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2025-09-06 06:27:01
| downloads
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| likes
int64 0
11.7k
| library_name
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sarnikowski/electra-small-discriminator-da-256-cased
|
sarnikowski
| 2020-12-11T22:01:11Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"da",
"arxiv:2003.10555",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: da
license: cc-by-4.0
---
# Danish ELECTRA small (cased)
An [ELECTRA](https://arxiv.org/abs/2003.10555) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers/tree/main/electra
## Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased")
model = AutoModel.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased")
```
## Questions?
If you have any questions feel free to open an issue on the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com
|
patrickvonplaten/roberta_shared_bbc_xsum
|
patrickvonplaten
| 2020-12-11T21:59:29Z | 11 | 4 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- xsum
tags:
- summarization
---
Shared RoBERTa2RoBERTa Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* model fine-tuned on the *BBC XSum* summarization dataset.
The model achieves a **16.89** ROUGE-2 score on *BBC XSUM*'s test dataset.
For more details on how the model was fine-tuned, please refer to
[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
|
patrickvonplaten/roberta2roberta-cnn_dailymail-fp16
|
patrickvonplaten
| 2020-12-11T21:59:23Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Roberta2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a Roberta2Roberta model fine-tuned on summarization.
Roberta2Roberta is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `roberta-base`
RoBERTa models. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the
two pretrained models can simply be loaded into the framework via:
```python
roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base")
```
The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal
masking for auto-regressiv generation.
Thus, ``roberta2roberta`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model
`roberta2roberta-cnn_dailymail-fp16` is uploaded here.
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable summarization results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 EncoderDecoder Framework.
The model can be used as follows:
```python
from transformers import BertTokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-cnn_dailymail-fp16")
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
article = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David B
oren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 185
6, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confede
rate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking fu
ll membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on t
he fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more invol
ved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members al
legedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a frat
ernity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,
' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloy
d's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing in
cidents."""
input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing racist chants. The fraternity's national chapter has had to close 12 in 18 months over hazing.
# Sigma has had more than 130 chapters in 18 states. University of Oklahoma president says fraternity has been "deteriorated".
```
## Training script:
**IMPORTANT**: In order for this code to work, make sure you checkout to the branch
[more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts
the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840.
The following code shows the complete training script that was used to fine-tune `roberta2roberta-cnn_dailymail-fp16
` for reproducability. The training last ~9h on a standard GPU.
```python
#!/usr/bin/env python3
import nlp
import logging
from transformers import RobertaTokenizer, EncoderDecoderModel, Trainer, TrainingArguments
logging.basicConfig(level=logging.INFO)
model = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base")
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")
# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=0)
# set decoding params
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
encoder_length = 512
decoder_length = 128
batch_size = 16
# map data correctly
def map_to_encoder_decoder_inputs(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at Longformer at 2048
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
# force summarization <= 256
outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
# mask loss for padding
batch["labels"] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
batch["decoder_attention_mask"] = outputs.attention_mask
assert all([len(x) == encoder_length for x in inputs.input_ids])
assert all([len(x) == decoder_length for x in outputs.input_ids])
return batch
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = tokenizer.eos_token_id
label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
# make train dataset ready
train_dataset = train_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "decoder_attention_mask", "decoder_input_ids", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch", columns=["input_ids", "decoder_attention_mask", "attention_mask", "decoder_input_ids", "labels"],
)
# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
output_dir="./",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_from_generate=True,
evaluate_during_training=True,
do_train=True,
do_eval=True,
logging_steps=1000,
save_steps=1000,
eval_steps=1000,
overwrite_output_dir=True,
warmup_steps=2000,
save_total_limit=3,
fp16=True,
)
# instantiate trainer
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
# start training
trainer.train()
```
## Evaluation
The following script evaluates the model on the test set of
CNN/Daily Mail.
```python
#!/usr/bin/env python3
import nlp
from transformers import RobertaTokenizer, EncoderDecoderModel
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-cnn_dailymail-fp16")
model.to("cuda")
test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 128
# map data correctly
def generate_summary(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at BERT max length 512
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
outputs = model.generate(input_ids, attention_mask=attention_mask)
# all special tokens including will be removed
output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch["pred"] = output_str
return batch
results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
# load rouge for validation
rouge = nlp.load_metric("rouge")
pred_str = results["pred"]
label_str = results["highlights"]
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
print(rouge_output)
```
The obtained results should be:
| - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
|----------|:-------------:|:------:|:------:|
| **CNN/Daily Mail** | 15.79 | 19.05 | **16.79** |
|
patrickvonplaten/longformer2roberta-cnn_dailymail-fp16
|
patrickvonplaten
| 2020-12-11T21:59:19Z | 102 | 6 |
transformers
|
[
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Longformer2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a Longformer2Roberta model fine-tuned on summarization.
Longformer2Roberta is a `EncoderDecoderModel`, meaning that both the encoder is a `allenai/longformer-base-4096` model and the decoder is a `roberta-base` model. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the
two pretrained models can simply be loaded into the framework via:
```python
roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("allenai/longformer-base-4096", "roberta-base")
```
The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal
masking for auto-regressiv generation.
Thus, ``longformer2roberta`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model
`longformer2roberta-cnn_dailymail-fp16` is uploaded here.
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable summarization results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 EncoderDecoder Framework.
The model can be used as follows:
```python
from transformers import LongformerTokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/longformer2roberta-cnn_dailymail-fp16")
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
article = """(CNN)James Holmes made his introduction to the world in a Colorado cinema filled with spectators watching a midnight showing of the new Batman movie, "The Dark Knight Rises," in June 2012. The moment became one of the deadliest shootings in U.S. history. Holmes is accused of opening fire on the crowd, killing 12 people and injuring or maiming 70 others in Aurora, a suburb of Denver. Holmes appeared like a comic book character: He resembled the Joker, with red-orange hair, similar to the late actor Heath Ledger\'s portrayal of the villain in an earlier Batman movie, authorities said. But Holmes was hardly a cartoon. Authorities said he wore body armor and carried several guns, including an AR-15 rifle, with lots of ammo. He also wore a gas mask. Holmes says he was insane at the time of the shootings, and that is his legal defense and court plea: not guilty by reason of insanity. Prosecutors aren\'t swayed and will seek the death penalty. Opening statements in his trial are scheduled to begin Monday. Holmes admits to the shootings but says he was suffering "a psychotic episode" at the time, according to court papers filed in July 2013 by the state public defenders, Daniel King and Tamara A. Brady. Evidence "revealed thus far in the case supports the defense\'s position that Mr. Holmes suffers from a severe mental illness and was in the throes of a psychotic episode when he committed the acts that resulted in the tragic loss of life and injuries sustained by moviegoers on July 20, 2012," the public defenders wrote. Holmes no longer looks like a dazed Joker, as he did in his first appearance before a judge in 2012. He appeared dramatically different in January when jury selection began for his trial: 9,000 potential jurors were summoned for duty, described as one of the nation\'s largest jury calls. Holmes now has a cleaner look, with a mustache, button-down shirt and khaki pants. In January, he had a beard and eyeglasses. If this new image sounds like one of an academician, it may be because Holmes, now 27, once was one. Just before the shooting, Holmes was a doctoral student in neuroscience, and he was studying how the brain works, with his schooling funded by a U.S. government grant. Yet for all his learning, Holmes apparently lacked the capacity to command his own mind, according to the case against him. A jury will ultimately decide Holmes\' fate. That panel is made up of 12 jurors and 12 alternates. They are 19 women and five men, and almost all are white and middle-aged. The trial could last until autumn. When jury summonses were issued in January, each potential juror stood a 0.2% chance of being selected, District Attorney George Brauchler told the final jury this month. He described the approaching trial as "four to five months of a horrible roller coaster through the worst haunted house you can imagine." The jury will have to render verdicts on each of the 165 counts against Holmes, including murder and attempted murder charges. Meanwhile, victims and their relatives are challenging all media outlets "to stop the gratuitous use of the name and likeness of mass killers, thereby depriving violent individuals the media celebrity and media spotlight they so crave," the No Notoriety group says. They are joined by victims from eight other mass shootings in recent U.S. history. Raised in central coastal California and in San Diego, James Eagan Holmes is the son of a mathematician father noted for his work at the FICO firm that provides credit scores and a registered nurse mother, according to the U-T San Diego newspaper. Holmes also has a sister, Chris, a musician, who\'s five years younger, the newspaper said. His childhood classmates remember him as a clean-cut, bespectacled boy with an "exemplary" character who "never gave any trouble, and never got in trouble himself," The Salinas Californian reported. His family then moved down the California coast, where Holmes grew up in the San Diego-area neighborhood of Rancho Peñasquitos, which a neighbor described as "kind of like Mayberry," the San Diego newspaper said. Holmes attended Westview High School, which says its school district sits in "a primarily middle- to upper-middle-income residential community." There, Holmes ran cross-country, played soccer and later worked at a biotechnology internship at the Salk Institute and Miramar College, which attracts academically talented students. By then, his peers described him as standoffish and a bit of a wiseacre, the San Diego newspaper said. Holmes attended college fairly close to home, in a neighboring area known as Southern California\'s "inland empire" because it\'s more than an hour\'s drive from the coast, in a warm, low-desert climate. He entered the University of California, Riverside, in 2006 as a scholarship student. In 2008 he was a summer camp counselor for disadvantaged children, age 7 to 14, at Camp Max Straus, run by Jewish Big Brothers Big Sisters of Los Angeles. He graduated from UC Riverside in 2010 with the highest honors and a bachelor\'s degree in neuroscience. "Academically, he was at the top of the top," Chancellor Timothy P. White said. He seemed destined for even higher achievement. By 2011, he had enrolled as a doctoral student in the neuroscience program at the University of Colorado Anschutz Medical Campus in Aurora, the largest academic health center in the Rocky Mountain region. The doctoral in neuroscience program attended by Holmes focuses on how the brain works, with an emphasis on processing of information, behavior, learning and memory. Holmes was one of six pre-thesis Ph.D. students in the program who were awarded a neuroscience training grant from the National Institutes of Health. The grant rewards outstanding neuroscientists who will make major contributions to neurobiology. A syllabus that listed Holmes as a student at the medical school shows he was to have delivered a presentation about microRNA biomarkers. But Holmes struggled, and his own mental health took an ominous turn. In March 2012, he told a classmate he wanted to kill people, and that he would do so "when his life was over," court documents said. Holmes was "denied access to the school after June 12, 2012, after he made threats to a professor," according to court documents. About that time, Holmes was a patient of University of Colorado psychiatrist Lynne Fenton. Fenton was so concerned about Holmes\' behavior that she mentioned it to her colleagues, saying he could be a danger to others, CNN affiliate KMGH-TV reported, citing sources with knowledge of the investigation. Fenton\'s concerns surfaced in early June, sources told the Denver station. Holmes began to fantasize about killing "a lot of people" in early June, nearly six weeks before the shootings, the station reported, citing unidentified sources familiar with the investigation. Holmes\' psychiatrist contacted several members of a "behavioral evaluation and threat assessment" team to say Holmes could be a danger to others, the station reported. At issue was whether to order Holmes held for 72 hours to be evaluated by mental health professionals, the station reported. "Fenton made initial phone calls about engaging the BETA team" in "the first 10 days" of June, but it "never came together" because in the period Fenton was having conversations with team members, Holmes began the process of dropping out of school, a source told KMGH. Defense attorneys have rejected the prosecution\'s assertions that Holmes was barred from campus. Citing statements from the university, Holmes\' attorneys have argued that his access was revoked because that\'s normal procedure when a student drops enrollment. What caused this turn for the worse for Holmes has yet to be clearly detailed. In the months before the shooting, he bought four weapons and more than 6,000 rounds of ammunition, authorities said. Police said he also booby-trapped his third-floor apartment with explosives, but police weren\'t fooled. After Holmes was caught in the cinema parking lot immediately after the shooting, bomb technicians went to the apartment and neutralized the explosives. No one was injured at the apartment building. Nine minutes before Holmes went into the movie theater, he called a University of Colorado switchboard, public defender Brady has said in court. The number he called can be used to get in contact with faculty members during off hours, Brady said. Court documents have also revealed that investigators have obtained text messages that Holmes exchanged with someone before the shooting. That person was not named, and the content of the texts has not been made public. According to The New York Times, Holmes sent a text message to a fellow graduate student, a woman, about two weeks before the shooting. She asked if he had left Aurora yet, reported the newspaper, which didn\'t identify her. No, he had two months left on his lease, Holmes wrote back, according to the Times. He asked if she had heard of "dysphoric mania," a form of bipolar disorder marked by the highs of mania and the dark and sometimes paranoid delusions of major depression. The woman asked if the disorder could be managed with treatment. "It was," Holmes wrote her, according to the Times. But he warned she should stay away from him "because I am bad news," the newspaper reported. It was her last contact with Holmes. After the shooting, Holmes\' family issued a brief statement: "Our hearts go out to those who were involved in this tragedy and to the families and friends of those involved," they said, without giving any information about their son. Since then, prosecutors have refused to offer a plea deal to Holmes. For Holmes, "justice is death," said Brauchler, the district attorney. In December, Holmes\' parents, who will be attending the trial, issued another statement: They asked that their son\'s life be spared and that he be sent to an institution for mentally ill people for the rest of his life, if he\'s found not guilty by reason of insanity. "He is not a monster," Robert and Arlene Holmes wrote, saying the death penalty is "morally wrong, especially when the condemned is mentally ill." "He is a human being gripped by a severe mental illness," the parents said. The matter will be settled by the jury. CNN\'s Ana Cabrera and Sara Weisfeldt contributed to this report from Denver."""
