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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-25 06:27:54
| downloads
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| likes
int64 0
11.7k
| library_name
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ericw0530/bert-finetuned-squad | ericw0530 | 2022-05-22T06:27:50Z | 3 | 0 | transformers | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T15:43:12Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ericw0530/bert-finetuned-squad
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. -->
# ericw0530/bert-finetuned-squad
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: 2.1800
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 2565, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.9079 | 0 |
| 3.5422 | 1 |
| 2.5645 | 2 |
| 2.2832 | 3 |
| 2.1800 | 4 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Yuriky/q-FrozenLake-v1-8x8-slippery | Yuriky | 2022-05-22T04:12:40Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-22T04:12:29Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Yuriky/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
stevemobs/bert-base-spanish-wwm-uncased-finetuned-squad_es | stevemobs | 2022-05-22T03:38:07Z | 416 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-21T22:57:12Z | ---
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: bert-base-spanish-wwm-uncased-finetuned-squad_es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-uncased-finetuned-squad_es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7747
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.5377 | 1.0 | 8259 | 1.4632 |
| 1.1928 | 2.0 | 16518 | 1.5536 |
| 0.9486 | 3.0 | 24777 | 1.7747 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DavidCollier/q-Taxi-v3 | DavidCollier | 2022-05-22T02:46:31Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-22T02:46:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DavidCollier/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ruselkomp/sber-framebank-hidesize-1 | ruselkomp | 2022-05-22T01:57:09Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-21T22:10:53Z | ---
tags:
- generated_from_trainer
model-index:
- name: sber-framebank-hidesize-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. -->
# sber-framebank-hidesize-1
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.053 | 1.0 | 11307 | 1.0655 |
| 0.835 | 2.0 | 22614 | 1.2487 |
| 0.6054 | 3.0 | 33921 | 1.4154 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
Forkits/q-FrozenLake-v1-4x4-no-slippery | Forkits | 2022-05-22T00:58:04Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-22T00:51:52Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Forkits/q-FrozenLake-v1-4x4-no-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sandrokim/two_tower_sentence_snoobert | sandrokim | 2022-05-22T00:02:17Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-05-22T00:00:32Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sandrokim/two_tower_sentence_snoobert
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sandrokim/two_tower_sentence_snoobert')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sandrokim/two_tower_sentence_snoobert')
model = AutoModel.from_pretrained('sandrokim/two_tower_sentence_snoobert')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sandrokim/two_tower_sentence_snoobert)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 992,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
drscotthawley/wav2vec2-base-timit-demo-google-colab | drscotthawley | 2022-05-21T23:41:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-21T22:20:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5436
