sentence']
- output_types: ['answer']
- prefix_types: ['ae']
- model: t5-small
- max_length: 512
- max_length_output: 32
- epoch: 7
- batch: 64
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 1
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squad-ae/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/cline | [
"pytorch",
"roberta",
"transformers"
]
| null | {
"architectures": [
"LecbertForPreTraining"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wikitext2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5068
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.571 | 1.0 | 300 | 1.5332 |
| 1.5725 | 2.0 | 600 | 1.5117 |
| 1.4742 | 3.0 | 900 | 1.5078 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
|
AnonymousSub/cline_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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:
- generated_from_keras_callback
model-index:
- name: Stock-Sentiment-Bert
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. -->
# Stock-Sentiment-Bert
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 10000, 'decay_rate': 0.9, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AnonymousSub/cline_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 27 | null | ---
license: cc0-1.0
inference: false
language:
- en
tags:
- stable-diffusion
- text-to-image
---
# Stable Diffusion fine tuned on art by [Jannis Mayr](https://www.artstation.com/joblyn)
### Usage
Use by adding the keyword "jannismayr" to the prompt. The model was trained with different classnames, which can also be added to the prompt. These classnames are the second words of the filenames.
## Samples
For this model I experimented and made several versions. I won't bore you with details but there were variations in learning rates and classifications. Just look at the samples and pick the one that looks like it suits you best.
The full images can be found in the files and versions tab as they are quite large.
<img src="https://huggingface.co/Froddan/jannismayr/resolve/main/xy_grid-0000-1454625692-.jpg"/>
<img src="https://huggingface.co/Froddan/jannismayr/resolve/main/xy_grid-0001-3762916514-.jpg"/>
<img src="https://huggingface.co/Froddan/jannismayr/resolve/main/xy_grid-0002-590770723-.jpg"/>
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
|
AnonymousSub/consert-emanuals-s10-SR | [
"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
}
}
} | 29 | null | Access to model statisticalplumber/sd-magicbowl is restricted and you are not in the authorized list. Visit https://huggingface.co/statisticalplumber/sd-magicbowl to ask for access. |
AnonymousSub/consert-s10-SR | [
"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 | ---
license: openrail
---
sd1.5 textual inversion for making things into toothpick art using the openjourney mdjrny-v4.ckpt as a base
trained on 40 images
trigger word is: skstpk
example prompt: mdjrny-v4 style painting of a skstpk dragon flying above a castle |
AnonymousSub/declutr-biomed-roberta-papers | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 7 | 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('RedPandaAINLP/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
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": {
"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 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- v2.0
- Embedding
---
Textual Inversion Embedding by ConflictX For SD 2.0 trained on 768x768 images from midjourney and other sources.
Install by downloading the step embedding, and put it in the \embeddings folder
Another themed one, this one is more focused on vibrant and sweet environments.
Use keyword: CandyPunk
Images:
|
AnonymousSub/rule_based_bert_quadruplet_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,
"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:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
# NOTE: This model is only pretrained on TeaBReaC, and not on any real QA dataset.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-bart-large"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/rule_based_bert_quadruplet_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 | ---
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(matteopilotto/sd-class-butterflies-32-v2)
image = pipeline().images[0]
image
```
|
AnonymousSub/rule_based_bert_quadruplet_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
}
}
} | 33 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Gorenzelg/bert-finetuned-squad2
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. -->
# Gorenzelg/bert-finetuned-squad2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2633
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, '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 | Epoch |
|:----------:|:-----:|
| 1.2633 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.10.1
- Datasets 2.6.1
- Tokenizers 0.11.0
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"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 | 2022-12-03T22:08:24Z | ---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pong-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"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
}
}
} | 1 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/lucawashenko/1670105990389/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1576009861845831682/-EvmkRdp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Luca</div>
<div style="text-align: center; font-size: 14px;">@lucawashenko</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Luca.
| Data | Luca |
| --- | --- |
| Tweets downloaded | 2314 |
| Retweets | 53 |
| Short tweets | 141 |
| Tweets kept | 2120 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33kqp600/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lucawashenko's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30ctv1h1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30ctv1h1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lucawashenko')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AnonymousSub/rule_based_hier_quadruplet_0.1_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,
"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 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
# NOTE: This model is only pretrained on TeaBReaC, and not on any real QA dataset.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
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 | 2022-12-03T22:29:35Z | ---
language:
- pt
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Portuguese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 pt
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 10.245288411425497
---
# Whisper Small Portuguese 🇧🇷🇵🇹
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [mozilla-foundation/common_voice_11_0 pt](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/pt) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3887
- Wer: 10.2453
## 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: 64
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0492 | 3.04 | 1000 | 0.2459 | 10.3562 |
| 0.0065 | 7.02 | 2000 | 0.3180 | 10.4521 |
| 0.002 | 11.0 | 3000 | 0.3571 | 10.2924 |
| 0.0009 | 14.04 | 4000 | 0.3816 | 10.2268 |
| 0.0008 | 18.02 | 5000 | 0.3887 | 10.2453 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- 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,
"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 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-t5-3b-iirc-retrieved"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
# NOTE: This model is only pretrained on TeaBReaC, and not on any real QA dataset.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-preasm-large"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"Who scored the first touchdown of the game?\n" +
"... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 6 | 2022-12-03T23:07:27Z | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-preasm-large-drop"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"Who scored the first touchdown of the game?\n" +
"... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 27 | null | ---
tags:
- question-answering, multi-step-reasoning, multi-hop-reasoning
thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
license: cc-by-4.0
---
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-poet-large-numglue"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` |
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 10 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Stable Diffusion on Satellite Images
This model is a diffusion model for unconditional image generation of Sentinel-2 Images based on the EuroSAT Dataset.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('nkasmanoff/sd-eurosat')
image = pipeline().images[0]
image
```
|
AnonymousSub/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
},
"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
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Matthewww/mt5_NytNews
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. -->
# Matthewww/mt5_NytNews
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: nan
- Validation Loss: nan
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 6000, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| nan | nan | 0 |
| nan | nan | 1 |
| nan | nan | 2 |
| nan | nan | 3 |
| nan | nan | 4 |
| nan | nan | 5 |
| nan | nan | 6 |
| nan | nan | 7 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | null | ---
license: openrail
---
# 2d pixel art (beta) embedding for SD 2.0 768px
Hi - I am a big fan of retro/nostalgia things.
This is the reason why I made this embedding.
I have trained it on 70 images, the version I will be targeting in upcoming weeks will be based on 128 or 256 well-selected and filtered images, and processed
through pixelate tool to keep the same pixel size on each of the input data. This should improve the embedding dramatically.
**Images:**
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/lVqWhmx.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/eMZvUTY.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/fyGB10h.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/SJGs2nD.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/R7t1zrG.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/rLyeX7J.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/9eOUMiD.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/bfknB8t.png">
</div>
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/3bXwXMh.png">
</div>
### Installation:
Just drop the embedding files (.pt extension) to your SD embeddings folder (`your-folder/embeddings`).