input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# James Holmes, 27, is accused of opening fire on a Colorado theater.
# He was a doctoral student at University of Colorado.
# Holmes says he was suffering "a psychotic episode" at the time of the shooting.
# Prosecutors won't say whether Holmes was barred from campus.
```
Such an article has a length of > 2000 tokens, which means that it cannot be handled correctly by Bert or Roberta encoders.
## Training script:
**IMPORTANT**: In order for this code to work, make sure you checkout to the branch
[more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts
the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840.
The following code shows the complete training script that was used to fine-tune `longformer2roberta-cnn_dailymail-fp16
` for reproducability. The training last ~90h on a standard GPU.
```python
#!/usr/bin/env python3
import nlp
import logging
from transformers import LongformerTokenizer, EncoderDecoderModel, Trainer, TrainingArguments
logging.basicConfig(level=logging.INFO)
model = EncoderDecoderModel.from_encoder_decoder_pretrained("allenai/longformer-base-4096", "roberta-base")
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")
# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=0)
# enable gradient checkpointing for longformer encoder
model.encoder.config.gradient_checkpointing = True
# set decoding params
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
encoder_length = 2048
decoder_length = 128
batch_size = 16
# map data correctly
def map_to_encoder_decoder_inputs(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at Longformer at 2048
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
# force summarization <= 128
outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
# set 128 tokens to global attention
batch["global_attention_mask"] = [[1 if i < 128 else 0 for i in range(sequence_length)] for sequence_length in len(inputs.input_ids) * [encoder_length]]
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
# mask loss for padding
batch["labels"] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
batch["decoder_attention_mask"] = outputs.attention_mask
assert all([len(x) == encoder_length for x in inputs.input_ids])
assert all([len(x) == decoder_length for x in outputs.input_ids])
return batch
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = tokenizer.eos_token_id
label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
# make train dataset ready
train_dataset = train_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch", columns=["input_ids", "global_attention_mask", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
output_dir="./",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_from_generate=True,
evaluate_during_training=True,
do_train=True,
do_eval=True,
logging_steps=1000,
save_steps=1000,
eval_steps=1000,
overwrite_output_dir=True,
warmup_steps=2000,
save_total_limit=3,
fp16=True,
)
# instantiate trainer
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
# start training
trainer.train()
```
## Evaluation
The following script evaluates the model on the test set of
CNN/Daily Mail.
```python
#!/usr/bin/env python3
import nlp
import torch
from transformers import LongformerTokenizer, EncoderDecoderModel
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/longformer2roberta-cnn_dailymail-fp16")
model.to("cuda")
test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 32
encoder_length = 2048
decoder_length = 128
# map data correctly
def generate_summary(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at BERT max length 512
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
global_attention_mask = torch.zeros_like(attention_mask)
global_attention_mask[:, :decoder_length] = 1
outputs = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
# all special tokens including will be removed
output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch["pred"] = output_str
return batch
results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
# load rouge for validation
rouge = nlp.load_metric("rouge")
pred_str = results["pred"]
label_str = results["highlights"]
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
print(rouge_output)
```
The obtained results should be:
| - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
|----------|:-------------:|:------:|:------:|
| **CNN/Daily Mail** | 12.39 | 15.05 | **13.21** |
**Note** This model was trained to show how Longformer can be used as an Encoder model in a EncoderDecoder setup.
Better results are obtained for datasets of much longer inputs.
|
mrm8488/xlm-multi-finetuned-xquadv1
|
mrm8488
| 2020-12-11T21:56:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm",
"question-answering",
"multilingual",
"arxiv:1901.07291",
"arxiv:1910.11856",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: multilingual
thumbnail:
---
# [XLM](https://github.com/facebookresearch/XLM/) (multilingual version) fine-tuned for multilingual Q&A
Released from `Facebook` together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.
## Details of the language model('xlm-mlm-100-1280')
[Language model](https://github.com/facebookresearch/XLM/#ii-cross-lingual-language-model-pretraining-xlm)
| Languages
| --------- |
| 100 |
It includes the following languages:
<details>
en-es-fr-de-zh-ru-pt-it-ar-ja-id-tr-nl-pl-simple-fa-vi-sv-ko-he-ro-no-hi-uk-cs-fi-hu-th-da-ca-el-bg-sr-ms-bn-hr-sl-zh_yue-az-sk-eo-ta-sh-lt-et-ml-la-bs-sq-arz-af-ka-mr-eu-tl-ang-gl-nn-ur-kk-be-hy-te-lv-mk-zh_classical-als-is-wuu-my-sco-mn-ceb-ast-cy-kn-br-an-gu-bar-uz-lb-ne-si-war-jv-ga-zh_min_nan-oc-ku-sw-nds-ckb-ia-yi-fy-scn-gan-tt-am
</details>
## Details of the downstream task (multilingual Q&A) - Dataset
Deepmind [XQuAD](https://github.com/deepmind/xquad)
Languages covered:
- Arabic: `ar`
- German: `de`
- Greek: `el`
- English: `en`
- Spanish: `es`
- Hindi: `hi`
- Russian: `ru`
- Thai: `th`
- Turkish: `tr`
- Vietnamese: `vi`
- Chinese: `zh`
As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this
setting so that models can focus on cross-lingual transfer.
We show the average number of tokens per paragraph, question, and answer for each language in the
table below. The statistics were obtained using [Jieba](https://github.com/fxsjy/jieba) for Chinese
and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
for the other languages.
| | en | es | de | el | ru | tr | ar | vi | th | zh | hi |
| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
| Question | 11.5 | 13.4 | 11.0 | 11.7 | 10.0 | 9.8 | 10.7 | 14.8 | 11.5 | 10.5 | 18.7 |
| Answer | 3.1 | 3.6 | 3.0 | 3.3 | 3.1 | 3.1 | 3.1 | 4.5 | 4.1 | 3.5 | 5.6 |
Citation:
<details>
```bibtex
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
```
</details>
As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
| Dataset | # samples |
| ----------- | --------- |
| XQUAD train | 50 K |
| XQUAD test | 8 K |
## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="mrm8488/xlm-multi-finetuned-xquadv1",
tokenizer="mrm8488/xlm-multi-finetuned-xquadv1"
)
# English
qa_pipeline({
'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
'question': "Who has been working hard for hugginface/transformers lately?"
})
#Output: {'answer': 'Manuel', 'end': 6, 'score': 8.531880747878265e-05, 'start': 0}
# Russian
qa_pipeline({
'context': "Мануэль Ромеро в последнее время почти не работал в репозитории hugginface / transformers",
'question': "Кто в последнее время усердно работал над обнимашками / трансформерами?"
})
#Output: {'answer': 'работал в репозитории hugginface /','end': 76, 'score': 0.00012340750456964894, 'start': 42}
```
Try it on a Colab (*Do not forget to change the model and tokenizer path in the Colab if necessary*):
<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Try_mrm8488_xquad_finetuned_uncased_model.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it
|
mrm8488
| 2020-12-11T21:56:44Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"it",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: it
---
# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓
[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking.
UmBERTo-Wikipedia-Uncased Training is trained on a relative small corpus (~7GB) extracted from Wikipedia-ITA.
## Details of the downstream task (Q&A) - Dataset 📚
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) [Rajpurkar et al. 2016] is a large scale dataset for training of question answering systems on factoid questions. It contains more than 100,000 question-answer pairs about passages from 536 articles chosen from various domains of Wikipedia.
**SQuAD-it** is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path 'Musixmatch/umberto-wikipedia-uncased-v1' \
--do_eval \
--do_train \
--do_lower_case \
--train_file '/content/dataset/SQuAD_it-train.json' \
--predict_file '/content/dataset/SQuAD_it-test.json' \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/drive/My\ Drive/umberto-uncased-finetuned-squadv1-it \
--overwrite_output_dir \
--save_steps 1000
```
With 10 epochs the model overfits the train dataset so I evaluated the different checkpoints created during training (every 1000 steps) and chose the best (In this case the one created at 17000 steps).
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **60.50** |
| **F1** | **72.41** |
```json
{
'exact': 60.50729399395453,
'f1': 72.4141113348361,
'total': 7609,
'HasAns_exact': 60.50729399395453,
'HasAns_f1': 72.4141113348361,
'HasAns_total': 7609,
'best_exact': 60.50729399395453,
'best_exact_thresh': 0.0,
'best_f1': 72.4141113348361,
'best_f1_thresh': 0.0
}
```
## Comparison ⚖️
| Model | EM | F1 score |
| -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- |
| [DrQA-it trained on SQuAD-it ](https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it) | 56.1 | 65.9 |
| This one |60.50 |72.41 |
| [bert-italian-finedtuned-squadv1-it-alfa](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) |**62.51** |**74.16** | | **62.51** | **74.16** |
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it')
QnA_pipeline({
'context': 'Marco Aurelio era un imperatore romano che praticava lo stoicismo come filosofia di vita .',
'question': 'Quale filosofia seguì Marco Aurelio ?'