- Wer: 0.3401
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5276 | 1.0 | 500 | 1.9983 | 1.0066 |
| 0.8606 | 2.01 | 1000 | 0.5323 | 0.5220 |
| 0.4339 | 3.01 | 1500 | 0.4697 | 0.4512 |
| 0.3026 | 4.02 | 2000 | 0.4342 | 0.4266 |
| 0.2297 | 5.02 | 2500 | 0.5001 | 0.4135 |
| 0.1939 | 6.02 | 3000 | 0.4350 | 0.3897 |
| 0.1613 | 7.03 | 3500 | 0.4740 | 0.3883 |
| 0.1452 | 8.03 | 4000 | 0.4289 | 0.3825 |
| 0.1362 | 9.04 | 4500 | 0.4721 | 0.3927 |
| 0.1146 | 10.04 | 5000 | 0.4707 | 0.3730 |
| 0.1061 | 11.04 | 5500 | 0.4470 | 0.3701 |
| 0.0947 | 12.05 | 6000 | 0.4694 | 0.3722 |
| 0.0852 | 13.05 | 6500 | 0.5222 | 0.3733 |
| 0.0741 | 14.06 | 7000 | 0.4881 | 0.3657 |
| 0.069 | 15.06 | 7500 | 0.4957 | 0.3677 |
| 0.0679 | 16.06 | 8000 | 0.5241 | 0.3634 |
| 0.0618 | 17.07 | 8500 | 0.5091 | 0.3564 |
| 0.0576 | 18.07 | 9000 | 0.5055 | 0.3557 |
| 0.0493 | 19.08 | 9500 | 0.5013 | 0.3515 |
| 0.0469 | 20.08 | 10000 | 0.5506 | 0.3530 |
| 0.044 | 21.08 | 10500 | 0.5564 | 0.3528 |
| 0.0368 | 22.09 | 11000 | 0.5213 | 0.3509 |
| 0.0355 | 23.09 | 11500 | 0.5707 | 0.3495 |
| 0.0357 | 24.1 | 12000 | 0.5558 | 0.3483 |
| 0.0285 | 25.1 | 12500 | 0.5613 | 0.3455 |
| 0.0285 | 26.1 | 13000 | 0.5533 | 0.3480 |
| 0.0266 | 27.11 | 13500 | 0.5526 | 0.3462 |
| 0.0249 | 28.11 | 14000 | 0.5488 | 0.3429 |
| 0.0237 | 29.12 | 14500 | 0.5436 | 0.3401 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu115
- Datasets 1.18.3
- Tokenizers 0.12.1
|
JDog/90 | JDog | 2022-05-21T22:47:48Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-05-21T22:47:48Z | ---
license: cc-by-nc-sa-4.0
---
|
dalvarez/q-Taxi-v3 | dalvarez | 2022-05-21T22:19:49Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T22:19:42Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dalvarez/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
brad/q-Taxi-v3 | brad | 2022-05-21T22:11:03Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T22:10:57Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="brad/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
brad/q-FrozenLake-v1-4x4-no_slippery | brad | 2022-05-21T21:58:15Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T21:58:09Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no_slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="brad/q-FrozenLake-v1-4x4-no_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
kRo0T/q-FrozenLake-v1-8x8-slippery | kRo0T | 2022-05-21T21:33:24Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T21:33:16Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kRo0T/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
itaihay/wav2vec_asr_swbd | itaihay | 2022-05-21T20:37:08Z | 134 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-31T16:52:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_asr_swbd
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. -->
# wav2vec_asr_swbd
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3052
- Wer: 0.5302
## 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: 0.0004
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5445 | 0.29 | 500 | 0.9114 | 0.6197 |
| 0.9397 | 0.58 | 1000 | 0.5057 | 0.5902 |
| 0.8557 | 0.86 | 1500 | 0.4465 | 0.6264 |
| 0.7716 | 1.15 | 2000 | 0.4182 | 0.5594 |
| 0.7659 | 1.44 | 2500 | 0.4111 | 0.7048 |
| 0.7406 | 1.73 | 3000 | 0.3927 | 0.5944 |
| 0.6857 | 2.02 | 3500 | 0.3852 | 0.7118 |
| 0.7113 | 2.31 | 4000 | 0.3775 | 0.5608 |
| 0.6804 | 2.59 | 4500 | 0.3885 | 0.5759 |
| 0.6654 | 2.88 | 5000 | 0.3703 | 0.7226 |
| 0.6569 | 3.17 | 5500 | 0.3688 | 0.5972 |
| 0.6335 | 3.46 | 6000 | 0.3661 | 0.7278 |
