modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Brendan/cse244b-hw2-roberta
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
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| 28 | null |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/bert-base-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 64.48
name: Eval EM
- type: f1
value: 76.14
name: Eval F1
- type: exact_match
value: 48.89
name: Test EM
- type: f1
value: 59.89
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** BERT-Base-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [BERT-Base-uncased](https://huggingface.co/bert-base-uncased)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 8:39:10 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/bert-base-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.9254004955291748,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"bert-base-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
Broadus20/DialoGPT-small-joshua
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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}
| 12 | 2023-02-17T20:46:18Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/bert-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 69.52
name: Eval EM
- type: f1
value: 80.50
name: Eval F1
- type: exact_match
value: 55.00
name: Test EM
- type: f1
value: 65.78
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** BERT-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [BERT-Large-uncased](https://huggingface.co/bert-large-uncased)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 28:35:38 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/bert-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.864973783493042,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"bert-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=8,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
|TinyRoBERTa | 81,529.346 | 4:27:06 *| 9:54 | 1:04 | 69.38 | 80.07| 53.29| 64.16|
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
Brona/model1
|
[] | null |
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| 0 | 2023-02-17T20:52:30Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/TinyRoBERTa-MRQA
results:
- task:
type: Question-Answering
dataset:
type: mrqa # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: mrqa # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: exact_match
value: 22.78
name: Eval EM
- type: f1
value: 32.42
name: Eval F1
- type: exact_match
value: 10.18
name: Test EM
- type: f1
value: 18.72
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** BERT-Tiny-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [BERT-Tiny-uncased](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 26:11 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/bert-tiny-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.134057879447937,
# 'start': 76,
# 'end': 80,
# 'answer': '2019'
# }
```
Yes, you read that correctly ... this model thinks MRQA is "2019". Look at its eval and test scores. A coin toss is more likely to get you a decent answer, haha.
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"bert-tiny-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
BrunoNogueira/DialoGPT-kungfupanda
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
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}
| 10 | 2023-02-17T20:56:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: Wav2vec2-large-arabic-no-diacs_V1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2vec2-large-arabic-no-diacs_V1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2894
- Wer: 0.2470
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6563 | 0.83 | 1000 | 0.5231 | 0.5077 |
| 0.4532 | 1.66 | 2000 | 0.3642 | 0.3588 |
| 0.2687 | 2.5 | 3000 | 0.3245 | 0.2920 |
| 0.1846 | 3.33 | 4000 | 0.3050 | 0.2640 |
| 0.1587 | 4.16 | 5000 | 0.2894 | 0.2470 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Bryan190/Aguy190
|
[] | null |
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| 0 | 2023-02-17T20:58:10Z |
---
license: creativeml-openrail-m
language:
- en
tags:
- embedding
- textual inversion
- isometric
- isometric dreams
---
# Isometric Dreams TEXTUAL INVERSION HUB
It won't let me upload a zip file or the text files, so this is the WHOLE LOT.
# coffee is nice: https://ko-fi.com/DUSKFALLcrew
Yea we're low on funds and need to keep up with the insanity of making things :3
# Invidiual files are on Civit
https://civitai.com/models/9064/isometric-ti-sets
https://civitai.com/models/9265/isometric-ti-sets-pt-2
https://civitai.com/models/9666/isometric-dreams-15-ti-set-3
https://civitai.com/models/9062/isometric-dreams-15-2000-ti
https://civitai.com/models/9060/isometric-ti-4000
https://civitai.com/models/9059/isometric-dreams-ti-4400
https://civitai.com/models/9058/isometricdreams-3900
https://civitai.com/models/8132/isometricdreams-300
|
Brykee/BrykeeBot
|
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| 0 | 2023-02-17T21:03:42Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/deberta-v3-xsmall-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 65.58
name: Eval EM
- type: f1
value: 77.17
name: Eval F1
- type: exact_match
value: 50.92
name: Test EM
- type: f1
value: 62.58
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** DeBERTa-v3-xsmall-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [DeBERTa-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 5:19:05 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/deberta-v3-xsmall-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.8907278776168823,
# 'start': 29,
# 'end': 68,
# 'answer': ' Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"deberta-v3-xsmall-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
Bryson575x/riceboi
|
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| 0 | 2023-02-17T21:04:04Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 223.70 +/- 73.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BumBelDumBel/TRUMP
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] |
text-generation
|
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| 5 | null |
Access to model s-1-n-t-h/bart-summarizer is restricted and you are not in the authorized list. Visit https://huggingface.co/s-1-n-t-h/bart-summarizer to ask for access.
|
BumBelDumBel/ZORK-AI-TEST
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] |
text-generation
|
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}
| 9 | null |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/deberta-v3-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 76.08
name: Eval EM
- type: f1
value: 86.23
name: Eval F1
- type: exact_match
value: 64.27
name: Test EM
- type: f1
value: 75.22
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** DeBERTa-v3-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [DeBERTa-v3-Large](https://huggingface.co/microsoft/deberta-v3-large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 38:36:13 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/deberta-v3-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.9785431623458862,
# 'start': 29,
# 'end': 68,
# 'answer': ' Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"deberta-v3-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=8,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
BumBelDumBel/ZORK_AI_FANTASY
|
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| 0 | 2023-02-17T21:11:00Z |
---
license: apache-2.0
language:
- bn
Tags:
- paraphrase
tags:
- bangla
- semantics
- paraphrase
---
|
CALM/backup
|
[
"lean_albert",
"transformers"
] | null |
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"num_beams": null,
"prefix": null
}
}
}
| 4 | 2023-02-17T21:20:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Yelinz/FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
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},
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},
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},
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}
}
}
| 85 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: gpt-neo-2.7B-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-2.7B-imdb
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
}
}
| 16,451 | null |
---
license: mit
---
### tim-sale1 on Stable Diffusion
This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 71 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 238.75 +/- 22.75
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 580 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.37 +/- 0.34
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 32 | 2023-02-17T21:38:08Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: menoua/ML-Agents-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 27 | 2023-02-17T21:40:58Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/minilmv2-l6-h384-from-bert-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 59.31
name: Eval EM
- type: f1
value: 71.09
name: Eval F1
- type: exact_match
value: 41.78
name: Test EM
- type: f1
value: 53.30
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** MiniLMv2-L6-H384-from-BERT-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [MiniLMv2-L6-H384-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 2:12:48 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/minilmv2-l6-h384-from-bert-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.30876627564430237,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"minilmv2-l6-h384-from-bert-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
CAMeL-Lab/bert-base-arabic-camelbert-da
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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}
}
}
| 449 | 2023-02-17T21:42:19Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/minilmv2-l6-h384-from-roberta-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 59.27
name: Eval EM
- type: f1
value: 70.64
name: Eval F1
- type: exact_match
value: 42.95
name: Test EM
- type: f1
value: 54.03
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** MiniLMv2-L6-H384-from-RoBERTa-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 2:15:10 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/minilmv2-l6-h384-from-roberta-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.5269668698310852,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"minilmv2-l6-h384-from-roberta-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 34 | 2023-02-17T21:43:36Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/minilmv2-l6-h768-from-bert-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 64.27
name: Eval EM
- type: f1
value: 75.84
name: Eval F1
- type: exact_match
value: 49.05
name: Test EM
- type: f1
value: 59.82
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** MiniLMv2-L6-H768-from-BERT-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [MiniLMv2-L6-H768-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 4:42:59 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/minilmv2-l6-h768-from-bert-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.8620206117630005,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"minilmv2-l6-h768-from-bert-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,860 | 2023-02-17T21:46:51Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.58 +/- 2.69
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="shaaaanya/taxi-v3-v2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 62 | 2023-02-17T21:48:07Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of airobots robot in the Acropolis