})
# Output:
{'answer': 'stoicismo', 'end': 65, 'score': 0.9477770241566028, 'start': 56}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/t5-small-finetuned-squadv1
|
mrm8488
| 2020-12-11T21:56:34Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- squad
---
# T5-small fine-tuned on SQuAD
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [(small)](https://huggingface.co/t5-small) fine-tuned on [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| squad | train | 87599 |
| squad | valid | 10570 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('squad, split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('squad', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28)
## Results 📝
| Metric | # Value |
| ------ | --------- |
| **EM** | **76.95** |
| **F1** | **85.71** |
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-squadv1")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-squadv1")
def get_answer(question, context):
input_text = "question: %s context: %s </s>" % (question, context)
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
context = "Manuel have created RuPERTa-base (a Spanish RoBERTa) with the support of HF-Transformers and Google"
question = "Who has supported Manuel?"
get_answer(question, context)
# output: 'HF-Transformers and Google'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/t5-small-finetuned-emotion
|
mrm8488
| 2020-12-11T21:56:24Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:emotion",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- emotion
---
# T5-small fine-tuned for Emotion Recognition 😂😢😡😃😯
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) dataset for **Emotion Recognition** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Sentiment Recognition) - Dataset 📚
[Elvis Saravia](https://twitter.com/omarsar0) has gathered a great [dataset](https://github.com/dair-ai/emotion_dataset) for emotion recognition. It allows to classifiy the text into one of the following **6** emotions:
- sadness 😢
- joy 😃
- love 🥰
- anger 😡
- fear 😱
- surprise 😯
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
## Test set metrics 🧾
| |precision | recall | f1-score |support|
|----------|----------|---------|----------|-------|
|anger | 0.92| 0.93| 0.92| 275|
|fear | 0.90| 0.90| 0.90| 224|
|joy | 0.97| 0.91| 0.94| 695|
|love | 0.75| 0.89| 0.82| 159|
|sadness | 0.96| 0.97| 0.96| 581|
|surpirse | 0.73| 0.80| 0.76| 66|
| |
|accuracy| | | 0.92| 2000|
|macro avg| 0.87| 0.90| 0.88| 2000|
|weighted avg| 0.93| 0.92| 0.92| 2000|
Confusion Matrix
## Model in Action 🚀
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-emotion")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-emotion")
def get_emotion(text):
input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
output = model.generate(input_ids=input_ids,
max_length=2)
dec = [tokenizer.decode(ids) for ids in output]
label = dec[0]
return label
get_emotion("i feel as if i havent blogged in ages are at least truly blogged i am doing an update cute") # Output: 'joy'
get_emotion("i have a feeling i kinda lost my best friend") # Output: 'sadness'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/t5-base-finetuned-wikiSQL-sql-to-en
|
mrm8488
| 2020-12-11T21:56:17Z | 35 | 12 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:wikisql",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- wikisql
---
# T5-base fine-tuned on WikiSQL for SQL to English translation
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **SQL** to **English** **translation** task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| wikisql | train | 56355 |
| wikisql | valid | 14436 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('wikisql', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('wikisql', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql-to-en")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql-to-en")
def get_explanation(query):
input_text = "translate Sql to English: %s </s>" % query
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
query = "SELECT COUNT Params form model where location=HF-Hub"
get_explanation(query)
# output: 'How many parameters form model for HF-hub?'
```
Play with it in a Colab:
<img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg">
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/squeezebert-finetuned-squadv2
|
mrm8488
| 2020-12-11T21:55:26Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"squeezebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2006.11316",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- squad_v2
---
# SqueezeBERT + SQuAD v2
[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v2](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of SqueezeBERT
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/).
The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone.
More about the model [here](https://arxiv.org/abs/2004.02984)
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
**SQuAD2.0** combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python /content/transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path squeezebert/squeezebert-uncased \
--do_train \
--do_eval \
--do_lower_case \
--train_file /content/dataset/train-v2.0.json \
--predict_file /content/dataset/dev-v2.0.json \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 15 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output_dir \
--overwrite_output_dir \
--version_2_with_negative \
--save_steps 2000
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **69.98** |
| **F1** | **74.14** |
Model Size: **195 MB**
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-finetuned-squadv2')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'Who did identified it ?'
})
# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.9768241047859192, 'start': 96}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/electricidad-base-generator
|
mrm8488
| 2020-12-11T21:54:10Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"es",
"arxiv:1406.2661",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: es
thumbnail: https://i.imgur.com/uxAvBfh.png
widget:
- text: "Madrid es una ciudad muy [MASK] en España."
---
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-generator** (uncased) is a ```base``` Electra like model (generator in this case) trained on a + 20 GB of the [OSCAR](https://oscar-corpus.com/) Spanish corpus.
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
## Fast example of usage 🚀
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="mrm8488/electricidad-base-generator",
tokenizer="mrm8488/electricidad-base-generator"
)
print(
fill_mask(f"HuggingFace está creando {fill_mask.tokenizer.mask_token} que la comunidad usa para resolver tareas de NLP.")
)
# Output: [{'sequence': '[CLS] huggingface esta creando herramientas que la comunidad usa para resolver tareas de nlp. [SEP]', 'score': 0.0896105170249939, 'token': 8760, 'token_str': 'herramientas'}, ...]
```
## Acknowledgments
I thank [🤗/transformers team](https://github.com/huggingface/transformers) for allowing me to train the model (specially to [Julien Chaumond](https://twitter.com/julien_c)).
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/electra-small-finetuned-squadv1
|
mrm8488
| 2020-12-11T21:53:59Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"en",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
---
# Electra small ⚡ + SQuAD v1 ❓
[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
## Details of the downstream task (Q&A) - Dataset 📚
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type electra \
--model_name_or_path 'google/electra-small-discriminator' \
--do_eval \
--do_train \
--do_lower_case \
--train_file '/content/dataset/train-v1.1.json' \
--predict_file '/content/dataset/dev-v1.1.json' \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir '/content/output' \
--overwrite_output_dir \
--save_steps 1000
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **77.70** |
| **F1** | **85.74** |
| **Size**| **50 MB** |
Very good metrics for such a "small" model!
```json
{
'exact': 77.70104068117313,
'f1': 85.73991234187997,
'total': 10570,
'HasAns_exact': 77.70104068117313,
'HasAns_f1': 85.73991234187997,
'HasAns_total': 10570,
'best_exact': 77.70104068117313,
'best_exact_thresh': 0.0,
'best_f1': 85.73991234187997,
'best_f1_thresh': 0.0
}
```
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/electra-small-finetuned-squadv1')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'What has been discovered by scientists from China ?'
})
# Output:
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.7950334108113424, 'start': 0}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
moumeneb1/flaubert-base-cased-ecology_crisis
|
moumeneb1
| 2020-12-11T21:51:41Z | 5 | 0 |
transformers
|
[
"transformers",
"flaubert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# Flaubert-base-cased-ecology_crisis
An adapted [__Flaubert/Flaubert_base-cased model__](https://github.com/getalp/Flaubert) Trained further on a Language modeling Task of unlabeled French tweets used to create the [CrisisDataset](https://github.com/DiegoKoz/french_ecological_crisis), The intermediate task of masqued language modeling helped us improve the results on our [paper](http://www.sciencedirect.com/science/article/pii/S0306457320300650) compared to the standard flaubert-base-cased model.
If you use this pretrained model on your work, please cite us as follows 🤗
```
@article{Kozlowski-et-al2020,
title = "A three-level classification of French tweets in ecological crises",
journal = "Information Processing & Management",
volume = "57",
number = "5",
pages = "102284",
year = "2020",
issn = "0306-4573",
doi = "https://doi.org/10.1016/j.ipm.2020.102284",
url = "http://www.sciencedirect.com/science/article/pii/S0306457320300650",
author = "Diego Kozlowski and Elisa Lannelongue and Frédéric Saudemont and Farah Benamara and Alda Mari and Véronique Moriceau and Abdelmoumene Boumadane",
keywords = "Crisis response from social media, Machine learning, Natural language processing, Transfer learning",
}
```
|
m3hrdadfi/bert2bert-fa-news-headline
|
m3hrdadfi
| 2020-12-11T21:50:16Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
tags:
- summarization
---
A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.
For more detail, please follow the [News Headline Generation](https://github.com/m3hrdadfi/news-headline-generation) repo.
## Eval results
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.
| % | Precision | Recall | FMeasure |
|:-------:|:---------:|:------:|:--------:|
| ROUGE-1 | 43.78 | 45.52 | 43.54 |
| ROUGE-2 | 24.50 | 25.30* | 24.24 |
| ROUGE-L | 41.20 | 42.22 | 40.76 |
## Questions?
Post a Github issue on the [News Headline Generation](https://github.com/hooshvare/news-headline-generation/issues) repo.
|
loodos/electra-small-turkish-cased-discriminator
|
loodos
| 2020-12-11T21:49:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: tr
---
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Small-discriminator (cased)
This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layers size trained on cased Turkish dataset.
## Usage
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
model = AutoModelWithLMHead.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
```
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
|
loodos/electra-base-turkish-uncased-discriminator
|
loodos
| 2020-12-11T21:49:30Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: tr
---
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Base-discriminator (uncased)
This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset.
## Usage
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-uncased-discriminator", do_lower_case=False)
model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-uncased-discriminator")
normalizer = TextNormalization()
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
- "I" and "İ" to 'i'
- 'i' and 'ı' to 'I'
respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
|
krevas/finance-koelectra-small-discriminator
|
krevas
| 2020-12-11T21:48:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ko
---
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-discriminator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
> the discriminator of a GAN.
More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
## Stats
The current version of the model is trained on a financial news data of Naver news.
The final training corpus has a size of 25GB and 2.3B tokens.
This model was trained a cased model on a TITAN RTX for 500k steps.
## Usage
```python
from transformers import ElectraForPreTraining, ElectraTokenizer
import torch
discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-small-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-small-discriminator")
sentence = "내일 해당 종목이 대폭 상승할 것이다"
fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
```
# Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/krevas).
|
krevas/finance-koelectra-base-discriminator
|
krevas
| 2020-12-11T21:48:27Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ko
---
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-discriminator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
> the discriminator of a GAN.
More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
## Stats
The current version of the model is trained on a financial news data of Naver news.
The final training corpus has a size of 25GB and 2.3B tokens.
This model was trained a cased model on a TITAN RTX for 500k steps.
## Usage
```python
from transformers import ElectraForPreTraining, ElectraTokenizer
import torch
discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-base-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-base-discriminator")
sentence = "내일 해당 종목이 대폭 상승할 것이다"
fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
```
# Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/krevas).
|
jplu/tf-xlm-roberta-large
|
jplu
| 2020-12-11T21:48:04Z | 144 | 1 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Tensorflow XLM-RoBERTa
In this repository you will find different versions of the XLM-RoBERTa model for Tensorflow.
## XLM-RoBERTa
[XLM-RoBERTa](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) is a scaled cross lingual sentence encoder. It is trained on 2.5T of data across 100 languages data filtered from Common Crawl. XLM-R achieves state-of-the-arts results on multiple cross lingual benchmarks.