Restart the app if running. Use the keyword as a filename ("pixelart" for example). You can rename them as you wish.
Before we start: cool tool to enhance your results even further: [link](https://giventofly.github.io/pixelit/#tryit)
**Version of Stable Diffusion:** `2.0 - 768`
**Supported diffusers:**
- Euler a (Preffered)
- DDIM
- other (partially)
### Embeddings
**pixelart:**
The most generic one. Usually gives decent pixels, reads quite well prompts, is not to "old-school".
Used for "pixelating process" in img2img.
**pixelart-soft:**
The softer version of an embedding. One of the most generic ones.Usually good for characters.
**pixelart-hard:**
More pixelated version of embedding. Vintage/old-school. Depends on the topic - can be colorful or very vintage/dull.
**pixelart-1 & pixelart-2:**
less generic ones. These sometimes give even better results than original (depends on topic, tags and diffuser)
**pixelizer:**
Fun but chaotic one. Good for some experiments but usually gives colorful 8-bit like pixelated platformers/game screens stuff.
I have left that one for experiments or as a factor for combination with other ones.
### Usage
I highly recommend use these embeddings with `Euler a` diffuser. It will give usually best results. In some cases it would be
good to use negative prompts. Sometimes for testing if you caught good composition/colors - you might add or remove them
to impact the image.
**negative prompt**:
Recommend this negative to test stuff when you get bad results:
"3d, 3d render, disfigured, kitsch, ugly, oversaturated, grain, low-res, Deformed, blurry, bad anatomy,
disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry,
floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, ugly, disgusting,
poorly drawn, childish, mutilated, mangled, old, surreal"
If you have too much pixelated/chaotic output - add (all or some):
"grid, Tetris, tetris blocks, mosaic, mosaic, pixelated, pixel art"
### Img2img
The embeddings give you a great opportunity to change some of your works into pixel ones.
The best way to do it is to follow this process:
First get your subject. If this is a simple image as:
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/ks4bpSV.png">
</div>
Here it needed just one step!
What I did here was to use:
**Positive prompt:** "game icon, raven, by pixelart, pixelated" (very important to add pixelated)
**Negative prompt:** none (recommended, but you can of course experiment, especially if subject needs that)
**Sampler:** Euler a (needed for any pixelation)
**steps:** 20,
**CFG:** 7,
**Denoising:** 0.58.
**Resolution:** 768x768.
The result:
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/wErverC.png">
</div>
Of course, the result can be better - you can re-roll to infinity or choose better settings or different embedding than recommended pixelart (in some cases you can try others)
Pixelating photos/more complex images:
This is more tricky - but doable. As a baseline use the above settings,
you can experiment with higher/lower CFG or denoising. To keep likeness I don't recommend you to go over 0.6 denoising.
Replace the first part of the prompt with a simple description, at the end should be part: "by/in style pixelart, pixelated"
Probably this will take up to 2-3 rounds. When I like the output - I set it as a base for next iteration. Then I reduce denoising by 0.4 each
extra round.
Of course this is rough process - might be different based on images.
Here are some examples:
Input:
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/l2dGhgH.png">
</div>
Resuts:
<div style="display: flex; flex-direction: row; flex-wrap: wrap">
<img src="https://i.imgur.com/zsrd92w.png">
</div>
I still investigating how to improve on the process. |
AnonymousSub/rule_based_twostagetriplet_hier_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,
"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
}
}
} | 5 | null | I have recently become interested in creating embedding for SD 2.0. This is the first embedding I share, it is for stylizing a subject in a kind of 3d style with exaggerated proportions. I still need to figure out exactly the best options to get a good result; in the meantime if you are interested you can try the embedding I have gotten so far. Expect updates. Place this file in the embeddings folder, restart AUTOMATIC1111 and use "art by _stlz_" (place one underscore before and one after the word stlz; I'm writing this because writing in that format here on huggingface change the text in italic instead of visualizing the underscores) in your prompt for the embedding to take effect |
AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: KoT5-test
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. -->
# KoT5-test
This model is a fine-tuned version of [hyorea1/KoT5-test](https://huggingface.co/hyorea1/KoT5-test) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1671
- Rouge1: 12.2606
- Rouge2: 2.9413
- Rougel: 12.1602
- Rougelsum: 12.1171
- Gen Len: 34.7162
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 1.39 | 0.26 | 400 | 40.4846 | 1.2447 | 11.4691 | 2.9613 | 11.3903 | 11.2842 |
| 1.6078 | 0.52 | 800 | 37.1794 | 1.2214 | 11.4047 | 2.8134 | 11.2911 | 11.2286 |
| 1.2711 | 0.78 | 1200 | 36.1625 | 1.2092 | 11.3608 | 2.8542 | 11.2502 | 11.1927 |
| 1.1407 | 1.05 | 1600 | 35.8515 | 1.1953 | 11.6278 | 2.6468 | 11.5164 | 11.4848 |
| 1.3556 | 1.31 | 2000 | 35.8926 | 1.1911 | 11.5258 | 3.2315 | 11.4592 | 11.4318 |
| 1.2502 | 1.57 | 2400 | 34.8743 | 1.1782 | 11.6087 | 3.0687 | 11.5359 | 11.4555 |
| 1.1821 | 1.83 | 2800 | 35.35 | 1.1731 | 11.6414 | 3.2523 | 11.5635 | 11.4865 |
| 1.5721 | 2.09 | 3200 | 35.5346 | 1.1740 | 11.9067 | 3.3382 | 11.8748 | 11.8156 |
| 1.014 | 2.35 | 3600 | 1.1666 | 11.6128 | 3.1918 | 11.5348| 11.453 | 34.1853 |
| 1.2737 | 2.61 | 4000 | 1.1711 | 12.2584 | 2.9711 | 12.2113| 12.1541 | 35.3162 |
| 1.1664 | 2.88 | 4400 | 1.1623 | 12.4344 | 3.221 | 12.3251| 12.2923 | 34.5096 |
| 1.0872 | 3.14 | 4800 | 1.1677 | 12.6984 | 3.1725 | 12.5901| 12.5768 | 34.5162 |
| 0.9654 | 3.4 | 5200 | 1.1622 | 12.2024 | 3.3137 | 12.1166| 12.0733 | 33.7537 |
| 1.2357 | 3.66 | 5600 | 1.1614 | 12.0954 | 3.0476 | 12.0709| 12.0331 | 34.5257 |
| 1.0516 | 3.92 | 6000 | 1.1610 | 12.2234 | 3.2148 | 12.1003| 12.0567 | 34.5478 |
| 0.9412 | 4.18 | 6400 | 1.1614 | 12.1884 | 3.1935 | 12.1168| 12.1024 | 34.4493 |
| 1.2583 | 4.44 | 6800 | 1.1609 | 12.5444 | 3.2265 | 12.5044| 12.4172 | 34.8132 |
| 1.122 | 4.71 | 7200 | 1.1639 | 12.2393 | 3.2752 | 12.1647| 12.1575 | 34.2728 |
| 1.4178 | 4.97 | 7600 | 1.1629 | 12.4617 | 3.2909 | 12.3475| 12.3123 | 34.6971 |
| 1.1506 | 5.23 | 8000 | 1.1671 | 12.2606 | 2.9413 | 12.1602| 12.1171 | 34.7162 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Anupam/QuestionClassifier | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8431372549019608
- name: F1
type: f1
value: 0.891156462585034
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5509
- Accuracy: 0.8431
- F1: 0.8912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.4740 | 0.7819 | 0.8499 |
| No log | 2.0 | 460 | 0.4150 | 0.8309 | 0.8821 |
| 0.4787 | 3.0 | 690 | 0.4475 | 0.8186 | 0.8706 |
| 0.4787 | 4.0 | 920 | 0.5340 | 0.8358 | 0.8885 |
| 0.2314 | 5.0 | 1150 | 0.5509 | 0.8431 | 0.8912 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ArBert/albert-base-v2-finetuned-ner-gmm-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"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
}
}
} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### dua_lipa Dreambooth model trained by hargup with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
The keyword is `pilauua`, use this instead of "Dua Lipa", needed to a create a new word ensure that Stable Diffusion doesn't learn on a word it already knows.