---
# DreamBooth model for the airobots concept trained by hulkster on the hulkster/airobotics dataset.
This is a Stable Diffusion model fine-tuned on the airobots concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of airobots robot**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `robot` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('hulkster/airobots-robot')
image = pipeline().images[0]
image
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,862 | 2023-02-17T21:49:55Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/roberta-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 74.08
name: Eval EM
- type: f1
value: 84.38
name: Eval F1
- type: exact_match
value: 62.20
name: Test EM
- type: f1
value: 72.88
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** RoBERTa-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [RoBERTa-Large](https://huggingface.co/roberta-large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 29:16:06 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/roberta-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.9120895862579346,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"roberta-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=8,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"min_length": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 21 | null |
---
datasets:
- relbert/relational_similarity
model-index:
- name: relbert/relbert-roberta-large-nce-d-semeval2012-t-rex
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8246428571428571
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6818181818181818
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6765578635014837
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7815453029460812
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.912
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6491228070175439
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6203703703703703
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3934563758389262
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6939890710382514
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6916666666666667
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9144191652855206
- name: F1 (macro)
type: f1_macro
value: 0.9090326046297017
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8525821596244132
- name: F1 (macro)
type: f1_macro
value: 0.6842287666651263
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6700975081256771
- name: F1 (macro)
type: f1_macro
value: 0.6583310456420314
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9554148987966892
- name: F1 (macro)
type: f1_macro
value: 0.8781071111856901
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8928235662801629
- name: F1 (macro)
type: f1_macro
value: 0.8921854652640585
---
# relbert/relbert-roberta-large-nce-d-semeval2012-t-rex
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/relational_similarity](https://huggingface.co/datasets/relbert/relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-t-rex/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6818181818181818
- Accuracy on SAT: 0.6765578635014837
- Accuracy on BATS: 0.7815453029460812
- Accuracy on U2: 0.6491228070175439
- Accuracy on U4: 0.6203703703703703
- Accuracy on Google: 0.912
- Accuracy on ConceptNet Analogy: 0.3934563758389262
- Accuracy on T-Rex Analogy: 0.6939890710382514
- Accuracy on NELL-ONE Analogy: 0.6916666666666667
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-t-rex/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9144191652855206
- Micro F1 score on CogALexV: 0.8525821596244132
- Micro F1 score on EVALution: 0.6700975081256771
- Micro F1 score on K&H+N: 0.9554148987966892
- Micro F1 score on ROOT09: 0.8928235662801629
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-t-rex/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8246428571428571
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-nce-d-semeval2012-t-rex")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 20
- batch: 64
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/relational_similarity
- data_name: semeval2012_relational_similarity.t_rex_relational_similarity
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 10}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012-t-rex/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 229 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -125.60 +/- 77.76
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'z4x/ppo-LunarLander-v2-CleanRL'
'batch_size': 512
'minibatch_size': 128}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
}
| 25 | 2023-02-17T22:16:31Z |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 21 | 2023-02-17T22:21:08Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.35 +/- 0.10
name: mean_reward
verified: false
---
# **SAC** Agent playing **PandaReachDense-v2**
This is a trained model of a **SAC** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12 | 2023-02-17T22:28:56Z |
---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 244.30 +/- 65.98
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **CartPole-v1**
This is a trained model of a DQN agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline_VIDEO.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQN_baseline_VIDEO]"
python -m cleanrl_utils.enjoy --exp-name DQN_baseline_VIDEO --env-id CartPole-v1
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_baseline_VIDEO-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_baseline_VIDEO-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_baseline_VIDEO-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --exp-name DQN_baseline_VIDEO --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id CartPole-v1 --seed 1 --total-timesteps 100000
```
# Hyperparameters
```python
{'batch_size': 128,
'buffer_size': 10000,
'capture_video': False,
'cuda': True,
'end_e': 0.05,
'env_id': 'CartPole-v1',
'exp_name': 'DQN_baseline_VIDEO',
'exploration_fraction': 0.5,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.00025,
'learning_starts': 10000,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 500,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 100000,
'track': True,
'train_frequency': 10,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
| 26 | 2023-02-17T22:41:47Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: parsasam/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAUKiel/JavaBERT
|
[
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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| 388 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/anthrophobe1/1676674313589/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1619867533640605697/rGD6NShu_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">.</div>
<div style="text-align: center; font-size: 14px;">@anthrophobe1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ..
| Data | . |
| --- | --- |
| Tweets downloaded | 1603 |
| Retweets | 247 |
| Short tweets | 128 |
| Tweets kept | 1228 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/v2ott2rf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @anthrophobe1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/o3d9xnm1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/o3d9xnm1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/anthrophobe1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CBreit00/DialoGPT_small_Rick
|
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| 0 | 2023-02-17T22:54:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CLAck/en-km
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
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| 12 | 2023-02-17T22:58:59Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: ArtYac/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-tiny-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
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| 5 | null |
Access to model CQTTS/NZ is restricted and you are not in the authorized list. Visit https://huggingface.co/CQTTS/NZ to ask for access.
|
dccuchile/albert-large-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
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| 75 | null |
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Medium.en - genevera
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium.en - genevera
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 100
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Chakita/KannadaBERT
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
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}
| 5 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -4.92 +/- 2.45
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Chan/distilroberta-base-finetuned-wikitext2
|
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| 0 | null |
---
language:
- en
metrics:
- f1
- accuracy
pipeline_tag: text-classification
widget:
- text: "Every woman wants to be a model. It's codeword for 'I get everything for free and people want me'"
---
### distilbert-base-sexism-detector
This is a fine-tuned model of distilbert-base on the Explainable Detection of Online Sexism (EDOS) dataset. It is intended to be used as a classification model for identifying tweets (0 - not sexist; 1 - sexist).
**This is a light model with an 81.2 F1 score. Use this model for fase prediction using the online API, if you like to see our best model with 86.3 F1 score , use this [link](https://huggingface.co/NLP-LTU/BERTweet-large-sexism-detector).**
Classification examples (use these example in the Hosted Inference API in the right panel ):
|Prediction|Tweet|
|-----|--------|
|sexist |Every woman wants to be a model. It's codeword for "I get everything for free and people want me" |
|not sexist |basically I placed more value on her than I should then?|
# More Details
For more details about the datasets and eval results, see (we will updated the page with our paper link)
# How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer,pipeline
import torch
model = AutoModelForSequenceClassification.from_pretrained('NLP-LTU/distilbert-sexism-detector')
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
prediction=classifier("Every woman wants to be a model. It's codeword for 'I get everything for free and people want me' ")
label_pred = 'not sexist' if prediction == 0 else 'sexist'
print(label_pred)
```
```
precision recall f1-score support
not sexsit 0.9000 0.9264 0.9130 3030
sexist 0.7469 0.6784 0.7110 970
accuracy 0.8662 4000
macro avg 0.8234 0.8024 0.8120 4000
weighted avg 0.8628 0.8662 0.8640 4000
```
|
ChauhanVipul/BERT
|
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| 0 | 2023-02-18T09:25:37Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Ahmade/bert-fine-tuned-cola2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Ahmade/bert-fine-tuned-cola2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3115
- Validation Loss: 0.4341
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5109 | 0.4635 | 0 |
| 0.3115 | 0.4341 | 1 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CheonggyeMountain-Sherpa/kogpt-trinity-poem
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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}
| 15 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.706732347314747
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
evaluating using gymnasium gives a weird score
value: 814.92 +/- 28.41
## Usage
```python
model = load_from_hub(repo_id="Yelinz/q-Taxi-v3-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ChrisVCB/DialoGPT-medium-cmjs
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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},
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}
| 7 | 2023-02-18T10:54:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.66 +/- 18.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ChristianOrr/madnet_keras
|
[
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
] |
depth-estimation
|
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}
}
| 0 | 2023-02-18T11:05:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="parsasam/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ChristopherA08/IndoELECTRA
|
[
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
] | null |
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"ElectraForPreTraining"
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}
| 4 | 2023-02-18T11:06:08Z |
# TorToiSe
Tortoise is a text-to-speech program built with the following priorities:
1. Strong multi-voice capabilities.
2. Highly realistic prosody and intonation.
This repo contains all the code needed to run Tortoise TTS in inference mode.