## Model Weights
| Model | Downloads
| -------------------------------- | ---------------------------------------------------------------------------------------------------------------
| `jplu/tf-xlm-roberta-base` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/jplu/tf-xlm-roberta-base/config.json) • [`tf_model.h5`](https://s3.amazonaws.com/models.huggingface.co/bert/jplu/tf-xlm-roberta-base/tf_model.h5)
| `jplu/tf-xlm-roberta-large` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/jplu/tf-xlm-roberta-large/config.json) • [`tf_model.h5`](https://s3.amazonaws.com/models.huggingface.co/bert/jplu/tf-xlm-roberta-large/tf_model.h5)
## Usage
With Transformers >= 2.4 the Tensorflow models of XLM-RoBERTa can be loaded like:
```python
from transformers import TFXLMRobertaModel
model = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base")
```
Or
```
model = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-large")
```
## Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/jplu).
## Acknowledgments
Thanks to all the Huggingface team for the support and their amazing library!
|
indobenchmark/indobert-lite-large-p2
|
indobenchmark
| 2020-12-11T21:45:59Z | 186 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT-Lite Large Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) |
## How to use
### Load model and tokenizer
```python
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-large-p2")
model = AutoModel.from_pretrained("indobenchmark/indobert-lite-large-p2")
```
### Extract contextual representation
```python
x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1)
print(x, model(x)[0].sum())
```
## Authors
<b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
```
|
indobenchmark/indobert-lite-large-p1
|
indobenchmark
| 2020-12-11T21:45:56Z | 40 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"indobert",
"indobenchmark",
"indonlu",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"license:mit",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT-Lite Large Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) |
## How to use
### Load model and tokenizer
```python
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-large-p1")
model = AutoModel.from_pretrained("indobenchmark/indobert-lite-large-p1")
```
### Extract contextual representation
```python
x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1)
print(x, model(x)[0].sum())
```
## Authors
<b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
```
|
illuin/camembert-base-fquad
|
illuin
| 2020-12-11T21:45:27Z | 506 | 7 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"fr",
"dataset:fquad",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: fr
tags:
- question-answering
- camembert
license: gpl-3.0
datasets:
- fquad
---
# camembert-base-fquad
## Description
A native French Question Answering model [CamemBERT-base](https://camembert-model.fr/) fine-tuned on [FQuAD](https://fquad.illuin.tech/).
## Evaluation results
On the development set.
```shell
{"f1": 88.1, "exact_match": 78.1}
```
On the test set.
```shell
{"f1": 88.3, "exact_match": 78.0}
```
## Usage
```python
from transformers import pipeline
nlp = pipeline('question-answering', model='illuin/camembert-base-fquad', tokenizer='illuin/camembert-base-fquad')
nlp({
'question': "Qui est Claude Monet?",
'context': "Claude Monet, né le 14 novembre 1840 à Paris et mort le 5 décembre 1926 à Giverny, est un peintre français et l’un des fondateurs de l'impressionnisme."
})
```
## Citation
If you use our work, please cite:
```bibtex
@article{dHoffschmidt2020FQuADFQ,
title={FQuAD: French Question Answering Dataset},
author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich},
journal={ArXiv},
year={2020},
volume={abs/2002.06071}
}
```
|
healx/gpt-2-pubmed-medium
|
healx
| 2020-12-11T21:43:41Z | 3,105 | 2 |
transformers
|
[
"transformers",
"pytorch",
"arxiv:2004.13845",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
GPT-2 (355M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
|
facebook/rag-token-base
|
facebook
| 2020-12-11T21:39:44Z | 7,396 | 17 |
transformers
|
[
"transformers",
"pytorch",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- wiki_dpr
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
## RAG
This is a non-finetuned version of the RAG-Token model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
Rag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a `RagRetriever` instance. The *question encoder* can be any model that can be loaded with `AutoModel` and the *generator* can be any model that can be loaded with `AutoModelForSeq2SeqLM`.
This model is a non-finetuned RAG-Token model and was created as follows:
```python
from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration, AutoTokenizer
model = RagTokenForGeneration.from_pretrained_question_encoder_generator("facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large")
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
retriever = RagRetriever(model.config, question_encoder_tokenizer, generator_tokenizer)
model.save_pretrained("./")
tokenizer.save_pretrained("./")
retriever.save_pretrained("./")
```
Note that the model is *uncased* so that all capital input letters are converted to lower-case.
## Usage:
*Note*: the model uses the *dummy* retriever as a default. Better results are obtained by using the full retriever,
by setting `config.index_name="legacy"` and `config.use_dummy_dataset=False`.
The model can be fine-tuned as follows:
```python
from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
retriever = RagRetriever.from_pretrained("facebook/rag-token-base")
model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base", retriever=retriever)
input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt")
outputs = model(input_dict["input_ids"], labels=input_dict["labels"])
loss = outputs.loss
# train on loss
```
|
elgeish/cs224n-squad2.0-albert-xxlarge-v1
|
elgeish
| 2020-12-11T21:39:01Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- exbert
---
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,
evaluation and model selection were performed using roughly half of the official
dev set, 6078 examples, picked at random. The data files can be found at
<https://github.com/elgeish/squad/tree/master/data> — this is the Winter 2020
version. Given that the official SQuAD2.0 dev set contains the project's test
set, students must make sure not to use the official SQuAD2.0 dev set in any way
— including the use of models fine-tuned on the official SQuAD2.0, since they
used the official SQuAD2.0 dev set for model selection.
<a href="https://huggingface.co/exbert/?model=elgeish/cs224n-squad2.0-albert-xxlarge-v1">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
## Results
```json
{
"exact": 85.93287265547877,
"f1": 88.91258331187983,
"total": 6078,
"HasAns_exact": 84.36426116838489,
"HasAns_f1": 90.58786301361013,
"HasAns_total": 2910,
"NoAns_exact": 87.37373737373737,
"NoAns_f1": 87.37373737373737,
"NoAns_total": 3168,
"best_exact": 85.93287265547877,
"best_exact_thresh": 0.0,
"best_f1": 88.91258331187993,
"best_f1_thresh": 0.0
}
```
## Notable Arguments
```json
{
"do_lower_case": true,
"doc_stride": 128,
"fp16": false,
"fp16_opt_level": "O1",
"gradient_accumulation_steps": 24,
"learning_rate": 3e-05,
"max_answer_length": 30,
"max_grad_norm": 1,
"max_query_length": 64,
"max_seq_length": 512,
"model_name_or_path": "albert-xxlarge-v1",
"model_type": "albert",
"num_train_epochs": 4,
"per_gpu_train_batch_size": 1,
"save_steps": 1000,
"seed": 42,
"train_batch_size": 1,
"version_2_with_negative": true,
"warmup_steps": 814,
"weight_decay": 0
}
```
## Environment Setup
```json
{
"transformers": "2.5.1",
"pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0",
"python": "3.6.5=hc3d631a_2",
"os": "Linux 4.15.0-1060-aws #62-Ubuntu SMP Tue Feb 11 21:23:22 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux",
"gpu": "Tesla V100-SXM2-16GB"
}
```
## How to Cite
```BibTeX
@misc{elgeish2020gestalt,
title={Gestalt: a Stacking Ensemble for SQuAD2.0},
author={Mohamed El-Geish},
journal={arXiv e-prints},
archivePrefix={arXiv},
eprint={2004.07067},
year={2020},
}
```
## Related Models
* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2)
* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2)
* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased)
* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base)
|
elgeish/cs224n-squad2.0-albert-large-v2
|
elgeish
| 2020-12-11T21:38:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- exbert
---
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,
evaluation and model selection were performed using roughly half of the official
dev set, 6078 examples, picked at random. The data files can be found at
<https://github.com/elgeish/squad/tree/master/data> — this is the Winter 2020
version. Given that the official SQuAD2.0 dev set contains the project's test
set, students must make sure not to use the official SQuAD2.0 dev set in any way
— including the use of models fine-tuned on the official SQuAD2.0, since they
used the official SQuAD2.0 dev set for model selection.
<a href="https://huggingface.co/exbert/?model=elgeish/cs224n-squad2.0-albert-large-v2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
## Results
```json
{
"exact": 79.2694965449161,
"f1": 82.50844352970152,
"total": 6078,
"HasAns_exact": 74.87972508591065,
"HasAns_f1": 81.64478342732858,
"HasAns_total": 2910,
"NoAns_exact": 83.30176767676768,
"NoAns_f1": 83.30176767676768,
"NoAns_total": 3168,
"best_exact": 79.2694965449161,
"best_exact_thresh": 0.0,
"best_f1": 82.50844352970155,
"best_f1_thresh": 0.0
}
```
## Notable Arguments
```json
{
"do_lower_case": true,
"doc_stride": 128,
"fp16": false,
"fp16_opt_level": "O1",
"gradient_accumulation_steps": 1,
"learning_rate": 3e-05,
"max_answer_length": 30,
"max_grad_norm": 1,
"max_query_length": 64,
"max_seq_length": 384,
"model_name_or_path": "albert-large-v2",
"model_type": "albert",
"num_train_epochs": 5,
"per_gpu_train_batch_size": 8,
"save_steps": 5000,
"seed": 42,
"train_batch_size": 8,
"version_2_with_negative": true,
"warmup_steps": 0,
"weight_decay": 0
}
```
## Environment Setup
```json
{
"transformers": "2.5.1",
"pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0",
"python": "3.6.5=hc3d631a_2",
"os": "Linux 4.15.0-1060-aws #62-Ubuntu SMP Tue Feb 11 21:23:22 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux",
"gpu": "Tesla V100-SXM2-16GB"
}
```
## How to Cite
```BibTeX
@misc{elgeish2020gestalt,
title={Gestalt: a Stacking Ensemble for SQuAD2.0},
author={Mohamed El-Geish},
journal={arXiv e-prints},
archivePrefix={arXiv},
eprint={2004.07067},
year={2020},
}
```
## Related Models
* [elgeish/cs224n-squad2.0-albert-base-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2)
* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1)
* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased)
* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base)
|
elgeish/cs224n-squad2.0-albert-base-v2
|
elgeish
| 2020-12-11T21:38:54Z | 1,062 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- exbert
---
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,
evaluation and model selection were performed using roughly half of the official
dev set, 6078 examples, picked at random. The data files can be found at
<https://github.com/elgeish/squad/tree/master/data> — this is the Winter 2020
version. Given that the official SQuAD2.0 dev set contains the project's test
set, students must make sure not to use the official SQuAD2.0 dev set in any way
— including the use of models fine-tuned on the official SQuAD2.0, since they
used the official SQuAD2.0 dev set for model selection.