Sample pictures of this concept:
Prompt: 100x100 cm Your representation of pilauua, complimentary colors, sharp focus, studio shot, intricate detail, highly detailed, sharp focus, studio photo, by WLO, eugene de blaas and ross tran
Prompt: 3 d render of a cute thin young pilauua, red blush, wearing casual clothes, small smile, relaxing on a couch, cuddling up under a blanket, cozy living room, medium shot, 8 k, octane render, trending on artstation, art by artgerm, unreal engine 5, hyperrealism, hyperdetailed, ultra realistic
|
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"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
}
}
} | 8 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- dippatel11/autotrain-data-dippatel_summarizer
co2_eq_emissions:
emissions: 68.41274041098731
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2331873599
- CO2 Emissions (in grams): 68.4127
## Validation Metrics
- Loss: 1.513
- Rouge1: 49.434
- Rouge2: 24.817
- RougeL: 41.176
- RougeLsum: 44.737
- Gen Len: 18.258
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/dippatel11/autotrain-dippatel_summarizer-2331873599
``` |
ArJakusz/DialoGPT-small-stark | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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 | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/tm2
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-tm2-context3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/tm2
name: Taskmaster-2
split: test
revision: cdc314b156e7f7ffa81a1e7398f1f8a2e86c0095
metrics:
- type: Dialog acts Accuracy
value: 82.4
name: Accuracy
- type: Dialog acts F1
value: 74.3
name: F1
widget:
- text: "user: Hi, I'm looking for a flight. I need to visit a friend.\nsystem: Hello, how can I help you? Sure, I can help you with that. On what dates?\nuser: I'm looking to travel from March 20th to 22nd."
- text: "system: Anything else?\nuser: That should be everything.\nsystem: I found a flight for $424 on United Airlines.\nuser: Okay, is that for New York?"
inference:
parameters:
max_length: 100
---
# t5-small-nlu-tm2-context3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-2](https://huggingface.co/datasets/ConvLab/tm2) with context window size == 3.
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArJakusz/DialoGPT-small-starky | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/tm3
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-tm3-context3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/tm3
name: Taskmaster-3
split: test
revision: 910584e5451e2e439bb2a07b8544ecb42ff8835b
metrics:
- type: Dialog acts Accuracy
value: 89.0
name: Accuracy
- type: Dialog acts F1
value: 85.1
name: F1
widget:
- text: "system: OK. And where will you be seeing the movie?\nuser: In Creek's End, Oregon\nsystem: Creek’s End, Oregon. Got it. Is there a particular movie you have in mind?\nuser: Mulan, please. We are taking the kids"
- text: "system: No problem. It looks like tonight’s remaining showtimes for Mulan at AMC Mercado 24 are 5:00pm, 7:10pm, and 9:45pm. Which is best for you?\nuser: I would like the earliest time, 5:00pm\nsystem: Great. And how many tickets?\nuser: three please"
inference:
parameters:
max_length: 100
---
# t5-small-nlu-tm3-context3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3) with context window size == 3.
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArashEsk95/bert-base-uncased-finetuned-cola | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-Test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.902
- name: F1
type: f1
value: 0.9037328094302554
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-Test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2833
- Accuracy: 0.902
- F1: 0.9037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ArashEsk95/bert-base-uncased-finetuned-sst2 | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: gifted_shirley
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. -->
# gifted_shirley
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 1562
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1661599744},
'generation': {'every_n_steps': 16,
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'every_n_steps': 16,
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0',
'value_head_config': {'is_detached': False}},
'path_or_name': 'tomekkorbak/goofy_pasteur'},
'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'gifted_shirley',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 1673,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1661599744,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1rminqjf |
AriakimTaiyo/DialoGPT-medium-Kumiko | [
"conversational"
]
| conversational | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- gl
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small gl - Galician
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. -->
# Whisper Small gl - Galician
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
asaakyan/mbart-poetic-all | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 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('magicknight/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
ArtemisZealot/DialoGTP-small-Qkarin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 313 with parameters:
```
{'batch_size': 32, '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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 32,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Ashl3y/model_name | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
---
# Introduction
This repo contains ConvEmformer transducer models that have been
converted to ncnn format.
You can use models from this repo with <https://github.com/k2-fsa/sherpa-ncnn>
for speech recognition. It runs on x86 machines as well as on embedded devices.