### New features
#### v2.1; 2022/5/2
- Added ability to produce totally random voices.
- Added ability to download voice conditioning latent via a script, and then use a user-provided conditioning latent.
- Added ability to use your own pretrained models.
- Refactored directory structures.
- Performance improvements & bug fixes.
## What's in a name?
I'm naming my speech-related repos after Mojave desert flora and fauna. Tortoise is a bit tongue in cheek: this model
is insanely slow. It leverages both an autoregressive decoder **and** a diffusion decoder; both known for their low
sampling rates. On a K80, expect to generate a medium sized sentence every 2 minutes.
## Demos
See [this page](http://nonint.com/static/tortoise_v2_examples.html) for a large list of example outputs.
## Usage guide
### Colab
Colab is the easiest way to try this out. I've put together a notebook you can use here:
https://colab.research.google.com/drive/1wVVqUPqwiDBUVeWWOUNglpGhU3hg_cbR?usp=sharing
### Installation
If you want to use this on your own computer, you must have an NVIDIA GPU. First, install pytorch using these
instructions: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
Then:
```shell
git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts
python setup.py install
```
### do_tts.py
This script allows you to speak a single phrase with one or more voices.
```shell
python tortoise/do_tts.py --text "I'm going to speak this" --voice random --preset fast
```
### read.py
This script provides tools for reading large amounts of text.
```shell
python tortoise/read.py --textfile <your text to be read> --voice random
```
This will break up the textfile into sentences, and then convert them to speech one at a time. It will output a series
of spoken clips as they are generated. Once all the clips are generated, it will combine them into a single file and
output that as well.
Sometimes Tortoise screws up an output. You can re-generate any bad clips by re-running `read.py` with the --regenerate
argument.
### API
Tortoise can be used programmatically, like so:
```python
reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech()
pcm_audio = tts.tts_with_preset("your text here", reference_clips, preset='fast')
```
## Voice customization guide
Tortoise was specifically trained to be a multi-speaker model. It accomplishes this by consulting reference clips.
These reference clips are recordings of a speaker that you provide to guide speech generation. These clips are used to determine many properties of the output, such as the pitch and tone of the voice, speaking speed, and even speaking defects like a lisp or stuttering. The reference clip is also used to determine non-voice related aspects of the audio output like volume, background noise, recording quality and reverb.
### Random voice
I've included a feature which randomly generates a voice. These voices don't actually exist and will be random every time you run
it. The results are quite fascinating and I recommend you play around with it!
You can use the random voice by passing in 'random' as the voice name. Tortoise will take care of the rest.
For the those in the ML space: this is created by projecting a random vector onto the voice conditioning latent space.
### Provided voices
This repo comes with several pre-packaged voices. You will be familiar with many of them. :)
Most of the provided voices were not found in the training set. Experimentally, it seems that voices from the training set
produce more realistic outputs then those outside of the training set. Any voice prepended with "train" came from the
training set.
### Adding a new voice
To add new voices to Tortoise, you will need to do the following:
1. Gather audio clips of your speaker(s). Good sources are YouTube interviews (you can use youtube-dl to fetch the audio), audiobooks or podcasts. Guidelines for good clips are in the next section.
2. Cut your clips into ~10 second segments. You want at least 3 clips. More is better, but I only experimented with up to 5 in my testing.
3. Save the clips as a WAV file with floating point format and a 22,050 sample rate.
4. Create a subdirectory in voices/
5. Put your clips in that subdirectory.
6. Run tortoise utilities with --voice=<your_subdirectory_name>.
### Picking good reference clips
As mentioned above, your reference clips have a profound impact on the output of Tortoise. Following are some tips for picking
good clips:
1. Avoid clips with background music, noise or reverb. These clips were removed from the training dataset. Tortoise is unlikely to do well with them.
2. Avoid speeches. These generally have distortion caused by the amplification system.
3. Avoid clips from phone calls.
4. Avoid clips that have excessive stuttering, stammering or words like "uh" or "like" in them.
5. Try to find clips that are spoken in such a way as you wish your output to sound like. For example, if you want to hear your target voice read an audiobook, try to find clips of them reading a book.
6. The text being spoken in the clips does not matter, but diverse text does seem to perform better.
## Advanced Usage
### Generation settings
Tortoise is primarily an autoregressive decoder model combined with a diffusion model. Both of these have a lot of knobs
that can be turned that I've abstracted away for the sake of ease of use. I did this by generating thousands of clips using
various permutations of the settings and using a metric for voice realism and intelligibility to measure their effects. I've
set the defaults to the best overall settings I was able to find. For specific use-cases, it might be effective to play with
these settings (and it's very likely that I missed something!)
These settings are not available in the normal scripts packaged with Tortoise. They are available, however, in the API. See
```api.tts``` for a full list.
### Prompt engineering
Some people have discovered that it is possible to do prompt engineering with Tortoise! For example, you can evoke emotion
by including things like "I am really sad," before your text. I've built an automated redaction system that you can use to
take advantage of this. It works by attempting to redact any text in the prompt surrounded by brackets. For example, the
prompt "\[I am really sad,\] Please feed me." will only speak the words "Please feed me" (with a sad tonality).
### Playing with the voice latent
Tortoise ingests reference clips by feeding them through individually through a small submodel that produces a point latent,
then taking the mean of all of the produced latents. The experimentation I have done has indicated that these point latents
are quite expressive, affecting everything from tone to speaking rate to speech abnormalities.
This lends itself to some neat tricks. For example, you can combine feed two different voices to tortoise and it will output
what it thinks the "average" of those two voices sounds like.
#### Generating conditioning latents from voices
Use the script `get_conditioning_latents.py` to extract conditioning latents for a voice you have installed. This script
will dump the latents to a .pth pickle file. The file will contain a single tuple, (autoregressive_latent, diffusion_latent).
Alternatively, use the api.TextToSpeech.get_conditioning_latents() to fetch the latents.
#### Using raw conditioning latents to generate speech
After you've played with them, you can use them to generate speech by creating a subdirectory in voices/ with a single
".pth" file containing the pickled conditioning latents as a tuple (autoregressive_latent, diffusion_latent).
### Send me feedback!
Probabilistic models like Tortoise are best thought of as an "augmented search" - in this case, through the space of possible
utterances of a specific string of text. The impact of community involvement in perusing these spaces (such as is being done with
GPT-3 or CLIP) has really surprised me. If you find something neat that you can do with Tortoise that isn't documented here,
please report it to me! I would be glad to publish it to this page.
## Tortoise-detect
Out of concerns that this model might be misused, I've built a classifier that tells the likelihood that an audio clip
came from Tortoise.
This classifier can be run on any computer, usage is as follows:
```commandline
python tortoise/is_this_from_tortoise.py --clip=<path_to_suspicious_audio_file>
```
This model has 100% accuracy on the contents of the results/ and voices/ folders in this repo. Still, treat this classifier
as a "strong signal". Classifiers can be fooled and it is likewise not impossible for this classifier to exhibit false
positives.
## Model architecture
Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data and using a better decoder. It is made up of 5 separate
models that work together. I've assembled a write-up of the system architecture here:
[https://nonint.com/2022/04/25/tortoise-architectural-design-doc/](https://nonint.com/2022/04/25/tortoise-architectural-design-doc/)
## Training
These models were trained on my "homelab" server with 8 RTX 3090s over the course of several months. They were trained on a dataset consisting of
~50k hours of speech data, most of which was transcribed by [ocotillo](http://www.github.com/neonbjb/ocotillo). Training was done on my own
[DLAS](https://github.com/neonbjb/DL-Art-School) trainer.
I currently do not have plans to release the training configurations or methodology. See the next section..