<a href="https://huggingface.co/exbert/?model=elgeish/cs224n-squad2.0-albert-base-v2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
## Results
```json
{
"exact": 78.94044093451794,
"f1": 81.7724930324639,
"total": 6078,
"HasAns_exact": 76.28865979381443,
"HasAns_f1": 82.20385314478195,
"HasAns_total": 2910,
"NoAns_exact": 81.37626262626263,
"NoAns_f1": 81.37626262626263,
"NoAns_total": 3168,
"best_exact": 78.95689371503784,
"best_exact_thresh": 0.0,
"best_f1": 81.78894581298378,
"best_f1_thresh": 0.0
}
```
## Notable Arguments
```json
{
"do_lower_case": true,
"doc_stride": 128,
"fp16": false,
"fp16_opt_level": "O1",
"gradient_accumulation_steps": 24,
"learning_rate": 3e-05,
"max_answer_length": 30,
"max_grad_norm": 1,
"max_query_length": 64,
"max_seq_length": 384,
"model_name_or_path": "albert-base-v2",
"model_type": "albert",
"num_train_epochs": 3,
"per_gpu_train_batch_size": 8,
"save_steps": 5000,
"seed": 42,
"train_batch_size": 8,
"version_2_with_negative": true,
"warmup_steps": 0,
"weight_decay": 0
}
```
## Environment Setup
```json
{
"transformers": "2.5.1",
"pytorch": "1.4.0=py3.6_cuda10.1.243_cudnn7.6.3_0",
"python": "3.6.5=hc3d631a_2",
"os": "Linux 4.15.0-1060-aws #62-Ubuntu SMP Tue Feb 11 21:23:22 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux",
"gpu": "Tesla V100-SXM2-16GB"
}
```
## How to Cite
```BibTeX
@misc{elgeish2020gestalt,
title={Gestalt: a Stacking Ensemble for SQuAD2.0},
author={Mohamed El-Geish},
journal={arXiv e-prints},
archivePrefix={arXiv},
eprint={2004.07067},
year={2020},
}
```
## Related Models
* [elgeish/cs224n-squad2.0-albert-large-v2](https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2)
* [elgeish/cs224n-squad2.0-albert-xxlarge-v1](https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1)
* [elgeish/cs224n-squad2.0-distilbert-base-uncased](https://huggingface.co/elgeish/cs224n-squad2.0-distilbert-base-uncased)
* [elgeish/cs224n-squad2.0-roberta-base](https://huggingface.co/elgeish/cs224n-squad2.0-roberta-base)
|
txus/calbert-tiny-uncased
|
txus
| 2020-12-11T21:36:14Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"masked-lm",
"catalan",
"exbert",
"ca",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "ca"
tags:
- masked-lm
- catalan
- exbert
license: mit
---
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/).
For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert)
## Pre-trained models
| Model | Arch. | Training data |
| ----------------------------------- | -------------- | ---------------------- |
| `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) |
| `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) |
## How to use Calbert with HuggingFace
#### Load Calbert and its tokenizer:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-tiny-uncased")
model = AutoModel.from_pretrained("codegram/calbert-tiny-uncased")
model.eval() # disable dropout (or leave in train mode to finetune
```
#### Filling masks using pipeline
```python
from transformers import pipeline
calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-tiny-uncased", tokenizer="codegram/calbert-tiny-uncased")
results = calbert_fill_mask("M'agrada [MASK] això")
# results
# [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.4403671622276306, 'token': 61},
# {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.050061386078596115, 'token': 43},
# {'sequence': "[CLS] m'agrada veure aixo[SEP]", 'score': 0.026286985725164413, 'token': 157},
# {'sequence': "[CLS] m'agrada bastant aixo[SEP]", 'score': 0.022483550012111664, 'token': 2143},
# {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.014491282403469086, 'token': 4867}]
```
#### Extract contextual embedding features from Calbert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("M'és una mica igual")
# ['▁m', "'", 'es', '▁una', '▁mica', '▁igual']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [2, 109, 7, 71, 36, 371, 1103, 3]
# NB: Can be done in one step : tokenize.encode("M'és una mica igual")
# Feed tokens to Calbert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = model(encoded_sentence)
embeddings.size()
# torch.Size([1, 8, 312])
embeddings.detach()
# tensor([[[-0.2726, -0.9855, 0.9643, ..., 0.3511, 0.3499, -0.1984],
# [-0.2824, -1.1693, -0.2365, ..., -3.1866, -0.9386, -1.3718],
# [-2.3645, -2.2477, -1.6985, ..., -1.4606, -2.7294, 0.2495],
# ...,
# [ 0.8800, -0.0244, -3.0446, ..., 0.5148, -3.0903, 1.1879],
# [ 1.1300, 0.2425, 0.2162, ..., -0.5722, -2.2004, 0.4045],
# [ 0.4549, -0.2378, -0.2290, ..., -2.1247, -2.2769, -0.0820]]])
```
## Authors
CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.
<a href="https://huggingface.co/exbert/?model=codegram/calbert-tiny-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
aliosm/ai-soco-cpp-roberta-tiny
|
aliosm
| 2020-12-11T21:32:46Z | 0 | 0 | null |
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
license: "mit"
datasets:
- ai-soco
metrics:
- perplexity
---
# ai-soco-c++-roberta-tiny
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
aliosm/ai-soco-cpp-roberta-tiny-96-clas
|
aliosm
| 2020-12-11T21:32:40Z | 0 | 0 | null |
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
- classification
license: "mit"
datasets:
- ai-soco
metrics:
- accuracy
---
# ai-soco-c++-roberta-tiny-96-clas
## Model description
`ai-soco-c++-roberta-tiny-96` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from [`ai-soco-c++-roberta-tiny-96`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny-96) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
## Eval results
The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96-clas">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
aliosm/ai-soco-cpp-roberta-small
|
aliosm
| 2020-12-11T21:32:38Z | 0 | 0 | null |
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
license: "mit"
datasets:
- ai-soco
metrics:
- perplexity
---
# ai-soco-c++-roberta-small
## Model description
From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
aliosm/ai-soco-cpp-roberta-small-clas
|
aliosm
| 2020-12-11T21:32:36Z | 0 | 0 | null |
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "c++"
tags:
- exbert
- authorship-identification
- fire2020
- pan2020
- ai-soco
- classification
license: "mit"
datasets:
- ai-soco
metrics:
- accuracy
---
# ai-soco-c++-roberta-small-clas
## Model description
`ai-soco-c++-roberta-small` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from [`ai-soco-c++-roberta-small`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-small) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
## Eval results
The model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small-clas">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
akhooli/mbart-large-cc25-ar-en
|
akhooli
| 2020-12-11T21:32:04Z | 17 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"ar",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
tags:
- translation
language:
- ar
- en
license: mit
---
### mbart-large-ar-en
This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en.
Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing)
Note: model has limited training set, not fully trained (do not use for production).
Other models by me: [Abed Khooli](https://huggingface.co/akhooli)
|
ahotrod/electra_large_discriminator_squad2_512
|
ahotrod
| 2020-12-11T21:31:42Z | 22,523 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
### with the following results:
```
"exact": 87.09677419354838,
"f1": 89.98343832723452,
"total": 11873,
"HasAns_exact": 84.66599190283401,
"HasAns_f1": 90.44759839056285,
"HasAns_total": 5928,
"NoAns_exact": 89.52060555088309,
"NoAns_f1": 89.52060555088309,
"NoAns_total": 5945,
"best_exact": 87.09677419354838,
"best_exact_thresh": 0.0,
"best_f1": 89.98343832723432,
"best_f1_thresh": 0.0
```
### from script:
```
python ${EXAMPLES}/run_squad.py \
--model_type electra \
--model_name_or_path google/electra-large-discriminator \
--do_train \
--do_eval \
--train_file ${SQUAD}/train-v2.0.json \
--predict_file ${SQUAD}/dev-v2.0.json \
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--warmup_steps 306 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--per_gpu_train_batch_size 8 \
--gradient_accumulation_steps 16 \
--per_gpu_eval_batch_size 128 \
--fp16 \
--fp16_opt_level O1 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--output_dir ${MODEL_PATH}
```
### using the following system & software:
```
Transformers: 2.11.0
PyTorch: 1.5.0
TensorFlow: 2.2.0
Python: 3.8.1
OS/Platform: Linux-5.3.0-59-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```
|
Rostlab/prot_t5_xl_bfd
|
Rostlab
| 2020-12-11T21:30:13Z | 2,933 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"protein language model",
"dataset:BFD",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language: protein
tags:
- protein language model
datasets:
- BFD
---
# ProtT5-XL-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
## Model description
ProtT5-XL-BFD is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion.
This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
One important difference between this T5 model and the original T5 version is the denosing objective.
The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective.
The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input.
It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape.
shape.
This implied learning some of the grammar of the language of life realized in protein sequences.
## Intended uses & limitations
The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder.
### How to use
Here is how to use this model to extract the features of a given protein sequence in PyTorch:
```python
from transformers import T5Tokenizer, T5Model
import re
import torch
tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_bfd', do_lower_case=False)
model = T5Model.from_pretrained("Rostlab/prot_t5_xl_bfd")
sequences_Example = ["A E T C Z A O","S K T Z P"]
sequences_Example = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences_Example]
ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True)
input_ids = torch.tensor(ids['input_ids'])
attention_mask = torch.tensor(ids['attention_mask'])
with torch.no_grad():
embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None)
# For feature extraction we recommend to use the encoder embedding
encoder_embedding = embedding[2].cpu().numpy()
decoder_embedding = embedding[0].cpu().numpy()
```
## Training data
The ProtT5-XL-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.
## Training procedure
### Preprocessing
The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X".
The inputs of the model are then of the form:
```
Protein Sequence [EOS]
```
The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens.
The details of the masking procedure for each sequence are as follows:
- 15% of the amino acids are masked.
- In 90% of the cases, the masked amino acids are replaced by `[MASK]` token.
- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
### Pretraining
The model was trained on a single TPU Pod V3-1024 for 1.2 million steps in total, using sequence length 512 (batch size 4k).
It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
## Evaluation results
When the model is used for feature etraction, this model achieves the following results:
Test results :
| Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| CASP12 | 77 | 66 | | |
| TS115 | 85 | 74 | | |
| CB513 | 84 | 71 | | |
| DeepLoc | | | 77 | 91 |
### BibTeX entry and citation info
```bibtex
@article {Elnaggar2020.07.12.199554,
author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
elocation-id = {2020.07.12.199554},
year = {2020},
doi = {10.1101/2020.07.12.199554},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
journal = {bioRxiv}
}
```
> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)
|
Rostlab/prot_bert_bfd
|
Rostlab
| 2020-12-11T21:30:10Z | 47,440 | 15 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"fill-mask",
"protein language model",
"dataset:BFD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: protein
tags:
- protein language model
datasets:
- BFD
---
# ProtBert-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
## Model description
ProtBert-BFD is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion.
This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents
This means the Next sentence prediction is not used, as each sequence is treated as a complete document.
The masking follows the original Bert training with randomly masks 15% of the amino acids in the input.
At the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein
shape.
This implied learning some of the grammar of the language of life realized in protein sequences.