If you are interested in model training and conversion, please have a look at
<https://github.com/k2-fsa/icefall/pull/717>
The original torchscript is from
<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>
|
AshtonBenson/DialoGPT-small-quentin-coldwater | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Vietnamese
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 vi
type: mozilla-foundation/common_voice_11_0
config: vi
split: test
args: vi
metrics:
- type: wer
value: 15.492494795661225
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: vi_vn
split: test
metrics:
- type: wer
value: 19.55
name: WER
---
<!-- 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 Medium Vietnamese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7136
- Wer: 15.4925
## 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: 64
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0001 | 124.0 | 1000 | 0.7136 | 15.4925 |
| 0.0001 | 249.0 | 2000 | 0.8532 | 17.0045 |
| 0.0 | 374.0 | 3000 | 0.9251 | 19.0972 |
| 0.0 | 499.0 | 4000 | 0.9787 | 21.5953 |
| 0.0 | 624.0 | 5000 | 0.9921 | 21.4638 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Augustvember/WokkaBot | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small - Swedish
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. -->
# Whisper Small - Swedish
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 & NST dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3551
- Wer: 19.2143
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2128 | 0.85 | 1000 | 0.2955 | 22.1613 |
| 0.0871 | 1.71 | 2000 | 0.2790 | 20.8034 |
| 0.0373 | 2.56 | 3000 | 0.2884 | 19.9269 |
| 0.0163 | 3.41 | 4000 | 0.3082 | 19.5477 |
| 0.0046 | 4.27 | 5000 | 0.3183 | 19.5881 |
| 0.0023 | 5.12 | 6000 | 0.3397 | 19.3757 |
| 0.0023 | 5.97 | 7000 | 0.3468 | 19.3219 |
| 0.0013 | 6.83 | 8000 | 0.3551 | 19.2143 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Augustvember/WokkaBot2 | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-toi
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. -->
# whisper-small-toi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1668
- Wer: 63.5938
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.568 | 1.47 | 500 | 2.1883 | 72.0402 |
| 0.2614 | 2.95 | 1000 | 2.1071 | 67.1034 |
| 0.0811 | 4.42 | 1500 | 2.3456 | 67.5012 |
| 0.0383 | 5.9 | 2000 | 2.4961 | 67.9691 |
| 0.021 | 7.37 | 2500 | 2.6259 | 68.8348 |
| 0.0077 | 8.85 | 3000 | 2.6423 | 66.6823 |
| 0.0046 | 10.32 | 3500 | 2.8497 | 65.9336 |
| 0.0005 | 11.8 | 4000 | 2.8305 | 64.6467 |
| 0.0014 | 13.27 | 4500 | 2.9174 | 66.0739 |
| 0.0003 | 14.75 | 5000 | 2.9358 | 63.2663 |
| 0.0002 | 16.22 | 5500 | 2.9820 | 63.8278 |
| 0.0002 | 17.7 | 6000 | 3.0369 | 64.7403 |
| 0.0001 | 19.17 | 6500 | 3.0641 | 63.3832 |
| 0.0005 | 20.65 | 7000 | 3.0512 | 63.1493 |
| 0.0001 | 22.12 | 7500 | 3.0924 | 63.5002 |
| 0.0001 | 23.6 | 8000 | 3.1215 | 65.0679 |
| 0.0001 | 25.07 | 8500 | 3.1336 | 64.6233 |
| 0.0001 | 26.55 | 9000 | 3.1513 | 63.7108 |
| 0.0001 | 28.02 | 9500 | 3.1620 | 63.5938 |
| 0.0001 | 29.5 | 10000 | 3.1668 | 63.5938 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Augustvember/WokkaBot6 | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
Augustvember/WokkaBot7 | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-04T15:21:19Z | ---
language: el
tags:
- whisper-small
- mozilla-foundation/common_voice_11_0
- greek
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics: wer
license: creativeml-openrail-m
---
# Summary
This is an early attempt during the December 2022 [Whisper Event](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event)
to finetune `whisper-small` for the Greek language (el). The notebook with all parameters is provided. |
Augustvember/wokka2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 12 | null | ---
license: bigscience-openrail-m
---
This is an embedding based off midjourney images of various characters infected with an alien Symbiote (venom from Marvel)
Trained on v2.0 768-v-ema
Some additional weighting is usually required to get the desired results, e.g. (mjSymbiote:1.2)
Sample Images by this Embed:
 |
Augustvember/wokka5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 11 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
---
I forgot to record the exact weights to the recipe, but it goes mostly like this:
[[EimisSemiRealistic_1-0v + trinart2_step115000] + [[Float's Mix + Fruity Mix] + CandyMissionBerryF222-hassan]] + [samdoesartsultmerge + [samdoesarts v2 + [CopeSeetheMald-berry200_20400 + jhSSamdoesarts_v5]]] |
Augustvember/wokkabottest2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 13 | null | ---
tags:
- text-generation
model-index:
- name: promptgen
results: []
---
# aksty/promptgen: Prompt generation for Text-to-Image Models
This is a text generation model trained on data specifically designed to generate prompts for text-to-image models. It is based on the [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) pre-trained model, which has been fine-tuned using the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
### Notebook with promptgen + Stable Diffusion v2
[](https://colab.research.google.com/github/aksty/promptgen/blob/main/notebook/StableDiffusion_with_PromptGen.ipynb)
## Usage
To use this model, you will need to have `PyTorch` and the `transformers` library installed. You can then use the following code to generate text using the model:
```python
import torch
from transformers import GPT2Tokenizer, GPTNeoForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained("aksty/promptgen")
model = GPTNeoForCausalLM.from_pretrained("aksty/promptgen")
def generate_text(prompt):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids, do_sample=True, max_length=100)
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
```
Output :
```python
generate_text("A painting of an ancient city ")
```
```
['A painting of an ancient city on the top of a cliff, a small sign charging through the sky, cinematic view, epic sky, detailed, concept art, low angle, high detail, warm lighting, volumetric, godrays, vivid, beautiful, trending on artstation, by jordan grimmer, huge scene, grass, art greg rutkowski']
```
## Disclaimer
It is important to note that the results generated by promptgen are not guaranteed to be accurate, complete, or suitable for any particular purpose. The model is intended for research and educational purposes only and should not be relied upon for any other purposes. The generated text may contain errors, omissions, or inappropriate language. The user of the model is solely responsible for any actions or decisions made based on the generated text.
|
Augustvember/your-model-name | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Czech CV11
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
split: test
config: cs
metrics:
- name: Wer
type: wer
value: 18.55567623290319
---
<!-- 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 Czech CV11
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3587
- Wer: 18.5557
## 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: 64
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0392 | 4.01 | 1000 | 0.2857 | 19.6675 |
| 0.0028 | 8.02 | 2000 | 0.3204 | 18.3495 |
| 0.0011 | 12.03 | 3000 | 0.3402 | 18.5336 |
| 0.0008 | 16.04 | 4000 | 0.3537 | 18.4563 |
| 0.0007 | 21.01 | 5000 | 0.3587 | 18.5557 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Aurora/asdawd | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my_Med
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. -->
# my_Med
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5902
- Validation Loss: 1.3655
- Epoch: 9
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8689 | 2.0671 | 0 |
| 2.2250 | 1.8456 | 1 |
| 2.0132 | 1.7104 | 2 |
| 1.9079 | 1.6828 | 3 |
| 1.8237 | 1.5935 | 4 |
| 1.7651 | 1.5240 | 5 |
| 1.7246 | 1.4930 | 6 |
| 1.6565 | 1.4191 | 7 |
| 1.6166 | 1.3944 | 8 |
| 1.5902 | 1.3655 | 9 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Aviora/news2vec | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- safetensors
inference: true
---
Description
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run gigafractal2-diffusion:
[](https://huggingface.co/spaces/akhaliq/gigafractal2-diffusion)
Gigafractal2 Diffusion is a latent text-to-image diffusion model based on the original StabilityAI Stable Diffusion v2.0 and then fine-tuned on 40 images origanally made with another diffusion model named 'Disco Diffusion' using Dreambooth. This model has been created to explore the possibilities and limitations of Dreambooth training with training steps increased much more than usual and to overcome biases in the model created by the text incoder's token associations. The purpose of this model is to provide the biomorphic fractalism effect present in Disco Diffusion, but without the bias to 'Disco parties' and especially 'discoballs' for which [the model by snek](was known for).