## Ethical Considerations
Tortoise v2 works considerably better than I had planned. When I began hearing some of the outputs of the last few versions, I began
wondering whether or not I had an ethically unsound project on my hands. The ways in which a voice-cloning text-to-speech system
could be misused are many. It doesn't take much creativity to think up how.
After some thought, I have decided to go forward with releasing this. Following are the reasons for this choice:
1. It is primarily good at reading books and speaking poetry. Other forms of speech do not work well.
2. It was trained on a dataset which does not have the voices of public figures. While it will attempt to mimic these voices if they are provided as references, it does not do so in such a way that most humans would be fooled.
3. The above points could likely be resolved by scaling up the model and the dataset. For this reason, I am currently withholding details on how I trained the model, pending community feedback.
4. I am releasing a separate classifier model which will tell you whether a given audio clip was generated by Tortoise or not. See `tortoise-detect` above.
5. If I, a tinkerer with a BS in computer science with a ~$15k computer can build this, then any motivated corporation or state can as well. I would prefer that it be in the open and everyone know the kinds of things ML can do.
### Diversity
The diversity expressed by ML models is strongly tied to the datasets they were trained on.
Tortoise was trained primarily on a dataset consisting of audiobooks. I made no effort to
balance diversity in this dataset. For this reason, Tortoise will be particularly poor at generating the voices of minorities
or of people who speak with strong accents.
## Looking forward
Tortoise v2 is about as good as I think I can do in the TTS world with the resources I have access to. A phenomenon that happens when
training very large models is that as parameter count increases, the communication bandwidth needed to support distributed training
of the model increases multiplicatively. On enterprise-grade hardware, this is not an issue: GPUs are attached together with
exceptionally wide buses that can accommodate this bandwidth. I cannot afford enterprise hardware, though, so I am stuck.
I want to mention here
that I think Tortoise could do be a **lot** better. The three major components of Tortoise are either vanilla Transformer Encoder stacks
or Decoder stacks. Both of these types of models have a rich experimental history with scaling in the NLP realm. I see no reason
to believe that the same is not true of TTS.
The largest model in Tortoise v2 is considerably smaller than GPT-2 large. It is 20x smaller that the original DALLE transformer.
Imagine what a TTS model trained at or near GPT-3 or DALLE scale could achieve.
If you are an ethical organization with computational resources to spare interested in seeing what this model could do
if properly scaled out, please reach out to me! I would love to collaborate on this.
## Acknowledgements
This project has garnered more praise than I expected. I am standing on the shoulders of giants, though, and I want to
credit a few of the amazing folks in the community that have helped make this happen:
- Hugging Face, who wrote the GPT model and the generate API used by Tortoise, and who hosts the model weights.
- [Ramesh et al](https://arxiv.org/pdf/2102.12092.pdf) who authored the DALLE paper, which is the inspiration behind Tortoise.
- [Nichol and Dhariwal](https://arxiv.org/pdf/2102.09672.pdf) who authored the (revision of) the code that drives the diffusion model.
- [Jang et al](https://arxiv.org/pdf/2106.07889.pdf) who developed and open-sourced univnet, the vocoder this repo uses.
- [lucidrains](https://github.com/lucidrains) who writes awesome open source pytorch models, many of which are used here.
- [Patrick von Platen](https://huggingface.co/patrickvonplaten) whose guides on setting up wav2vec were invaluable to building my dataset.
## Notice
Tortoise was built entirely by me using my own hardware. My employer was not involved in any facet of Tortoise's development.
If you use this repo or the ideas therein for your research, please cite it! A bibtex entree can be found in the right pane on GitHub.
|
Chuah/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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},
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},
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},
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},
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}
}
}
| 9 | 2023-02-18T11:07:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="parsasam/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Chun/DialoGPT-large-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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},
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}
}
}
| 6 | 2023-02-18T11:12:17Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-stocks-event-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-stocks-event-all
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6143
- Rouge1: 0.4336
- Rouge2: 0.3906
- Rougel: 0.4328
- Rougelsum: 0.4317
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 6.4821 | 1.0 | 97 | 2.0502 | 0.1648 | 0.0891 | 0.1587 | 0.1588 |
| 2.3093 | 2.0 | 194 | 0.9278 | 0.3059 | 0.2526 | 0.3063 | 0.3048 |
| 1.5634 | 3.0 | 291 | 0.7646 | 0.3348 | 0.2917 | 0.3334 | 0.3334 |
| 1.2392 | 4.0 | 388 | 0.7398 | 0.3721 | 0.3193 | 0.3725 | 0.3721 |
| 1.1755 | 5.0 | 485 | 0.6872 | 0.3498 | 0.2990 | 0.3496 | 0.3507 |
| 1.0981 | 6.0 | 582 | 0.6685 | 0.3579 | 0.3256 | 0.3591 | 0.3566 |
| 1.0238 | 7.0 | 679 | 0.6379 | 0.4261 | 0.3882 | 0.4278 | 0.4253 |
| 1.0265 | 8.0 | 776 | 0.6143 | 0.4336 | 0.3906 | 0.4328 | 0.4317 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Chun/DialoGPT-small-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
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| 10 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: ZhihongDeng/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ClaudeYang/awesome_fb_model
|
[
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] |
zero-shot-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
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"max_length": null
},
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},
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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}
}
}
| 26 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jondister/Soccer_JD
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CodeMonkey98/distilroberta-base-finetuned-wikitext2
|
[] | null |
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}
| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.70 +/- 27.76
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
CoffeeAddict93/gpt1-modest-proposal
|
[
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
"task_specific_params": {
"conversational": {
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| 11 | null |
### Safety label classifier
A model trained on []() to classify prompts to different safety labels. For more information refer [here]()
### Run Model
```python
from torch.utils.data import Dataset
class ProSocialDataset(Dataset):
def __init__(self,split):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(MODEL)
self.sep_token = self.tokenizer.sep_token
self.dataset = dataset[split]
self.label2id = {"__casual__":0,"__needs_caution__":1,"__needs_intervention__":2,"__probably_needs_caution__":3,"__possibly_needs_caution__":4}
self.id2label = {v:k for k,v in label_to_id.items()}
def __len__(self):
return len(self.dataset)
def __getitem__(self,idx):
context = self.dataset[idx]
idx_start = idx
end = self.dataset[max(0,idx_start-1)]["episode_done"]
while ((not end) and (idx_start>0)):
end = self.dataset[max(0,idx_start-2)]["episode_done"]
idx_start -= 1
idx_start = max(0,idx_start)
prev_context = [f'{self.dataset[i]["context"]}' for i in range(idx_start,idx)]
rots = self.dataset[idx]["rots"]
context = f'{self.dataset[idx]["context"]}' + self.sep_token + "".join(prev_context) + self.sep_token + "".join(rots)
encoding = self.tokenizer(
context,
max_length=MAXLEN,
add_special_tokens=True,
truncation=True,
padding='max_length')
encoding["labels"] = self.label2id[self.dataset[idx]["safety_label"]]
return encoding
model = AutoModelForSequenceClassification.from_pretrained("shahules786/prosocial-classifier")
pro_social_data = load_dataset("allenai/prosocial-dialog")
dataset = ProSocialDataset(split="test")
for item in dataset:
pred = model(**item).logits
```
### Citations
```
@inproceedings{
kim2022prosocialdialog,
title={ProsocialDialog: A Prosocial Backbone for Conversational Agents},
author={Hyunwoo Kim and Youngjae Yu and Liwei Jiang and Ximing Lu and Daniel Khashabi and Gunhee Kim and Yejin Choi and Maarten Sap},
booktitle={EMNLP},
year=2022
}
```
|
CoffeeAddict93/gpt2-medium-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
| 14 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -26.69 +/- 86.05
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 2000000
'learning_rate': 0.00025
'num_envs': 16
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'saikiranp/ppo-LunarLandr-v2-CleanRL'
'batch_size': 16384
'minibatch_size': 4096}
```
|
CogComp/bart-faithful-summary-detector
|
[
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
| 234 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: ibadrehman/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ComCom/gpt2-medium
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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}
| 5 | null |
---
license: gpl-3.0
language:
- en
- zh
pipeline_tag: image-to-image
tags:
- art
- image generation
- live3d
- anime
---
# Live3D v2.2 (AttNR) [Windows Bundle ](https://huggingface.co/transpchan/Live3D-v2-windowsbundle/resolve/main/Live3D.zip) | [一键启动Windows懒人包](https://huggingface.co/transpchan/Live3D-v2-windowsbundle/resolve/main/Live3D.zip)
[](https://transpchan.github.io/live3d)
### This is only a mirror, not the original repo! Please go to [Github](https://github.com/transpchan/Live3D-v2) for latest updates.