## Intended uses & limitations
The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
We have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import BertForMaskedLM, BertTokenizer, pipeline
>>> tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )
>>> model = BertForMaskedLM.from_pretrained("Rostlab/prot_bert_bfd")
>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T')
[{'score': 0.1165614128112793,
'sequence': '[CLS] D L I P T S S K L V V L D T S L Q V K K A F F A L V T [SEP]',
'token': 5,
'token_str': 'L'},
{'score': 0.08976086974143982,
'sequence': '[CLS] D L I P T S S K L V V V D T S L Q V K K A F F A L V T [SEP]',
'token': 8,
'token_str': 'V'},
{'score': 0.08864385634660721,
'sequence': '[CLS] D L I P T S S K L V V S D T S L Q V K K A F F A L V T [SEP]',
'token': 10,
'token_str': 'S'},
{'score': 0.06227643042802811,
'sequence': '[CLS] D L I P T S S K L V V A D T S L Q V K K A F F A L V T [SEP]',
'token': 6,
'token_str': 'A'},
{'score': 0.06194969266653061,
'sequence': '[CLS] D L I P T S S K L V V T D T S L Q V K K A F F A L V T [SEP]',
'token': 15,
'token_str': 'T'}]
```
Here is how to use this model to get the features of a given protein sequence in PyTorch:
```python
from transformers import BertModel, BertTokenizer
import re
tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )
model = BertModel.from_pretrained("Rostlab/prot_bert_bfd")
sequence_Example = "A E T C Z A O"
sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example)
encoded_input = tokenizer(sequence_Example, return_tensors='pt')
output = model(**encoded_input)
```
## Training data
The ProtBert-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.
## Training procedure
### Preprocessing
The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21.
The inputs of the model are then of the form:
```
[CLS] Protein Sequence A [SEP] Protein Sequence B [SEP]
```
Furthermore, each protein sequence was treated as a separate document.
The preprocessing step was performed twice, once for a combined length (2 sequences) of less than 512 amino acids, and another time using a combined length (2 sequences) of less than 2048 amino acids.
The details of the masking procedure for each sequence followed the original Bert model as following:
- 15% of the amino acids are masked.
- In 80% of the cases, the masked amino acids are replaced by `[MASK]`.
- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
- In the 10% remaining cases, the masked amino acids are left as is.
### Pretraining
The model was trained on a single TPU Pod V3-1024 for one million steps in total.
800k steps using sequence length 512 (batch size 32k), and 200K steps using sequence length 2048 (batch size 6k).
The optimizer used is Lamb with a learning rate of 0.002, a weight decay of 0.01, learning rate warmup for 140k steps and linear decay of the learning rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Test results :
| Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| CASP12 | 76 | 65 | | |
| TS115 | 84 | 73 | | |
| CB513 | 83 | 70 | | |
| DeepLoc | | | 78 | 91 |
### BibTeX entry and citation info
```bibtex
@article {Elnaggar2020.07.12.199554,
author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
elocation-id = {2020.07.12.199554},
year = {2020},
doi = {10.1101/2020.07.12.199554},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
journal = {bioRxiv}
}
```
> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)
|
Ogayo/Hel-ach-en
|
Ogayo
| 2020-12-11T21:30:01Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"ach",
"en",
"dataset:JW300",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:04Z |
---
language:
- ach
- en
tags:
- translation
license: cc-by-4.0
datasets:
- JW300
metrics:
- bleu
---
# HEL-ACH-EN
## Model description
MT model translating Acholi to English initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en) on HuggingFace.
## Intended uses & limitations
Machine Translation experiments. Do not use for sensitive tasks.
#### How to use
```python
# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Ogayo/Hel-ach-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Ogayo/Hel-ach-en")
```
#### Limitations and bias
Trained on Jehovah Witnesses data so contains theirs and Christian views.
## Training data
Trained on OPUS JW300 data.
Initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en?text=Bed+gi+nyasi+mar+chieng%27+nyuol+mopong%27+gi+mor%21#model_card)
## Training procedure
Remove duplicates and rows with no alphabetic characters. Used GPU
## Eval results
testset | BLEU
--- | ---
JW300.luo.en| 46.1
|
cinmodel/electra-small-japanese-generator
|
cinmodel
| 2020-12-11T21:26:17Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"ja",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: ja
---
## Japanese ELECTRA-small
We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/latest), using the [Byte-Pair Encoding](https://www.aclweb.org/anthology/P16-1162.pdf) method and building on an initial tokenization with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd). For optimal performance, please take care to set your MeCab dictionary appropriately.
```
# ELECTRA-small generator usage
from transformers import BertJapaneseTokenizer, ElectraForMaskedLM
tokenizer = BertJapaneseTokenizer.from_pretrained('Cinnamon/electra-small-japanese-generator', mecab_kwargs={"mecab_option": "-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd"})
model = ElectraForMaskedLM.from_pretrained('Cinnamon/electra-small-japanese-generator')
```
|
nielsr/tapas-base
|
nielsr
| 2020-12-11T11:12:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tapas",
"feature-extraction",
"sequence-classification",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
---
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `revision="v1"`, which corresponds to `tapas_inter_masklm_base`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding one or more classification heads on top of the pre-trained model, and then
jointly train these randomly initialized classification heads with the base model on a downstream task.
## Intended uses & limitations
You can use the raw model for getting hidden representatons about table-question pairs, but it's mostly intended to be fine-tuned on a downstream task such as question answering or sequence classification. See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Pre-training
The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.
The optimizer used is Adam with a learning rate of 5e-5, and a warmup
ratio of 0.01.
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
stefan-it/flair-ner-conll03
|
stefan-it
| 2020-12-11T10:07:20Z | 7 | 0 |
flair
|
[
"flair",
"pytorch",
"sequence-tagger-model",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- flair
- sequence-tagger-model
license: mit
---
# CoNLL-2003 NER Model
Imported sequence tagger model for Flair, that was trained on English CoNLL-2003 corpus for NER.
|
google/t5-11b-ssm-wqo
|
google
| 2020-12-07T08:47:33Z | 0 | 1 | null |
[
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:web_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
datasets:
- c4
- wikipedia
- web_questions
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Web Questions (WQ)](https://huggingface.co/datasets/web_questions).
**Note**: The model was fine-tuned on 90% of the train splits of [Web Questions (WQ)](https://huggingface.co/datasets/web_questions) for 20k steps and validated on the held-out 10% of the train split.
Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)
Paper: [How Much Knowledge Can You Pack
Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)
Authors: *Adam Roberts, Colin Raffel, Noam Shazeer*
## Results on Web Questions - Test Set
|Id | link | Exact Match |
|---|---|---|
|**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wqo**|**40.8**|
|T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wqo|42.8|
## Usage
The model can be used as follows for **closed book question answering**:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wqo")
t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wqo")
input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]
print(t5_tok.decode(gen_output, skip_special_tokens=True))
```
## Abstract
It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.
|
Parth/mT5-question-generator
|
Parth
| 2020-12-01T03:38:27Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
from transformers import MT5ForConditionalGeneration, AutoTokenizer
model = MT5ForConditionalGeneration.from_pretrained("Parth/mT5-question-generator")
tokenizer = AutoTokenizer.from_pretrained("google/mt5-base")
|
seduerr/t5_base_paws_ger
|
seduerr
| 2020-11-30T11:17:06Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# T5 Base with Paraphrases in German Language
This T5 base model has been trained with the German part of the PAWS-X data set.
It can be used as any T5 model and will generated paraphrases with the prompt keyword: 'paraphrase: '__GermanSentence__
Please contact me, if you need more information (sduerr@mit.edu).
Thank you.
Sebastian
|
julien-c/flair-de-ner
|
julien-c
| 2020-11-26T21:59:38Z | 12 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
datasets:
- conll2003
inference: false
---
## Flair NER model `de-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/de-ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from flair.models import SequenceTagger
sentence = Sentence(
"Mein Name ist Julien, ich lebe zurzeit in Paris, ich arbeite bei Hugging Face, Inc."
)
tagger = SequenceTagger.load("julien-c/flair-de-ner")
# predict NER tags
tagger.predict(sentence)
# print sentence with predicted tags
print(sentence.to_tagged_string())
```
yields the following output:
> `Mein Name ist Julien <S-PER> , ich lebe zurzeit in Paris <S-LOC> , ich arbeite bei Hugging <B-ORG> Face <E-ORG> , Inc <S-ORG> .`
### Thanks [@stefan-it](https://huggingface.co/stefan-it) for the Flair integration ❤️ 🔥
|
sshleifer/bb3b-tok
|
sshleifer
| 2020-09-25T18:06:31Z | 3 | 0 |
transformers
|
[
"transformers",
"blenderbot",
"text2text-generation",
"translation",
"facebook",
"convAI",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail:
tags:
- translation
- facebook
- convAI
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
# Blenderbot-3B
## Model description
+ [Paper](https://arxiv.org/abs/1907.06616).
+ [Original PARLAI Code]
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers.tokenization_fsmt import FSMTTokenizer
from transformers.modeling_fsmt import FSMTForConditionalGeneration
mname = "facebook/wmt19-en-ru"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "Machine learning is great, isn't it?"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # Машинное обучение - это здорово, не так ли?
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
en-ru | [36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724) | 33.47
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR=en-ru
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020},
title={Facebook FAIR's WMT19 News Translation Task Submission},
author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
booktitle={Proc. of WMT},
}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
|
Capreolus/electra-base-msmarco
|
Capreolus
| 2020-09-08T14:53:10Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"text-classification",
"arxiv:2008.09093",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
# capreolus/electra-base-msmarco
## Model description
ELECTRA-Base model (`google/electra-base-discriminator`) fine-tuned on the MS MARCO passage classification task. It is intended to be used as a `ForSequenceClassification` model, but requires some modification since it contains a BERT classification head rather than the standard ELECTRA classification head. See the [TFElectraRelevanceHead](https://github.com/capreolus-ir/capreolus/blob/master/capreolus/reranker/TFBERTMaxP.py) in the Capreolus BERT-MaxP implementation for a usage example.
This corresponds to the ELECTRA-Base model used to initialize PARADE (ELECTRA) in [PARADE: Passage Representation Aggregation for Document Reranking](https://arxiv.org/abs/2008.09093) by Li et al. It was converted from the released [TFv1 checkpoint](https://zenodo.org/record/3974431/files/vanilla_electra_base_on_MSMARCO.tar.gz). Please cite the PARADE paper if you use these weights.
|
textattack/distilbert-base-uncased-ag-news
|
textattack
| 2020-07-07T22:01:14Z | 462 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model CardThis `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9478947368421052, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/distilbert-base-cased-snli
|
textattack
| 2020-07-06T16:37:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `distilbert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 256, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8768542979069295, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/xlnet-base-cased-rotten-tomatoes
|
textattack
| 2020-07-06T16:36:38Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9071294559099438, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/distilbert-base-uncased-rotten-tomatoes
|
textattack
| 2020-07-06T16:36:02Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8395872420262664, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-rotten-tomatoes
|
textattack
| 2020-07-06T16:35:34Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8808630393996247, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/xlnet-base-cased-imdb
|
textattack
| 2020-07-06T16:35:25Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 512.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.95352, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/xlnet-base-cased-WNLI
|
textattack
| 2020-07-06T16:34:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.5774647887323944, as measured by the
eval set accuracy, found after 0 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-WNLI
|
textattack
| 2020-07-06T16:33:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.5915492957746479, as measured by the
eval set accuracy, found after 0 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/xlnet-base-cased-STS-B
|
textattack
| 2020-07-06T16:33:08Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a regression task, the model was trained with a mean squared error loss function.