To use this style in your generations, add `gigafractal artstyle` to the prompts.
Dreambooth hyperparameters
```
export MODEL_NAME="stabilityai/stable-diffusion-2"
export INSTANCE_DIR="/home/{USERNAME}/kml/datasets/styles/dscdif"
export CLASS_DIR="/home/{USERNAME}/kml/datasets/styles/dscdif2"
export OUTPUT_DIR="/home/{USERNAME}/kml/models1"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="gigafractal artstyle" \
--class_prompt="biomorphic" \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=2040 \
--mixed_precision 'no' \
--train_text_encoder
```
The regularization dataset of 200 AI-generated images had been produced in AUTOMATIC1111's webui with the following prompt which may have had a positive effect on the resulting quality.
```
a computer generated image of a spiral like object, digital art, polycount, generative art, (fractalism:0.7), lovecraftian, intricate, detailed matte painting, high detail, ornate, cgsociety, psychedelic art, gothic art, artstation hq, colorful, complex, biopunk, 8k, maxmialist Negative prompt: bad quality, text, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, flat, out of focus Steps: 20, Sampler: Euler a, CFG scale: 12.5, Seed: 2042420948, Size: 768x768, Model hash: a9263745
```
Model Description
The model originally used for fine-tuning is Stable Diffusion V2-0, see their infopage https://huggingface.co/stabilityai/stable-diffusion-2.
The current model has been fine-tuned with a learning rate of 1.0e-6 for 2040 steps using Dreambooth on Disco Diffusion produced images.
License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license https://huggingface.co/stabilityai/stable-diffusion-2
Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
Acknowledgements
Inspired by snek's work on https://huggingface.co/SDAddictsAnon/Snek/blob/main/arrow_disco_artstyle.ckpt.
This project would not have been possible without the incredible work by the CompVis Researchers, Disco Diffusion, Deforum devs and all the artists who made the content for training even if they were an AI.
The dataset for training currently resides here https://drive.google.com/drive/folders/1v-uW2ESlQRFe17tnWZ7_CtjadD9swfIG?usp=share_link. The author is grateful to snek for the provided dataset.
You can see some examples of Gigafractal2 Diffusion produced images at https://drive.google.com/drive/folders/1z6iXjd4SveZ5s3vbjc3mI_bPOASVVTst?usp=share_link. |
Axcel/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: whisper-ft-libri-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: librispeech_asr
type: librispeech_asr
config: clean
split: test
args: clean
metrics:
- name: Wer
type: wer
value: 31.616341030195382
---
<!-- 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-ft-libri-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8069
- Wer: 31.6163
## 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: 7.740176574997311e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.1717 | 0.38 | 5 | 2.1709 | 98.0462 |
| 1.2371 | 0.77 | 10 | 1.2719 | 79.9290 |
| 0.7577 | 1.15 | 15 | 1.0510 | 35.3464 |
| 0.5325 | 1.54 | 20 | 0.9475 | 32.6821 |
| 0.5545 | 1.92 | 25 | 0.8607 | 30.3730 |
| 0.2957 | 2.31 | 30 | 0.8051 | 33.3925 |
| 0.1846 | 2.69 | 35 | 0.7487 | 30.1954 |
| 0.0748 | 3.08 | 40 | 0.6882 | 32.1492 |
| 0.0709 | 3.46 | 45 | 0.6692 | 31.2611 |
| 0.0908 | 3.85 | 50 | 0.6465 | 29.4849 |
| 0.0764 | 4.23 | 55 | 0.6578 | 28.9520 |
| 0.0259 | 4.62 | 60 | 0.6637 | 30.0178 |
| 0.0178 | 5.0 | 65 | 0.6955 | 30.3730 |
| 0.0131 | 5.38 | 70 | 0.6869 | 33.2149 |
| 0.0162 | 5.77 | 75 | 0.7000 | 32.3268 |
| 0.0081 | 6.15 | 80 | 0.6814 | 32.3268 |
| 0.0075 | 6.54 | 85 | 0.6897 | 31.0835 |
| 0.0069 | 6.92 | 90 | 0.7151 | 32.6821 |
| 0.0062 | 7.31 | 95 | 0.7181 | 30.3730 |
| 0.0056 | 7.69 | 100 | 0.7173 | 30.0178 |
| 0.0052 | 8.08 | 105 | 0.7411 | 31.9716 |
| 0.0073 | 8.46 | 110 | 0.7526 | 32.5044 |
| 0.0061 | 8.85 | 115 | 0.7467 | 32.8597 |
| 0.0034 | 9.23 | 120 | 0.7314 | 31.7940 |
| 0.0122 | 9.62 | 125 | 0.7276 | 31.7940 |
| 0.0429 | 10.0 | 130 | 0.7417 | 32.5044 |
| 0.0032 | 10.38 | 135 | 0.7555 | 31.9716 |
| 0.0141 | 10.77 | 140 | 0.7636 | 31.2611 |
| 0.0038 | 11.15 | 145 | 0.7607 | 31.9716 |
| 0.0038 | 11.54 | 150 | 0.7716 | 33.0373 |
| 0.0035 | 11.92 | 155 | 0.7985 | 34.2806 |
| 0.0038 | 12.31 | 160 | 0.7797 | 32.1492 |
| 0.0036 | 12.69 | 165 | 0.7767 | 31.4387 |
| 0.0022 | 13.08 | 170 | 0.7830 | 31.7940 |
| 0.0033 | 13.46 | 175 | 0.7992 | 30.7282 |
| 0.0019 | 13.85 | 180 | 0.7541 | 30.0178 |
| 0.0016 | 14.23 | 185 | 0.7587 | 30.0178 |
| 0.0027 | 14.62 | 190 | 0.7766 | 30.3730 |
| 0.0016 | 15.0 | 195 | 0.8056 | 32.8597 |
| 0.0015 | 15.38 | 200 | 0.8096 | 32.5044 |
| 0.0012 | 15.77 | 205 | 0.7931 | 32.6821 |
| 0.001 | 16.15 | 210 | 0.7829 | 31.6163 |
| 0.0045 | 16.54 | 215 | 0.7774 | 30.9059 |
| 0.0009 | 16.92 | 220 | 0.7750 | 30.1954 |
| 0.0009 | 17.31 | 225 | 0.7780 | 28.9520 |
| 0.0008 | 17.69 | 230 | 0.7803 | 29.1297 |
| 0.0007 | 18.08 | 235 | 0.7807 | 29.6625 |
| 0.0025 | 18.46 | 240 | 0.7813 | 30.1954 |
| 0.0007 | 18.85 | 245 | 0.7840 | 30.0178 |
| 0.0006 | 19.23 | 250 | 0.7860 | 30.0178 |
| 0.0007 | 19.62 | 255 | 0.7839 | 30.1954 |
| 0.0005 | 20.0 | 260 | 0.7834 | 30.1954 |
| 0.0006 | 20.38 | 265 | 0.7844 | 30.3730 |
| 0.0102 | 20.77 | 270 | 0.7859 | 30.7282 |
| 0.0006 | 21.15 | 275 | 0.7901 | 30.7282 |
| 0.0006 | 21.54 | 280 | 0.7950 | 30.7282 |
| 0.0006 | 21.92 | 285 | 0.7975 | 31.0835 |
| 0.0006 | 22.31 | 290 | 0.7984 | 30.7282 |
| 0.0006 | 22.69 | 295 | 0.7954 | 30.3730 |
| 0.0005 | 23.08 | 300 | 0.7935 | 31.0835 |
| 0.0005 | 23.46 | 305 | 0.7928 | 31.0835 |
| 0.0005 | 23.85 | 310 | 0.7933 | 31.2611 |
| 0.0038 | 24.23 | 315 | 0.7950 | 30.9059 |
| 0.0005 | 24.62 | 320 | 0.7976 | 31.6163 |
| 0.0004 | 25.0 | 325 | 0.7995 | 31.7940 |
| 0.0004 | 25.38 | 330 | 0.8006 | 31.4387 |
| 0.0004 | 25.77 | 335 | 0.8005 | 31.6163 |