Neural Rendering with Attention: An Incremental Improvement for Anime Character Animation
[](https://colab.research.google.com/github/transpchan/Live3D-v2/blob/main/notebook.ipynb)
[](https://doi.org/10.5281/zenodo.7652719)
[](https://huggingface.co/transpchan/Live3D-v2-windowsbundle/resolve/main/Live3D.zip)
[](https://github.com/transpchan/Live3D-v2/)
[Discord](https://discord.gg/Md3cykbn36) |
[Twitter](https://twitter.com/transpchan) |
[Bilibili](https://space.bilibili.com/6418569) |
[Zhihu](https://zhuanlan.zhihu.com/p/565391665)
[](https://huggingface.co/transpchan/Live3D-v2-windowsbundle/resolve/main/Live3D.zip)
|
ComCom/gpt2
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"do_sample": true,
"max_length": 50
},
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},
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}
| 1 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 520.50 +/- 206.17
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brand25 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brand25 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga brand25
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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},
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},
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| 1 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: ibadrehman/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CouchCat/ma_sa_v7_distil
|
[
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
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}
}
}
| 38 | null |
---
tags:
- conversational
---
# My Awesome Model
|
Coyotl/DialoGPT-test-last-arthurmorgan
|
[
"conversational"
] |
conversational
|
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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"max_length": null,
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}
}
}
| 0 | null |
---
tags:
- conversational
---
# My Awesome Model
|
Crasher222/kaggle-comp-test
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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}
}
}
| 29 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: nlpbook_distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.929
- name: F1
type: f1
value: 0.9290966283051001
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nlpbook_distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2091
- Accuracy: 0.929
- F1: 0.9291
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8218 | 1.0 | 250 | 0.3043 | 0.9085 | 0.9055 |
| 0.2416 | 2.0 | 500 | 0.2091 | 0.929 | 0.9291 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3
|
CrayonShinchan/bart_fine_tune_test
|
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| 0 | null |
---
license: creativeml-openrail-m
---
Original model is here https://civitai.com/models/10106/merongmix
|
CrayonShinchan/fine_tune_try_1
|
[] | null |
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| 0 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -180.12 +/- 71.93
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
Culmenus/XLMR-ENIS-finetuned-ner
|
[
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
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"XLMRobertaForTokenClassification"
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}
| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Gholamreza/pquad
model-index:
- name: distilbert-fa-zwnj-base-finetuned-pquad
results: []
language:
- fa
library_name: transformers
pipeline_tag: question-answering
widget:
- text: اسم من چیست؟
context: >-
من، غلامرضا دار، 23 ساله از بندرعباس هستم. هم اکنون در دانشگاه امیرکبیر مشغول به تحصیل در رشته هوش مصنوعی می باشم.
example_title: "اسم"
- text: غلامرضا چند سال دارد؟
context: >-
من، غلامرضا دار، 23 ساله از بندرعباس هستم. هم اکنون در دانشگاه امیرکبیر مشغول به تحصیل در رشته هوش مصنوعی می باشم.
example_title: "سن"
- text: نام خانوادگی غلامرضا چیست؟
context: >-
من، غلامرضا دار، 23 ساله از بندرعباس هستم. هم اکنون در دانشگاه امیرکبیر مشغول به تحصیل در رشته هوش مصنوعی می باشم.
example_title: "نام خانوادگی"
- text: غلامرصا در چه دانشگاهی تحصیل میکند؟
context: >-
من، غلامرضا دار، 23 ساله از بندرعباس هستم. هم اکنون در دانشگاه امیرکبیر مشغول به تحصیل در رشته هوش مصنوعی می باشم.
example_title: "دانشگاه"
metrics:
- f1
- exact_match
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-fa-zwnj-base-finetuned-pquad
This model is a fine-tuned version of [HooshvareLab/distilbert-fa-zwnj-base](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base) on the pquad dataset.
## Results
### Test set
| name | value |
----- | ----- |
'exact' | 66.38340414896275,
'f1'| 80.23760220987583,
'total'| 8002,
'HasAns_exact'| 60.13469119579501,
'HasAns_f1'| 78.34449620292781,
'HasAns_total'| 6088,
'NoAns_exact'| 86.25914315569489,
'NoAns_f1'| 86.25914315569489,
'NoAns_total'| 1914,
'best_exact'| 66.38340414896275,
'best_exact_thresh'| 0.0,
'best_f1'| 80.23760220987589,
'best_f1_thresh'| 0.0
### Validation set
| name | value |
----- | ----- |
'exact'| 64.65646940822468,
'f1'| 78.88641788270802,
'total'| 7976,
'HasAns_exact'| 57.54795663052544,
'HasAns_f1'| 76.4800782372771,
'HasAns_total'| 5995,
'NoAns_exact'| 86.16860171630489,
'NoAns_f1'| 86.16860171630489,
'NoAns_total'| 1981,
'best_exact'| 64.65646940822468,
'best_exact_thresh'| 0.0,
'best_f1'| 78.88641788270819,
'best_f1_thresh'| 0.0
## Model description
uses [distilbert-fa-zwnj-base](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base) as base and fine-tunes it on [pquad](https://huggingface.co/datasets/Gholamreza/pquad) dataset.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1299 | 1.0 | 4003 | 1.1306 |
| 0.845 | 2.0 | 8006 | 1.0839 |
| 0.639 | 3.0 | 12009 | 1.1302 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2
|
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| 0 | null |
---
datasets:
- relbert/relational_similarity
model-index:
- name: relbert/relbert-roberta-large-nce-c-semeval2012-nell
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8470634920634921
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6898395721925134
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6973293768545994
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8243468593663146
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.944
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6929824561403509
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6597222222222222
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.436241610738255
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7103825136612022
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8066666666666666
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9177339159258702
- name: F1 (macro)
type: f1_macro
value: 0.9136940646765112
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8781690140845071
- name: F1 (macro)
type: f1_macro
value: 0.7339306967191377
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.704225352112676
- name: F1 (macro)
type: f1_macro
value: 0.6885755780161168
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9567364540585658
- name: F1 (macro)
type: f1_macro
value: 0.8700344787938971
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9166405515512378
- name: F1 (macro)
type: f1_macro
value: 0.9144585254310948
---
# relbert/relbert-roberta-large-nce-c-semeval2012-nell
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/relational_similarity](https://huggingface.co/datasets/relbert/relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-c-semeval2012-nell/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6898395721925134
- Accuracy on SAT: 0.6973293768545994
- Accuracy on BATS: 0.8243468593663146
- Accuracy on U2: 0.6929824561403509
- Accuracy on U4: 0.6597222222222222
- Accuracy on Google: 0.944
- Accuracy on ConceptNet Analogy: 0.436241610738255
- Accuracy on T-Rex Analogy: 0.7103825136612022
- Accuracy on NELL-ONE Analogy: 0.8066666666666666
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-c-semeval2012-nell/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9177339159258702
- Micro F1 score on CogALexV: 0.8781690140845071
- Micro F1 score on EVALution: 0.704225352112676
- Micro F1 score on K&H+N: 0.9567364540585658
- Micro F1 score on ROOT09: 0.9166405515512378
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-c-semeval2012-nell/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8470634920634921
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-nce-c-semeval2012-nell")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 20
- batch: 64
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/relational_similarity
- data_name: nell_relational_similarity.semeval2012_relational_similarity