The best score the model achieved on this task was 0.8892630070017784, as measured by the
eval set pearson correlation, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-STS-B
|
textattack
| 2020-07-06T16:32:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a regression task, the model was trained with a mean squared error loss function.
The best score the model achieved on this task was 0.9064220351504577, as measured by the
eval set pearson correlation, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/distilbert-base-uncased-RTE
|
textattack
| 2020-07-06T16:31:28Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.6570397111913358, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/xlnet-base-cased-MRPC
|
textattack
| 2020-07-06T16:30:46Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8897058823529411, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/distilbert-base-uncased-MRPC
|
textattack
| 2020-07-06T16:30:12Z | 31 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8578431372549019, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-MRPC
|
textattack
| 2020-07-06T16:29:43Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8970588235294118, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/distilbert-base-uncased-CoLA
|
textattack
| 2020-07-06T16:29:03Z | 3,039 | 3 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8235858101629914, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-CoLA
|
textattack
| 2020-07-06T16:28:50Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8245445829338447, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/albert-base-v2-rotten_tomatoes
|
textattack
| 2020-06-25T20:00:46Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset
This `albert-base-v2` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8855534709193246, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
djstrong/bg_cs_pl_ru_cased_L-12_H-768_A-12
|
djstrong
| 2020-02-15T11:33:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Slavic BERT from https://github.com/deepmipt/Slavic-BERT-NER http://files.deeppavlov.ai/deeppavlov_data/bg_cs_pl_ru_cased_L-12_H-768_A-12.tar.gz
|
csavzzcw/blockassist-bc-tawny_robust_buffalo_1757140004
|
csavzzcw
| 2025-09-06T06:27:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tawny robust buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T06:26:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tawny robust buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Alexshake78/Qwen3-0.6B-Gensyn-Swarm-darting_endangered_eel
|
Alexshake78
| 2025-09-06T06:26:50Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am darting_endangered_eel",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-26T21:12:47Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am darting_endangered_eel
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
a1ex971/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-patterned_arctic_shrimp
|
a1ex971
| 2025-09-06T06:26:43Z | 161 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am patterned_arctic_shrimp",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-23T13:39:39Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am patterned_arctic_shrimp
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
leonmullerrr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_wild_mouse
|
leonmullerrr
| 2025-09-06T06:26:22Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am coiled wild mouse",
"trl",
"genrl-swarm",
"I am coiled_wild_mouse",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-04T13:50:15Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_wild_mouse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am coiled wild mouse
- trl
- genrl-swarm
- I am coiled_wild_mouse
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_wild_mouse
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="leonmullerrr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_wild_mouse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gabrieln2h/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_stubby_bear
|
gabrieln2h
| 2025-09-06T06:26:19Z | 165 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am bipedal_stubby_bear",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T18:19:15Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am bipedal_stubby_bear
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gajahgajah/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_armored_wildebeest
|
gajahgajah
| 2025-09-06T06:26:19Z | 87 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am fanged_armored_wildebeest",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T17:47:01Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am fanged_armored_wildebeest
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/FractalSoup-L3-8b-GGUF
|
mradermacher
| 2025-09-06T06:26:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-06T06:26:14Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Entropicengine/FractalSoup-L3-8b
|
alsandeer33/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo
|
alsandeer33
| 2025-09-06T06:26:06Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am flightless arctic kangaroo",
"trl",
"genrl-swarm",
"I am flightless_arctic_kangaroo",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-04T13:54:45Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am flightless arctic kangaroo
- trl
- genrl-swarm
- I am flightless_arctic_kangaroo
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="alsandeer33/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
yuuutre/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_bold_mule
|
yuuutre
| 2025-09-06T06:25:58Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am enormous_bold_mule",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T15:13:41Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am enormous_bold_mule
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AchyutaGH/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grazing_ladybug
|
AchyutaGH
| 2025-09-06T06:25:55Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slender grazing ladybug",
"trl",
"genrl-swarm",
"I am slender_grazing_ladybug",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-18T23:00:30Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grazing_ladybug
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slender grazing ladybug
- trl
- genrl-swarm
- I am slender_grazing_ladybug
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grazing_ladybug
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AchyutaGH/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grazing_ladybug", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Alinaaa123/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_sizable_chicken
|
Alinaaa123
| 2025-09-06T06:25:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am thick_sizable_chicken",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T06:50:12Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am thick_sizable_chicken
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Mafikss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_unseen_impala
|
Mafikss
| 2025-09-06T06:25:52Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am leaping unseen impala",
"trl",
"genrl-swarm",
"I am leaping_unseen_impala",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-02T18:03:09Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_unseen_impala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am leaping unseen impala
- trl
- genrl-swarm
- I am leaping_unseen_impala
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_unseen_impala
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Mafikss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_unseen_impala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
nick00991/Qwen3-0.6B-Gensyn-Swarm-finicky_bristly_lion
|
nick00991
| 2025-09-06T06:25:50Z | 135 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am finicky_bristly_lion",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T03:02:35Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am finicky_bristly_lion
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bah63843/blockassist-bc-plump_fast_antelope_1757139902
|
bah63843
| 2025-09-06T06:25:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T06:25:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ankitkushwaha90/safetensor_model_fine_tuning_project
|
ankitkushwaha90
| 2025-09-06T06:25:48Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"token-classification",
"en",
"base_model:c2p-cmd/FaceEmotionClassifier",
"base_model:adapter:c2p-cmd/FaceEmotionClassifier",
"license:mit",
"region:us"
] |
token-classification
| 2025-09-04T06:13:12Z |
---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- c2p-cmd/FaceEmotionClassifier
new_version: openai/gpt-oss-120b
pipeline_tag: token-classification
library_name: adapter-transformers
tags:
- code
---
# T5 Command Description Generator
This project fine-tunes a T5 model (`t5-small`) to generate descriptions of terminal commands based on prompts in the format "Describe the command: {name} in {source}". The model is trained on a dataset (`all_commands.csv`) containing command names, descriptions, and sources (e.g., `cmd`, `linux`, `macos`, `vbscript`). After fine-tuning, the model can generate descriptions for commands, such as "List information about file(s)" for `ls` in `linux`.
## Table of Contents
- [Overview](#overview)
- [Dataset](#dataset)
- [Requirements](#requirements)
- [Setup](#setup)
- [Fine-Tuning the Model](#fine-tuning-the-model)
- [Using the Model](#using-the-model)
- [Example Output](#example-output)
- [Troubleshooting](#troubleshooting)
- [Future Improvements](#future-improvements)
## Overview
The T5 (Text-to-Text Transfer Transformer) model is fine-tuned to map prompts like "Describe the command: ls in linux" to descriptions like "List information about file(s)". The dataset used for training is `all_commands.csv`, which includes commands from various environments (`cmd`, `linux`, `macos`, `vbscript`). The fine-tuned model is saved to `./new_cmd_model` and can be used to generate command descriptions interactively or programmatically.
## Dataset
The dataset (`all_commands.csv`) contains the following columns:
- `name`: The command name (e.g., `ls`, `dir`, `chmod`, `MsgBox`).
- `description`: A brief description of what the command does (e.g., "List information about file(s)").
- `source`: The environment the command belongs to (`cmd`, `linux`, `macos`, `vbscript`).
Example entries:
```
name,description,source
ls,List information about file(s),linux
dir,Display a list of files and folders,cmd
chmod,Change access permissions,macos
MsgBox,Display a dialogue box message,vbscript
```
The dataset is split into 80% training and 20% validation sets for fine-tuning.
## Requirements
- Python 3.8+
- Libraries:
- `transformers`
- `torch`
- `sentencepiece`
- `datasets`
- CUDA-enabled GPU (optional, for faster training; `fp16=True` in the script enables mixed precision if available)
- Dataset file: `all_commands.csv` (place in the project directory)
Install dependencies:
```bash
pip install transformers torch sentencepiece datasets
```
## Setup
1. **Activate the Environment**:
Ensure you're in a Python environment with the required libraries. For example, using Conda:
```bash
conda activate safetensor_new
```
2. **Prepare the Dataset**:
Place `all_commands.csv` in the project directory (e.g., `C:\app\dataset`).
3. **Directory Structure**:
```
C:\app\dataset\
├── all_commands.csv
├── new_cmd_model\ (created after fine-tuning)
└── fine_tune_script.py
```
## Fine-Tuning the Model
The fine-tuning script (`fine_tune_script.py`) trains a `t5-small` model on the `all_commands.csv` dataset to generate command descriptions.
### Script Overview
- **Model**: `t5-small` (can be upgraded to `t5-base` for better performance).
- **Input Prompt**: "Describe the command: {name} in {source}" (e.g., "Describe the command: ls in linux").
- **Output**: The command’s description (e.g., "List information about file(s)").
- **Training Parameters**:
- Epochs: 3
- Learning rate: 5e-5
- Batch size: 8
- Output directory: `./new_cmd_model`
- Mixed precision training: Enabled if CUDA is available
### Running the Script
Save the following script as `fine_tune_script.py` and run it:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset
import torch
# Load model and tokenizer
model_name = "t5-small"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
# Load dataset
dataset = load_dataset("csv", data_files={"train": "all_commands.csv"})
dataset = dataset["train"].train_test_split(test_size=0.2)
dataset["validation"] = dataset["test"]
# Preprocess function
def preprocess_function(examples):
inputs = [f"Describe the command: {name} in {source}" for name, source in zip(examples["name"], examples["source"])]
targets = examples["description"]
model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding="max_length")
labels = tokenizer(targets, max_length=256, truncation=True, padding="max_length")
model_inputs["labels"] = labels["input_ids"]
return model_inputs
# Apply preprocessing
tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset["train"].column_names)
# Training arguments
training_args = TrainingArguments(
output_dir="./new_cmd_model",
evaluation_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
fp16=torch.cuda.is_available(),
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
)
# Train the model
trainer.train()
# Save the model and tokenizer
model.save_pretrained("./new_cmd_model")
tokenizer.save_pretrained("./new_cmd_model")
print("Fine-tuning complete. Model saved to './new_cmd_model'.")
```
Run the script:
```bash
python fine_tune_script.py
```
This will train the model and save it to `./new_cmd_model`.
## Using the Model
After fine-tuning, you can use the model to generate command descriptions with prompts like "Describe the command: {name} in {source}". Below is a script to load and use the model interactively or programmatically.
### Usage Script
Save the following as `use_t5_command_description.py`:
```python
import os
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
from datetime import datetime
# Define model path
model_path = "./new_cmd_model"
# Check if model directory exists
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model directory '{model_path}' not found.")