| 0.0005 | 26.15 | 340 | 0.8011 | 31.4387 |
| 0.0004 | 26.54 | 345 | 0.8020 | 31.6163 |
| 0.0004 | 26.92 | 350 | 0.8024 | 31.4387 |
| 0.0017 | 27.31 | 355 | 0.8029 | 31.4387 |
| 0.0004 | 27.69 | 360 | 0.8035 | 31.4387 |
| 0.0004 | 28.08 | 365 | 0.8045 | 31.4387 |
| 0.0004 | 28.46 | 370 | 0.8049 | 31.4387 |
| 0.0004 | 28.85 | 375 | 0.8056 | 31.4387 |
| 0.0011 | 29.23 | 380 | 0.8060 | 31.4387 |
| 0.0004 | 29.62 | 385 | 0.8065 | 31.4387 |
| 0.0004 | 30.0 | 390 | 0.8065 | 31.4387 |
| 0.0004 | 30.38 | 395 | 0.8068 | 31.4387 |
| 0.0004 | 30.77 | 400 | 0.8069 | 31.6163 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Axon/resnet18-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 0.11 +/- 80.08
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
...
```
|
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-mini-mlm-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-mini-mlm-finetuned-imdb
This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6935
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.2058 | 0.64 | 500 | 2.9411 |
| 3.1048 | 1.28 | 1000 | 2.9042 |
| 3.0631 | 1.92 | 1500 | 2.8780 |
| 3.0197 | 2.56 | 2000 | 2.8667 |
| 3.0071 | 3.2 | 2500 | 2.8503 |
| 2.9886 | 3.84 | 3000 | 2.8319 |
| 2.9577 | 4.48 | 3500 | 2.8127 |
| 2.9498 | 5.12 | 4000 | 2.8080 |
| 2.9301 | 5.75 | 4500 | 2.7894 |
| 2.9229 | 6.39 | 5000 | 2.7912 |
| 2.9027 | 7.03 | 5500 | 2.7874 |
| 2.8961 | 7.67 | 6000 | 2.7785 |
| 2.8869 | 8.31 | 6500 | 2.7619 |
| 2.8793 | 8.95 | 7000 | 2.7607 |
| 2.8729 | 9.59 | 7500 | 2.7581 |
| 2.8523 | 10.23 | 8000 | 2.7593 |
| 2.8525 | 10.87 | 8500 | 2.7433 |
| 2.8403 | 11.51 | 9000 | 2.7505 |
| 2.8318 | 12.15 | 9500 | 2.7444 |
| 2.8314 | 12.79 | 10000 | 2.7352 |
| 2.8136 | 13.43 | 10500 | 2.7334 |
| 2.8161 | 14.07 | 11000 | 2.7280 |
| 2.7955 | 14.71 | 11500 | 2.7342 |
| 2.7951 | 15.35 | 12000 | 2.7237 |
| 2.7878 | 15.98 | 12500 | 2.7171 |
| 2.7816 | 16.62 | 13000 | 2.7160 |
| 2.7805 | 17.26 | 13500 | 2.7120 |
| 2.7776 | 17.9 | 14000 | 2.7078 |
| 2.7661 | 18.54 | 14500 | 2.7086 |
| 2.7678 | 19.18 | 15000 | 2.7017 |
| 2.7613 | 19.82 | 15500 | 2.7015 |
| 2.7516 | 20.46 | 16000 | 2.6958 |
| 2.7529 | 21.1 | 16500 | 2.6909 |
| 2.7422 | 21.74 | 17000 | 2.6966 |
| 2.738 | 22.38 | 17500 | 2.7034 |
| 2.7303 | 23.02 | 18000 | 2.6935 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Aybars/XLM_Turkish | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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 | null | ---
language:
- es
tags:
- refugiados
license: "apache-2.0"
pipeline-tag: question-answering
---
# Model Card for chatbot-para-refugiados
<!-- Provide a quick summary of what the model is/does. [Optional] -->
Model for Saturdays.IA
# Table of Contents
- [Model Card for chatbot-para-refugiados](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use [Optional]](#downstream-use-optional)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
Model for Saturdays.IA
- **Developed by:** More information needed
- **Shared by [Optional]:** More information needed
- **Model type:** Language model
- **Language(s) (NLP):** es
- **License:** apache-2.0
- **Parent Model:** More information needed
- **Resources for more information:** More information needed
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
More information on training data needed
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
More information needed
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
More information needed
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
More information needed
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
More information needed
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
More information needed
**APA:**
More information needed
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
More information needed
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
More information needed
</details>
|
Ayham/albert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
} | 9 | 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: 0.9245
- name: F1
type: f1
value: 0.9244671567403487
---
<!-- 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: 0.2188
- Accuracy: 0.9245
- F1: 0.9245
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8065 | 1.0 | 250 | 0.3138 | 0.905 | 0.9022 |
| 0.2487 | 2.0 | 500 | 0.2188 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0.dev20230312
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ayham/albert_gpt2_Full_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
} | 9 | null | ### t5-base finetuned on xsum dataset
#### train args<br>
max_input_length: 512<br>
max_tgt_length: 128<br>
epoch: 3<br>
optimizer: AdamW<br>
lr: 2e-5<br>
weight_decay: 1e-3<br>
fp16: False<br>
prefix: "summarize: "<br>
#### performance<br>
train_loss 0.5976<br>
eval_loss: 0.5340<br>
eval_rouge1: 34.6791<br>
eval_rouge2: 12.8236<br>
eval_rougeL: 28.1201<br>
eval_rougeLsum: 28.1241<br>
#### usage<br>
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM<br>
#### dependency<br>
trained with transformers==4.24<br>
compatible with transformers==3.0.2<br> |
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
} | 7 | 2022-12-04T17:22:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.8786885245901639
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3203
- Accuracy: 0.8767
- F1: 0.8787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
} | 6 | 2022-12-04T18:21:10Z | ---
language:
- en
thumbnail: "https://huggingface.co/wavymulder/wavyfusion/resolve/main/images/page1.jpg"
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
**Wavyfusion**
[*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/wavyfusion/resolve/main/wa-vy-fusion_1.0.ckpt) - This is a dreambooth trained on a very diverse dataset ranging from photographs to paintings. The goal was to make a varied, general purpose model for illustrated styles.