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 10}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-c-semeval2012-nell/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Culmenus/opus-mt-de-is-finetuned-de-to-is
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
],
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| 1 | null |
---
datasets:
- lambdalabs/pokemon-blip-captions
pipeline_tag: unconditional-image-generation
tags:
- Image Generation
- Diffusers
---
|
CurtisBowser/DialoGPT-small-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
| 7 | 2023-02-18T16:32:05Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 236.96 +/- 76.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave
|
[] | null |
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| 11 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: dgodderis/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
D3vil/DialoGPT-smaall-harrypottery
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.40 +/- 20.38
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
D3xter1922/distilbert-base-uncased-finetuned-cola
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 607.00 +/- 169.87
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rdesarz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rdesarz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rdesarz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
D3xter1922/electra-base-discriminator-finetuned-mnli
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-v2-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9891
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.867 | 1.0 | 5540 | 0.8997 |
| 0.6439 | 2.0 | 11080 | 0.8932 |
| 0.4669 | 3.0 | 16620 | 0.9891 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D4RL1NG/yes
|
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| 0 | null |
---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
---
|
DARKVIP3R/DialoGPT-medium-Anakin
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 13 | null |
---
license: creativeml-openrail-m
thumbnail: >-
https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00031-1769428138-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20hands%20on%20ow.png
tags:
- stable-diffusion
- text-to-image
- safetensors
- diffusers
inference: true
language:
- en
widget:
- text: >-
masterpiece, best quality, 1girl, brown hair, green eyes, colorful,
autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
example_title: example 1girl
- text: >-
masterpiece, best quality, 1boy, medium hair, blonde hair, blue eyes,
bishounen, colorful, autumn, cumulonimbus clouds, lighting, blue sky,
falling leaves, garden
example_title: example 1boy
library_name: diffusers
---
## Introducing SomethingV2.2, An updated version of this model, can be found [here](https://huggingface.co/NoCrypt/SomethingV2_2)
---
[](https://huggingface.co/spaces/NoCrypt/SomethingV2)
<center><img src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/banner.webp" width="95%"/></center>
<center><h1><b>SomethingV2</b></h1></center>
<p align="center">Welcome to SomethingV2 - an anime latent diffusion model. This model is intended to produce vibrant but soft anime style images. </p>
## Recommended Settings
- VAE: None (Baked in model)
- Clip Skip: 2
- Sampler: DPM++ 2M Karras
- CFG Scale: 7 - 12
- Negative Prompt: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative)
- For better results, using hires fix is a must.
- Hires upscaler: Latent (any variant, such as nearest-exact)
- Resolution: At least 512x512 first pass, upscale up to 1500x1500
## Example
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00090-1829045217-masterpiece%20best%20quality%20hatsune%20miku%201girl%20white%20shirt%20blue%20necktie%20bare%20shoulders%20very%20detailed%20background%20hands%20on%20ow2473e4832c888be11494dab007c390c19c5b2f7d.png" width="32%"/>
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00022-1769428138-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20hands%20on%20ow.png" width="32%"/>
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00098-3514023396-masterpiece%2C%20best%20quality%2C%20hatsune%20miku%2C%201girl%2C%20white%20shirt%2C%20blue%20necktie%2C%20bare%20shoulders%2C%20very%20detailed%20background%2C%20cafe%2C%20angry.png" width="32%"/>
<details><summary><big><b>Prompts</b></big></summary>
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, hands on own cheeks, open mouth, one eye closed, clenched teeth, smile
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1829045217, Size: 456x592, Model: somethingv2_1, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, hands on own cheeks, open mouth, eyez closed, clenched teeth, smile, arms behind back,
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1769428138, Size: 456x592, Model: somethingv2_1, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
```yaml
masterpiece, best quality, hatsune miku, 1girl, white shirt, blue necktie, bare shoulders, very detailed background, cafe, angry, crossed arms, detached sleeves, light particles,
Negative prompt: EasyNegative, tattoo, (shoulder tattoo:1.0), (number tattoo:1.3), frills
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3514023396, Size: 456x592, Model: somethingv2_1, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 12, Hires upscaler: Latent (nearest-exact), Discard penultimate sigma: True
```
</details>
## FAQ
### Model differences?
<details><summary><big><b>Prompts</b></big></summary>
```yaml
masterpiece, hatsune miku, white shirt, blue necktie, bare shoulders, detached sleeves,
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 4163886333, Size: 440x592, Model: -, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 13, Hires upscaler: Latent (nearest-exact)
```
</details>
### Why all examples is miku?
Because I love miku. But here's other subjects
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00018-4018636341-masterpiece%2C%20best%20quality%2C%201girl%2C%20aqua%20eyes%2C%20baseball%20cap%2C%20blonde%20hair%2C%20closed%20mouth%2C%20earrings%2C%20green%20background%2C%20hat%2C%20hoop%20earr.png" width="49%"/>
<img style="display:inline;margin:0;padding:0;" src="https://huggingface.co/NoCrypt/SomethingV2/resolve/main/imgs/00019-1334620477-masterpiece%2C%20best%20quality%2C%20landscape.png" width="49%"/>
<details><summary><big><b>Prompts</b></big></summary>
```yaml
masterpiece, best quality, 1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 4018636341, Size: 440x592, Model: somethingv2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 13, Hires upscaler: Latent (nearest-exact)
```
```yaml
masterpiece, best quality, landscape
Negative prompt: EasyNegative
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1334620477, Size: 440x592, Model: somethingv2, Denoising strength: 0.53, Clip skip: 2, ENSD: 31337, Hires upscale: 1.65, Hires steps: 13, Hires upscaler: Latent (nearest-exact)
```
</details>
|
DCU-NLP/bert-base-irish-cased-v1
|
[
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
fill-mask
|
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| 1,244 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="JUNGU/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DCU-NLP/electra-base-irish-cased-discriminator-v1
|
[
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
] | null |
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"ElectraForPreTraining"
],
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| 4 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jackmedda/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DCU-NLP/electra-base-irish-cased-generator-v1
|
[
"pytorch",
"electra",
"fill-mask",
"ga",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 7 | 2023-02-18T17:03:24Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="JUNGU/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DHBaek/xlm-roberta-large-korquad-mask
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"XLMRobertaForQuestionAnswering"
],
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}
| 9 | null |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pong-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
DJStomp/TestingSalvoNET
|
[
"transformers"
] | null |
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}
| 1 | 2023-02-18T17:15:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_3
This model is a fine-tuned version of [SajjadAyoubi/xlm-roberta-large-fa-qa](https://huggingface.co/SajjadAyoubi/xlm-roberta-large-fa-qa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4424 | 1.0 | 1500 | 2.0999 |
| 1.8186 | 2.0 | 3000 | 1.2042 |
| 1.2822 | 3.0 | 4500 | 0.8894 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DKpro000/DialoGPT-small-harrypotter