# Load the fine-tuned model and tokenizer
try:
model = T5ForConditionalGeneration.from_pretrained(model_path)
tokenizer = T5Tokenizer.from_pretrained(model_path, legacy=False)
print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Model and tokenizer loaded successfully.")
except Exception as e:
raise Exception(f"Error loading model or tokenizer: {str(e)}")
# Function to generate a command description
def generate_description(command, source, max_length=100):
prompt = f"Describe the command: {command} in {source}"
print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Input prompt: {prompt}")
inputs = tokenizer(prompt, return_tensors="pt", max_length=128, truncation=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = {key: value.to(device) for key, value in inputs.items()}
print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Using device: {device}")
try:
outputs = model.generate(
inputs["input_ids"],
max_length=max_length,
num_beams=4,
length_penalty=1.0,
early_stopping=True
)
description = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
if not description:
return "Warning: No description generated. Check if the command and source are valid."
return description
except Exception as e:
return f"Error generating description: {str(e)}"
# Example usage
test_commands = [
("ls", "linux"),
("dir", "cmd"),
("chmod", "macos"),
("MsgBox", "vbscript")
]
print("\nGenerated Descriptions:")
print("-" * 50)
for command, source in test_commands:
description = generate_description(command, source)
print(f"Command: {command} ({source})")
print(f"Description: {description}")
print("-" * 50)
# Interactive mode
print("\nInteractive Mode: Enter a command and source to get its description.")
print("Valid sources: cmd, linux, macos, vbscript")
print("Type 'exit' to quit.\n")
while True:
command = input("Enter command name (or 'exit' to quit): ").strip()
if command.lower() == "exit":
break
source = input("Enter source (e.g., cmd, linux, macos, vbscript): ").strip().lower()
valid_sources = ["cmd", "linux", "macos", "vbscript"]
if source not in valid_sources:
print(f"Invalid source. Please use one of: {', '.join(valid_sources)}")
continue
description = generate_description(command, source)
print(f"\nCommand: {command} ({source})")
print(f"Description: {description}")
print("-" * 50)
print("Exiting interactive mode.")
```
Run the script:
```bash
python use_t5_command_description.py
```
## Example Output
After fine-tuning and running the usage script, you should see output like:
```
[2025-09-04 11:50:00] Model and tokenizer loaded successfully.
Generated Descriptions:
--------------------------------------------------
[2025-09-04 11:50:01] Input prompt: Describe the command: ls in linux
[2025-09-04 11:50:01] Using device: cuda
Command: ls (linux)
Description: List information about file(s)
--------------------------------------------------
[2025-09-04 11:50:02] Input prompt: Describe the command: dir in cmd
[2025-09-04 11:50:02] Using device: cuda
Command: dir (cmd)
Description: Display a list of files and folders
--------------------------------------------------
[2025-09-04 11:50:03] Input prompt: Describe the command: chmod in macos
[2025-09-04 11:50:03] Using device: cuda
Command: chmod (macos)
Description: Change access permissions
--------------------------------------------------
[2025-09-04 11:50:04] Input prompt: Describe the command: MsgBox in vbscript
[2025-09-04 11:50:04] Using device: cuda
Command: MsgBox (vbscript)
Description: Display a dialogue box message
--------------------------------------------------
Interactive Mode: Enter a command and source to get its description.
Valid sources: cmd, linux, macos, vbscript
Type 'exit' to quit.
Enter command name (or 'exit' to quit): ping
Enter source (e.g., cmd, linux, macos, vbscript): linux
[2025-09-04 11:50:05] Input prompt: Describe the command: ping in linux
[2025-09-04 11:50:05] Using device: cuda
Command: ping (linux)
Description: Test a network connection
--------------------------------------------------
Enter command name (or 'exit' to quit): exit
Exiting interactive mode.
```
## Troubleshooting
- **Empty Descriptions**:
- Ensure `all_commands.csv` has valid entries with no missing descriptions.
- Increase `num_train_epochs` to 5–10 or use `t5-base` for better performance.
- Check training logs in `./new_cmd_model` for high loss values.
- **Model Loading Issues**:
- Verify the model saved correctly in `./new_cmd_model`.
- Try loading a checkpoint (e.g., `./new_cmd_model/checkpoint-XXX`) if issues persist.
- **Environment Errors**:
- Ensure dependencies are installed: `pip install transformers torch sentencepiece datasets`.
- For CUDA errors, ensure your GPU drivers are up-to-date or set `fp16=False` in the training script.
- **Deprecation Warning**:
- The script uses `evaluation_strategy`, which is deprecated. Update to `eval_strategy` in newer `transformers` versions:
```python
training_args = TrainingArguments(
output_dir="./new_cmd_model",
eval_strategy="epoch",
...
)
```
## Future Improvements
- **Augment Dataset**: Add more command descriptions or variations to improve generalization.
- **Use Larger Model**: Switch to `t5-base` for better accuracy (update `model_name` and retrain).
- **Extend Task**: Modify to generate commands from task descriptions (e.g., "List files in linux" → `ls`) by retraining with swapped inputs/outputs.
- **Command Execution**: Add functionality to execute generated commands (requires careful validation for security).
For questions about xAI’s API, visit [https://x.ai/api](https://x.ai/api).
[2025-09-04
|
100Pudoff/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_large_clam
|
100Pudoff
| 2025-09-06T06:25:42Z | 43 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am pensive_large_clam",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T09:12:04Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am pensive_large_clam
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kapitaka/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_meek_cheetah
|
Kapitaka
| 2025-09-06T06:25:35Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tawny meek cheetah",
"trl",
"genrl-swarm",
"I am tawny_meek_cheetah",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T17:08:56Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_meek_cheetah
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tawny meek cheetah
- trl
- genrl-swarm
- I am tawny_meek_cheetah
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_meek_cheetah
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kapitaka/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_meek_cheetah", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
0xgr3y/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_dextrous_caterpillar
|
0xgr3y
| 2025-09-06T06:25:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am burrowing_dextrous_caterpillar",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T09:06:53Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am burrowing_dextrous_caterpillar
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lecca157/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-knobby_fluffy_impala
|
lecca157
| 2025-09-06T06:25:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am knobby_fluffy_impala",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T03:46:03Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am knobby_fluffy_impala
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
okikripto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shiny_slender_gazelle
|
okikripto
| 2025-09-06T06:25:13Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shiny slender gazelle",
"trl",
"genrl-swarm",
"I am shiny_slender_gazelle",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-22T14:47:20Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shiny_slender_gazelle
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shiny slender gazelle
- trl
- genrl-swarm
- I am shiny_slender_gazelle
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shiny_slender_gazelle
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="okikripto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shiny_slender_gazelle", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.2
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
|
silverbenehi
| 2025-09-06T06:25:12Z | 36 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold running kangaroo",
"trl",
"genrl-swarm",
"I am bold_running_kangaroo",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T21:11:49Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold running kangaroo
- trl
- genrl-swarm
- I am bold_running_kangaroo
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Shnepsik/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-restless_fluffy_ocelot
|
Shnepsik
| 2025-09-06T06:25:11Z | 179 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am restless_fluffy_ocelot",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T14:11:45Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am restless_fluffy_ocelot
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cosmosistan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_patterned_jay
|
cosmosistan
| 2025-09-06T06:25:02Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am extinct patterned jay",
"trl",
"genrl-swarm",
"I am extinct_patterned_jay",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-28T22:33:52Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_patterned_jay
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am extinct patterned jay
- trl
- genrl-swarm
- I am extinct_patterned_jay
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_patterned_jay
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cosmosistan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_patterned_jay", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Avokado777/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_small_gibbon
|
Avokado777
| 2025-09-06T06:24:58Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fast small gibbon",
"trl",
"genrl-swarm",
"I am fast_small_gibbon",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-03T23:03:53Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_small_gibbon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fast small gibbon
- trl
- genrl-swarm
- I am fast_small_gibbon
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_small_gibbon
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Avokado777/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_small_gibbon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/corobov-mitya-individual/huggingface/runs/zcdsijaj)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
anatolijbatalko/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_ferocious_mink
|
anatolijbatalko
| 2025-09-06T06:24:54Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am thriving ferocious mink",
"trl",
"genrl-swarm",
"I am thriving_ferocious_mink",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-04T13:56:54Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_ferocious_mink
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am thriving ferocious mink
- trl
- genrl-swarm
- I am thriving_ferocious_mink
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_ferocious_mink
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="anatolijbatalko/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_ferocious_mink", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
elsvastika1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-unseen_burrowing_cassowary
|
elsvastika1
| 2025-09-06T06:24:53Z | 122 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am unseen_burrowing_cassowary",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T20:13:05Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am unseen_burrowing_cassowary
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
duppbuy/blockassist-bc-vocal_strong_okapi_1757139873
|
duppbuy
| 2025-09-06T06:24:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vocal strong okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T06:24:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vocal strong okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
565dfh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_squeaky_dog
|
565dfh
| 2025-09-06T06:24:44Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bipedal squeaky dog",
"trl",
"genrl-swarm",
"I am bipedal_squeaky_dog",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-20T18:26:31Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_squeaky_dog
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bipedal squeaky dog
- trl
- genrl-swarm
- I am bipedal_squeaky_dog
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_squeaky_dog
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="565dfh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_squeaky_dog", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
babycielou/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_thick_alpaca
|
babycielou
| 2025-09-06T06:24:37Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scampering thick alpaca",
"trl",
"genrl-swarm",
"I am scampering_thick_alpaca",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T20:59:53Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_thick_alpaca
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scampering thick alpaca
- trl
- genrl-swarm
- I am scampering_thick_alpaca
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_thick_alpaca
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="babycielou/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scampering_thick_alpaca", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AlexanderArtT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
|
AlexanderArtT
| 2025-09-06T06:24:33Z | 23 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tiny nimble warthog",
"trl",
"genrl-swarm",
"I am tiny_nimble_warthog",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-13T22:11:38Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tiny nimble warthog
- trl
- genrl-swarm
- I am tiny_nimble_warthog
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AlexanderArtT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Umbrellat/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_extinct_turtle
|
Umbrellat
| 2025-09-06T06:24:32Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shrewd extinct turtle",
"trl",
"genrl-swarm",
"I am shrewd_extinct_turtle",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-16T03:10:55Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_extinct_turtle
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shrewd extinct turtle
- trl
- genrl-swarm
- I am shrewd_extinct_turtle
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_extinct_turtle
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Umbrellat/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_extinct_turtle", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
EnriqueSolarte/qwen2.5-VL-7B-instruct-00004-VqCaAuuoeWk_0
|
EnriqueSolarte
| 2025-09-06T06:24:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T07:21:28Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen2.5-VL-7B-instruct-00004-VqCaAuuoeWk_0
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2.5-VL-7B-instruct-00004-VqCaAuuoeWk_0
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="EnriqueSolarte/qwen2.5-VL-7B-instruct-00004-VqCaAuuoeWk_0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
wacicu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_bristly_falcon
|
wacicu
| 2025-09-06T06:24:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am flightless_bristly_falcon",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T10:11:32Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am flightless_bristly_falcon
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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 [optional]
<!-- 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lecca157/Smoothie-Qwen3-1.7B-Gensyn-Swarm-knobby_fluffy_impala
|
lecca157
| 2025-09-06T06:24:25Z | 172 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am knobby_fluffy_impala",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T07:03:57Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am knobby_fluffy_impala
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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|
lecca157/AceInstruct-1.5B-Gensyn-Swarm-knobby_fluffy_impala
|
lecca157
| 2025-09-06T06:24:24Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am knobby_fluffy_impala",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T06:54:19Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am knobby_fluffy_impala
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [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. -->
[More Information 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 -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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 Dataset 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[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]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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|
1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion
|
1245erty
| 2025-09-06T06:24:12Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am jumping lithe scorpion",
"unsloth",
"trl",
"genrl-swarm",
"I am jumping_lithe_scorpion",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-20T16:38:45Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am jumping lithe scorpion
- unsloth
- trl
- genrl-swarm
- I am jumping_lithe_scorpion
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
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