In your prompt, use the activation token: `wa-vy style`
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run wavyfusion:
[](https://huggingface.co/spaces/akhaliq/wavyfusion)
We use wa-vy instead of wavy because 'wavy style' introduced unwanted oceans and wavy hair.
Trained from 1.5 with VAE.
There are a lot of cool styles you can achieve with this model. [Please see this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images.](https://huggingface.co/wavymulder/wavyfusion/resolve/main/prompts_for_examples.md)
[And here is an batch of 49 images (not cherrypicked) in both euler_a and DPM++ 2M Karras](https://imgur.com/a/rBft6mw)
Special thanks to [Nitrosocke](https://huggingface.co/nitrosocke) and [Guizmus](https://huggingface.co/Guizmus)
|
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
} | 10 | 2022-12-04T20:03:38Z | Trained from scratch multilingual language model (XLM-Roberta architecture) from our AACL 2022 paper Cross-lingual Similarity of Multilingual Representations Revisited.
Paper (model and training description): https://aclanthology.org/2022.aacl-main.15/ </br>
GitHub repo: https://github.com/delmaksym/xsim#cross-lingual-similarity-of-multilingual-representations-revisited
|
Ayoola/pytorch_model | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-04T20:08:17Z | Trained from scratch multilingual language model (XLM-Roberta architecture) from our AACL 2022 paper Cross-lingual Similarity of Multilingual Representations Revisited.
Paper (model and training description): https://aclanthology.org/2022.aacl-main.15/ </br>
GitHub repo: https://github.com/delmaksym/xsim#cross-lingual-similarity-of-multilingual-representations-revisited
|
Ayran/DialoGPT-medium-harry-1 | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: biolinkbert-mednli
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. -->
# biolinkbert-mednli
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on [MedNLI](https://physionet.org/content/mednli/1.0.0/).
It achieves the following results on the evaluation set:
```
{
"eval_accuracy": 0.8788530230522156,
"eval_loss": 0.7843484878540039,
"eval_runtime": 39.7009,
"eval_samples": 1395,
"eval_samples_per_second": 35.138,
"eval_steps_per_second": 1.108
}
```
The accuracy for the test set is
```
{
"eval_accuracy": 0.8607594966888428,
"eval_loss": 0.879707932472229,
"eval_runtime": 27.4404,
"eval_samples": 1395,
"eval_samples_per_second": 51.821,
"eval_steps_per_second": 1.64
}
```
The labels are
```
"id2label": {
"0": "entailment",
"1": "neutral",
"2": "contradiction"
},
```
## Training procedure
This model checkpoint is made by [mednli.py](https://huggingface.co/cnut1648/biolinkbert-mednli/blob/main/mednli.py) by the following command:
```shell
root=/path/to/mednli/;
python mednli.py \
--model_name_or_path michiyasunaga/BioLinkBERT-large \
--do_train --train_file ${root}/mli_train_v1.jsonl \
--do_eval --validation_file ${root}/mli_dev_v1.jsonl \
--do_predict --test_file ${root}/mli_test_v1.jsonl \
--max_seq_length 512 --fp16 --per_device_train_batch_size 16 --gradient_accumulation_steps 2 \
--learning_rate 3e-5 --warmup_ratio 0.5 --num_train_epochs 10 \
--output_dir ./biolinkbert_mednli
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.2
- Pytorch 1.13.0+cu117
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
} | 12 | 2022-12-04T20:37:05Z | ---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large Czech CV11
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
split: test
config: cs
metrics:
- name: Wer
type: wer
value: 10.82782615098577
---
<!-- 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 Large Czech CV11
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2528
- Wer: 10.8278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0058 | 4.02 | 1000 | 0.2097 | 11.9563 |
| 0.0012 | 8.04 | 2000 | 0.2210 | 10.9751 |
| 0.001 | 13.01 | 3000 | 0.2405 | 11.3488 |
| 0.0002 | 17.02 | 4000 | 0.2467 | 10.8794 |
| 0.0001 | 21.04 | 5000 | 0.2528 | 10.8278 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Ayu/Shiriro | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-04T20:47:50Z | ---
license: unknown
tags:
- stable-diffusion
- text-to-image
---
# HyperNetworkCollection
個人的に集めてる韓国のHyperNetworkコレクションやで
もれなくコレクションしたい人は一次ソースをあたってな↓
공유된 hypernet, embedding 모음 (샘플 有) - AI그림 학습 채널 - https://arca.live/b/hypernetworks/60940948?category=%EA%B3%B5%EC%9C%A0&p=1
# ダウンロード方法
## まとめてダウンロード
1. Gitをインストール
2. 好きなフォルダ作ってディレクトリ欄に cmd と入力 → Enterでフォルダのディレクトリでコマンドプロンプトを開く
3. 以下のコマンドを順に実行
4. git lfs install
5. git clone https://huggingface.co/WarriorMama777/HyperNetworkCollection_v2
6. 完了
## 個別にダウンロード
1. Files and vaersionsタブに移動
2. HyperNetworkCollection_v2/_Korea_arca.live_HypernetworkCollection/ダウンロードしたいHyperNetwork.pt
3. download
4. 完了 |
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
"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
}
}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hasoc19-bert-base-multilingual-uncased-sentiment-new
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. -->
# hasoc19-bert-base-multilingual-uncased-sentiment-new
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4879
- Accuracy: 0.8433
- Precision: 0.8441
- Recall: 0.8433
- F1: 0.8436
## 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: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4931 | 1.0 | 537 | 0.4011 | 0.8192 | 0.8212 | 0.8192 | 0.8198 |
| 0.3643 | 2.0 | 1074 | 0.4020 | 0.8291 | 0.8298 | 0.8291 | 0.8294 |
| 0.2816 | 3.0 | 1611 | 0.3837 | 0.8339 | 0.8378 | 0.8339 | 0.8347 |
| 0.2378 | 4.0 | 2148 | 0.4235 | 0.8381 | 0.8378 | 0.8381 | 0.8379 |
| 0.1904 | 5.0 | 2685 | 0.4753 | 0.8349 | 0.8350 | 0.8349 | 0.8349 |
| 0.1597 | 6.0 | 3222 | 0.4879 | 0.8433 | 0.8441 | 0.8433 | 0.8436 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BOON/electra-xlnet | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: STA
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. -->
# STA
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
BOON/electra_qa | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-04T22:30:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: traj-classifier-recency
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. -->
# traj-classifier-recency
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 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: 15
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
BSC-LT/RoBERTalex | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"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
}
}
} | 24 | 2022-12-04T22:30:49Z | The model is under construction.