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- Summarization
- generated_from_trainer
datasets:
- samsum
model-index:
- name: conversation-summ_longformer_bart_like
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# conversation-summ_longformer_bart_like
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|
| No log | 1.0 | 143 | 1.9126 | 42.0973 | 16.7856 | 33.784 | 37.7811 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Daltcamalea01/Camaleaodalt
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: loso_F04
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# loso_F04
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0791
- Wer: 1.4780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.9264 | 0.96 | 500 | 3.6742 | 1.0 |
| 2.6962 | 1.91 | 1000 | 1.7830 | 2.6233 |
| 1.1118 | 2.87 | 1500 | 0.5233 | 1.8458 |
| 0.3692 | 3.82 | 2000 | 0.1670 | 1.2423 |
| 0.1671 | 4.78 | 2500 | 0.1289 | 1.3700 |
| 0.0897 | 5.74 | 3000 | 0.1031 | 1.5110 |
| 0.0656 | 6.69 | 3500 | 0.0791 | 1.4780 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
|
DannyMichael/ECU911
|
[] | null |
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| 0 | 2023-02-18T18:22:33Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="seungwoos/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Davlan/bert-base-multilingual-cased-finetuned-igbo
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 15 | null |
Found. Redirecting to https://cdn-lfs.huggingface.co/repos/46/da/46daedf26dde3e0e117097544d3633a78b2cc26bbffccc5ce16e33dd5104c9c6/4eb72f9e6bdbfcf359ad66cdd7e26984449f47692069f81205f2217c9a821ba2?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1685106682&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzQ2L2RhLzQ2ZGFlZGYyNmRkZTNlMGUxMTcwOTc1NDRkMzYzM2E3OGIyY2MyNmJiZmZjY2M1Y2UxNmUzM2RkNTEwNGM5YzYvNGViNzJmOWU2YmRiZmNmMzU5YWQ2NmNkZDdlMjY5ODQ0NDlmNDc2OTIwNjlmODEyMDVmMjIxN2M5YTgyMWJhMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2ODUxMDY2ODJ9fX1dfQ__&Signature=hULjqmo7ppFrYP4MMPU92-mEUyrOl4f73y7X5sTgGrFcWkE3HoGlskALFBS5SiPGKMsIASKarzKT-optDNOz9u6rxhUyHw54XkQVX5qJc9p7ncfNxeK-hQvraX9pf033OSXPLsvlAZJ9inYlzxHfjfwGFNOAB3aNcdd7fFDmJ96qhFBvRPHkHpeqGszHh8c31t%7Engy28d5gJxkeEWbSSLxZDfwfTnx-PDfR73Pi6QjLhkdNlA8vfEn6KLvC507FsptoFTrG0Z6B8y8vt4L6rJvARc3oqyEjOIDxPw2hNSrfonlK-0nLsqszSi0ViRD1d4xtA2vuAQQQivzpfMC2fTQ__&Key-Pair-Id=KVTP0A1DKRTAX
|
Davlan/bert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 269,898 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LucaReggiani/t5-small-nlpfinalproject4-xsum
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# LucaReggiani/t5-small-nlpfinalproject4-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0688
- Validation Loss: 2.9609
- Train Rouge1: 22.9985
- Train Rouge2: 5.0413
- Train Rougel: 18.1856
- Train Rougelsum: 18.0816
- Train Gen Len: 18.67
- Epoch: 8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8921 | 3.2708 | 18.8870 | 3.0920 | 14.9668 | 14.9517 | 18.67 | 0 |
| 3.5034 | 3.1209 | 21.5417 | 3.8130 | 16.5211 | 16.5045 | 18.37 | 1 |
| 3.3763 | 3.0605 | 21.0710 | 3.6133 | 15.7808 | 15.7437 | 18.33 | 2 |
| 3.2971 | 3.0305 | 21.6173 | 4.0001 | 16.2502 | 16.2302 | 18.5 | 3 |
| 3.2452 | 3.0086 | 22.8085 | 4.9522 | 17.8831 | 17.7797 | 18.6 | 4 |
| 3.1899 | 2.9920 | 22.7903 | 5.3026 | 17.8844 | 17.8651 | 18.58 | 5 |
| 3.1514 | 2.9775 | 23.0533 | 5.3456 | 18.4312 | 18.3636 | 18.52 | 6 |
| 3.1050 | 2.9686 | 23.0767 | 5.1264 | 18.4552 | 18.3503 | 18.54 | 7 |
| 3.0688 | 2.9609 | 22.9985 | 5.0413 | 18.1856 | 18.0816 | 18.67 | 8 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/distilbert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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| 123,856 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Zangnan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Davlan/m2m100_418M-eng-yor-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
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}
| 9 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1004.35 +/- 86.89
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/mT5_base_yoruba_adr
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
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| 5 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/mbart50-large-eng-yor-mt
|
[
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MBartForConditionalGeneration"
],
"model_type": "mbart",
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| 5 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Zangnan/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Davlan/mbart50-large-yor-eng-mt
|
[
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MBartForConditionalGeneration"
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}
| 5 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 274.50 +/- 31.50
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Taratata -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Taratata -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Taratata
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 100000),
('n_timesteps', 10000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetune-dzongkha-to-romanized
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetune-dzongkha-to-romanized
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8324
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 3.1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 90 | 5.1830 | 0.0 | 0.0 | 0.0 | 0.0 | 3.2 |
| No log | 2.0 | 180 | 4.8936 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0667 |
| No log | 3.0 | 270 | 4.8324 | 0.0 | 0.0 | 0.0 | 0.0 | 3.1 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/mt5_base_eng_yor_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
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}
}
| 2 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 24.10 +/- 17.64
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/mt5_base_yor_eng_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1581
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2179 | 1.0 | 5533 | 1.1521 |
| 0.9581 | 2.0 | 11066 | 1.1296 |
| 0.7409 | 3.0 | 16599 | 1.1581 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.11.0
|
Declan/Breitbart_modelv7
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan-t5-large-da-multiwoz2.1_fs0.2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-da-multiwoz2.1_fs0.2
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3159
- Accuracy: 45.1554
- Num: 3689
- Gen Len: 15.5213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:|
| 0.9653 | 0.28 | 400 | 0.4635 | 31.3166 | 3689 | 15.196 |
| 0.5071 | 0.57 | 800 | 0.4031 | 35.8289 | 3689 | 15.6546 |
| 0.4603 | 0.85 | 1200 | 0.3718 | 37.6313 | 3689 | 15.6511 |
| 0.4219 | 1.13 | 1600 | 0.3577 | 37.9333 | 3689 | 16.5319 |
| 0.3991 | 1.42 | 2000 | 0.3491 | 40.5462 | 3689 | 15.453 |
| 0.394 | 1.7 | 2400 | 0.3409 | 40.9333 | 3689 | 15.5137 |
| 0.3822 | 1.98 | 2800 | 0.3370 | 41.2932 | 3689 | 15.225 |
| 0.3625 | 2.26 | 3200 | 0.3327 | 42.1132 | 3689 | 16.0718 |
| 0.3577 | 2.55 | 3600 | 0.3329 | 42.1372 | 3689 | 15.9973 |
| 0.3644 | 2.83 | 4000 | 0.3303 | 42.2529 | 3689 | 15.6525 |
| 0.349 | 3.11 | 4400 | 0.3256 | 43.2025 | 3689 | 15.6601 |
| 0.3355 | 3.4 | 4800 | 0.3243 | 43.791 | 3689 | 15.5451 |
| 0.338 | 3.68 | 5200 | 0.3231 | 43.5073 | 3689 | 15.7411 |
| 0.3424 | 3.96 | 5600 | 0.3196 | 44.5281 | 3689 | 15.1307 |
| 0.3299 | 4.25 | 6000 | 0.3159 | 45.1554 | 3689 | 15.5213 |
| 0.328 | 4.53 | 6400 | 0.3188 | 43.4699 | 3689 | 15.3849 |
| 0.3204 | 4.81 | 6800 | 0.3159 | 44.7764 | 3689 | 15.8219 |
| 0.3166 | 5.1 | 7200 | 0.3165 | 45.0608 | 3689 | 15.8791 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Declan/CNN_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
],
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan-t5-large-da-multiwoz2.1_fs0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-da-multiwoz2.1_fs0.1
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3373
- Accuracy: 43.2245
- Num: 3689
- Gen Len: 15.3058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:|
| 0.9683 | 0.56 | 400 | 0.4608 | 31.0524 | 3689 | 14.9279 |
| 0.5046 | 1.13 | 800 | 0.4028 | 35.7175 | 3689 | 15.1651 |
| 0.4488 | 1.69 | 1200 | 0.3803 | 36.4952 | 3689 | 16.3375 |
| 0.4267 | 2.25 | 1600 | 0.3613 | 38.4613 | 3689 | 15.2646 |
| 0.4003 | 2.81 | 2000 | 0.3538 | 39.8281 | 3689 | 15.5842 |
| 0.3862 | 3.38 | 2400 | 0.3497 | 40.0593 | 3689 | 15.2356 |
| 0.3729 | 3.94 | 2800 | 0.3433 | 40.857 | 3689 | 15.9675 |
| 0.3632 | 4.5 | 3200 | 0.3457 | 41.157 | 3689 | 15.8818 |
| 0.3534 | 5.06 | 3600 | 0.3367 | 42.9369 | 3689 | 15.7314 |
| 0.3432 | 5.63 | 4000 | 0.3358 | 41.9514 | 3689 | 15.7173 |