It is mainly built on [FinBERT-ESG](https://huggingface.co/yiyanghkust/finbert-esg)(Huang et al.,2022). That is it succeeded the representation from [FinBERT-ESG](https://huggingface.co/yiyanghkust/finbert-esg)(Huang et al.,2022) and fine-tuned on Reddit posts related to ESG.
You can use this model with the following code:
```
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("admation/ESG-BERT-Reddit")
```
## Reference
Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." Contemporary Accounting Research (2022). |
BatuhanYilmaz/dummy-model | [
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"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
}
}
} | 6 | 2022-12-05T01:46:32Z | ---
language:
- el
license: apache-2.0
tags:
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-el
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 el
type: mozilla-foundation/common_voice_11_0
config: el
split: test
args: el
metrics:
- name: Wer
type: wer
value: 25.696508172362552
---
# Whisper Small - Greek (el)
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 el dataset
for transcription in Greek.
It achieves the following results on the evaluation set:
- train_loss: 0.0615
- Wer: 20.2080
### Training results
Upon completion of training the best model was reloaded and tested with the following results extracted from the stdout log:
```
Loading best model from ./whisper-small-el/checkpoint-5000 (score: 20.208023774145616).
{'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34}
TrainOutput(global_step=5000,
training_loss=0.06146362095708027,
metrics={'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34})
```
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.12.1
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-05T02:00:41Z | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- jdminor/autotrain-data-t5-large-summary
co2_eq_emissions:
emissions: 0.2958140546196442
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2338073717
- CO2 Emissions (in grams): 0.2958
## Validation Metrics
- Loss: 1.536
- Rouge1: 45.911
- Rouge2: 18.396
- RougeL: 36.497
- RougeLsum: 40.822
- Gen Len: 23.070
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/jdminor/autotrain-t5-large-summary-2338073717
``` |
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-05T02:01:41Z | ---
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('jcgarciaca/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"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
}
}
} | 13 | 2022-12-05T02:23:57Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Lichtspiel Diffusion
This is a fine-tuned Stable Diffusion 1.5 model trained on stills from movies by celebrated cinematographers. It gives your images a cinematic look, muted colors, bloom and film grain. Sometimes it works great, sometimes not so well.
Sample pictures of this concept:
Download the *.ckpt file from the "files and versions" tab into the Stable Diffusion models folder of your web-ui of choice. Rename it to model.ckpt and ... that's it.
Use the token Lichtspiel Style to activate the effect. It works particularly well for portraits and if you additionally reference specific films like Blade Runner, True Grit or The Revenant.
The model was trained using dreambooth by TheLastBen based on the SD implementation by XavierXiao.
Trained with 10.500 steps.
Test the model with A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
This model is open access with a CreativeML Open RAIL++-M License further specifying rights and usage -> https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL
|
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"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": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,816 | 2022-12-05T02:28:41Z | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- jdminor/autotrain-data-pegasus-large-summary-2.0
co2_eq_emissions:
emissions: 74.34647142824745
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2338573727
- CO2 Emissions (in grams): 74.3465
## Validation Metrics
- Loss: 1.562
- Rouge1: 45.675
- Rouge2: 19.602
- RougeL: 36.750
- RougeLsum: 40.715
- Gen Len: 17.977
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/jdminor/autotrain-pegasus-large-summary-2.0-2338573727
``` |
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
]
| feature-extraction | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"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
}
}
} | 106 | 2022-12-05T02:45:21Z | This is an Embedding built for Stable Diffusion 2.0.
Trained on 14 screenshots from the Sega Megadrive/Genesis game Sonic The Hedgehog.
Training was done with the Automatic1111 WebUI
Batch Size 7
Gradient Accumulation Steps 2
200 Steps (I will include previous step versions for you to try as well)
Phrase to invoke the Embedding is "MDSth". This was to avoid any interaction with other words and is shortened from MegaDrive Sonic The Hedgehog.
Default Generations with the "MDSth" phrase generate items that look like Sonic/MegaDrive era side scrollers:
<img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937735-342089031-MDSth____.png" alt="Example Image" width="600"/>
Adding in other prompts can get interesting effects (Prompt: An Illustration of a Nissan GT-R by MDSth):
<img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937740-3934836779-An%20Il___.png" alt="An Illustration of a Nissan GT-R by MDSth" width="600"/>
Prompt: A close-up Portrait Illustration of a woman in a red dress by MDSth
<img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937743-3007053864-A%20clo___.png" alt="A close-up Portrait Illustration of a woman in a red dress by MDSth" width="600"/>
Also works well with Img2Img:
<img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937755-1544881965-A%20Cin___.png" alt="A Cinematic Photograph of Dwayne Johnson by MDSth" width="300"/> <img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937756-2903989987-A%20Cin___.png" alt="A Cinematic Photograph of Dwayne Johnson by MDSth" width="300"/>
An example of the different step results (Results are dependent on prompt, your mileage may vary):
<img src="https://huggingface.co/Arron17/Mega-Drive-Pixels/resolve/main/1662823937746-4082311272-A%20Cin___.png" alt="A Cinematic Photograph of a Nissan GT-R by MDSth" width="1000"/>
|
Beatriz/model_name | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-05T02:55:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-coba-coba-coba
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-coba-coba-coba
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5870
- Rouge1: 0.4336
- Rouge2: 0.288
- Rougel: 0.3746
- Rougelsum: 0.4095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-06
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 7.0922 | 1.0 | 7452 | 0.6538 | 0.3557 | 0.239 | 0.3216 | 0.3342 |
| 0.9442 | 2.0 | 14904 | 0.6900 | 0.427 | 0.2868 | 0.371 | 0.4028 |
| 3.0789 | 3.0 | 22356 | 0.6775 | 0.3801 | 0.2581 | 0.34 | 0.3564 |
| 1.0565 | 4.0 | 29808 | 0.5928 | 0.4345 | 0.2885 | 0.376 | 0.4102 |
| 0.7872 | 5.0 | 37260 | 0.5870 | 0.4336 | 0.288 | 0.3746 | 0.4095 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.9.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Belin/T5-Terms-and-Conditions | []
| null | {
"architectures": null,
"model_type": null,
"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
}
}
} | 0 | 2022-12-05T03:16:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: mc-bart-base-mqa-fine-tune
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. -->
# mc-bart-base-mqa-fine-tune
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8651
## 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
- num_epochs: 1.8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6673 | 0.59 | 10000 | 0.8870 |
| 0.6969 | 1.18 | 20000 | 0.8651 |
| 0.6298 | 1.77 | 30000 | 0.8651 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.