| 0.3395 | 6.19 | 4400 | 0.3373 | 43.2245 | 3689 | 15.3058 |
| 0.3345 | 6.75 | 4800 | 0.3351 | 42.4941 | 3689 | 14.8916 |
| 0.3266 | 7.31 | 5200 | 0.3360 | 42.9742 | 3689 | 15.7124 |
| 0.3233 | 7.88 | 5600 | 0.3327 | 43.1362 | 3689 | 15.9379 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Declan/CNN_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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| 5 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: question_v_statement_finetuned_roberta-basev2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# question_v_statement_finetuned_roberta-basev2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0052
- Accuracy: 0.9993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0077 | 1.0 | 3966 | 0.0055 | 0.9991 |
| 0.0008 | 2.0 | 7932 | 0.0052 | 0.9993 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Declan/CNN_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: xlsr-53-ur
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ur_pk
split: test
args: ur_pk
metrics:
- name: Wer
type: wer
value: 0.3450557529714496
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlsr-53-ur
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6860
- Wer: 0.3451
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 12
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0396 | 1.59 | 300 | 3.0179 | 1.0 |
| 0.4976 | 3.17 | 600 | 0.7037 | 0.5447 |
| 0.3062 | 4.76 | 900 | 0.5557 | 0.4036 |
| 0.2287 | 6.35 | 1200 | 0.5620 | 0.3935 |
| 0.2504 | 7.94 | 1500 | 0.5907 | 0.3677 |
| 0.0633 | 9.52 | 1800 | 0.6239 | 0.3773 |
| 0.0456 | 11.11 | 2100 | 0.6748 | 0.3604 |
| 0.0774 | 12.7 | 2400 | 0.6747 | 0.3552 |
| 0.058 | 14.29 | 2700 | 0.6860 | 0.3451 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/ChicagoTribune_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
| 3 | null |
Access to model spunk255/fernando is restricted and you are not in the authorized list. Visit https://huggingface.co/spunk255/fernando to ask for access.
|
Declan/ChicagoTribune_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 7 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: questionansweringmodel
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# questionansweringmodel
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7398 | 1.0 | 5520 | 0.7216 |
| 0.5263 | 2.0 | 11040 | 0.7513 |
| 0.3828 | 3.0 | 16560 | 0.8821 |
| 0.2411 | 4.0 | 22080 | 1.0024 |
| 0.1549 | 5.0 | 27600 | 1.3959 |
| 0.0996 | 6.0 | 33120 | 1.7559 |
| 0.0594 | 7.0 | 38640 | 2.0031 |
| 0.0393 | 8.0 | 44160 | 2.3654 |
| 0.0326 | 9.0 | 49680 | 2.7383 |
| 0.0162 | 10.0 | 55200 | 2.9365 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.11.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/ChicagoTribune_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"translation_en_to_fr": {
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}
| 5 | null |
---
tags:
- generated_from_trainer
model-index:
- name: petro-twitter-assistant-30ep
results: []
widget:
- text: Opino que mi gobierno es
datasets:
- jhonparra18/petro-tweets
language:
- es
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# petro-twitter-assistant-30ep
This model is a fine-tuned version of [flax-community/gpt-2-spanish](https://huggingface.co/flax-community/gpt-2-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.123 | 2.3 | 1000 | 3.0761 |
| 2.8048 | 4.6 | 2000 | 3.0394 |
| 2.5904 | 6.9 | 3000 | 3.0743 |
| 2.3804 | 9.2 | 4000 | 3.2378 |
| 2.1736 | 11.49 | 5000 | 3.4025 |
| 1.9736 | 13.79 | 6000 | 3.6284 |
| 1.779 | 16.09 | 7000 | 3.9806 |
| 1.5993 | 18.39 | 8000 | 4.2559 |
| 1.4584 | 20.69 | 9000 | 4.4938 |
| 1.3492 | 22.99 | 10000 | 4.6608 |
| 1.2701 | 25.29 | 11000 | 4.8302 |
| 1.2309 | 27.59 | 12000 | 4.8696 |
| 1.2161 | 29.89 | 13000 | 4.8837 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Declan/ChicagoTribune_model_v7
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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| 7 | null |
---
library_name: diffusers
pipeline_tag: text-to-image
base_model: CompVis/stable-diffusion-v1-4
---
|
Declan/FoxNews_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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| 7 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -64.99 +/- 26.01
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'oscarb92/cleanrl-ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Declan/FoxNews_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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| 7 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -118.98 +/- 36.13
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
| 1 | null |
Access to model FrancisDrakeK/test is restricted and you are not in the authorized list. Visit https://huggingface.co/FrancisDrakeK/test to ask for access.
|
Dmitriiserg/Pxd
|
[] | null |
{
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| 0 | null |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-22k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXt V2 (nano-sized model)
ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2).
Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various recognition benchmarks.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoImageProcessor, ConvNextV2ForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-nano-22k-384")
model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-huge-22k-384")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnextv2).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2301-00808,
author = {Sanghyun Woo and
Shoubhik Debnath and
Ronghang Hu and
Xinlei Chen and
Zhuang Liu and
In So Kweon and
Saining Xie},
title = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders},
journal = {CoRR},
volume = {abs/2301.00808},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2301.00808},
doi = {10.48550/arXiv.2301.00808},
eprinttype = {arXiv},
eprint = {2301.00808},
timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
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| 3 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Model Dreambooth model trained by thecreativemind2023 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Doquey/DialoGPT-small-Michaelbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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}
| 10 | null |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- Tritkoman/autotrain-data-romaniv2
co2_eq_emissions:
emissions: 71.97851742122822
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3584296276
- CO2 Emissions (in grams): 71.9785
## Validation Metrics
- Loss: 2.284
- SacreBLEU: 8.048
- Gen len: 49.335
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
}
| 27 | null |
---
tags:
- molecular language model
- SELFIES
- molecule optimization
inference: false
---
# MolGen-large-opt
MolGen-large-opt was introduced in the paper ["Molecular Language Model as Multi-task Generator"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen).
## Model description
MolGen-large-opt is the fine-tuned version of [MolGen-large](https://huggingface.co/zjunlp/MolGen-large). MolGen-large is the first pre-trained model that only produces chemically valid molecules.
With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large-opt can generate molecules with desired properties, making it a valuable tool for molecular optimization.
## Intended uses
You can use the fine-tuned model for molecule optimization for downstream tasks. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.
### How to use
Molecule optimization example:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large-opt")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large-opt")
>>> sf_input = tokenizer("[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]", return_tensors="pt")
>>> # beam search
>>> molecules = model.generate(input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
max_length=35,
min_length=5,
num_return_sequences=5,
num_beams=5)
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
['[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]']
```
### BibTeX entry and citation info
```bibtex
@article{fang2023molecular,
title={Molecular Language Model as Multi-task Generator},
author={Fang, Yin and Zhang, Ningyu and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
journal={arXiv preprint arXiv:2301.11259},
year={2023}
}
```
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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"AlbertForMaskedLM"
],
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}
| 687 | 2023-02-19T09:56:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
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