| 0.6309 | 3.75 | 6500 | 0.3579 | 0.6324 |
| 0.6231 | 4.03 | 7000 | 0.3620 | 0.5770 |
| 0.6171 | 4.32 | 7500 | 0.3640 | 0.5772 |
| 0.6191 | 4.61 | 8000 | 0.3553 | 0.6075 |
| 0.6142 | 4.9 | 8500 | 0.3543 | 0.6126 |
| 0.5905 | 5.19 | 9000 | 0.3601 | 0.6319 |
| 0.5846 | 5.48 | 9500 | 0.3429 | 0.7343 |
| 0.5874 | 5.76 | 10000 | 0.3429 | 0.5962 |
| 0.5768 | 6.05 | 10500 | 0.3381 | 0.7410 |
| 0.5783 | 6.34 | 11000 | 0.3391 | 0.5823 |
| 0.5835 | 6.63 | 11500 | 0.3447 | 0.5821 |
| 0.5817 | 6.92 | 12000 | 0.3314 | 0.6890 |
| 0.5459 | 7.2 | 12500 | 0.3363 | 0.5727 |
| 0.5575 | 7.49 | 13000 | 0.3363 | 0.7387 |
| 0.5505 | 7.78 | 13500 | 0.3368 | 0.5685 |
| 0.55 | 8.07 | 14000 | 0.3330 | 0.5587 |
| 0.5523 | 8.36 | 14500 | 0.3338 | 0.5484 |
| 0.5116 | 8.65 | 15000 | 0.3350 | 0.4351 |
| 0.5263 | 8.93 | 15500 | 0.3254 | 0.6235 |
| 0.5265 | 9.22 | 16000 | 0.3297 | 0.6207 |
| 0.5265 | 9.51 | 16500 | 0.3279 | 0.6143 |
| 0.5172 | 9.8 | 17000 | 0.3260 | 0.5800 |
| 0.5028 | 10.09 | 17500 | 0.3259 | 0.5774 |
| 0.5062 | 10.37 | 18000 | 0.3259 | 0.5552 |
| 0.5112 | 10.66 | 18500 | 0.3201 | 0.6625 |
| 0.5149 | 10.95 | 19000 | 0.3184 | 0.6865 |
| 0.4939 | 11.24 | 19500 | 0.3152 | 0.6116 |
| 0.5065 | 11.53 | 20000 | 0.3172 | 0.5246 |
| 0.5129 | 11.82 | 20500 | 0.3129 | 0.5908 |
| 0.4909 | 12.1 | 21000 | 0.3152 | 0.6075 |
| 0.4865 | 12.39 | 21500 | 0.3160 | 0.5037 |
| 0.4805 | 12.68 | 22000 | 0.3139 | 0.5458 |
| 0.4691 | 12.97 | 22500 | 0.3225 | 0.5815 |
| 0.4534 | 13.26 | 23000 | 0.3168 | 0.5614 |
| 0.4661 | 13.54 | 23500 | 0.3135 | 0.6053 |
| 0.4636 | 13.83 | 24000 | 0.3120 | 0.5142 |
| 0.4554 | 14.12 | 24500 | 0.3127 | 0.5552 |
| 0.4602 | 14.41 | 25000 | 0.3117 | 0.5562 |
| 0.4521 | 14.7 | 25500 | 0.3106 | 0.4995 |
| 0.4369 | 14.99 | 26000 | 0.3100 | 0.5663 |
| 0.4249 | 15.27 | 26500 | 0.3110 | 0.5262 |
| 0.4321 | 15.56 | 27000 | 0.3106 | 0.5183 |
| 0.4293 | 15.85 | 27500 | 0.3091 | 0.5311 |
| 0.4537 | 16.14 | 28000 | 0.3134 | 0.4986 |
| 0.4258 | 16.43 | 28500 | 0.3138 | 0.4487 |
| 0.4347 | 16.71 | 29000 | 0.3091 | 0.5011 |
| 0.4615 | 17.0 | 29500 | 0.3068 | 0.5616 |
| 0.4163 | 17.29 | 30000 | 0.3115 | 0.5426 |
| 0.4074 | 17.58 | 30500 | 0.3079 | 0.5341 |
| 0.4121 | 17.87 | 31000 | 0.3047 | 0.5619 |
| 0.4219 | 18.16 | 31500 | 0.3085 | 0.5051 |
| 0.4049 | 18.44 | 32000 | 0.3084 | 0.5116 |
| 0.4119 | 18.73 | 32500 | 0.3071 | 0.5028 |
| 0.4129 | 19.02 | 33000 | 0.3064 | 0.5030 |
| 0.4143 | 19.31 | 33500 | 0.3040 | 0.5086 |
| 0.4013 | 19.6 | 34000 | 0.3057 | 0.5271 |
| 0.4162 | 19.88 | 34500 | 0.3052 | 0.5302 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
subhasisj/ar-adapter-32 | subhasisj | 2022-05-21T20:22:40Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-21T18:21:11Z | ---
tags:
- generated_from_trainer
model-index:
- name: ar-adapter-32
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. -->
# ar-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 352 | 5.6861 |
| 5.7356 | 2.0 | 704 | 5.5388 |
| 5.5308 | 3.0 | 1056 | 5.4493 |
| 5.5308 | 4.0 | 1408 | 5.4030 |
| 5.4304 | 5.0 | 1760 | 5.3886 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
turhancan97/q-FrozenLake-v1 | turhancan97 | 2022-05-21T19:43:25Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T19:43:18Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="turhancan97/q-FrozenLake-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Ambiwlans/qtab-FrozenLake-v1-4x4-nslippery | Ambiwlans | 2022-05-21T18:41:17Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T18:41:10Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qtab-FrozenLake-v1-4x4-nslippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Ambiwlans/qtab-FrozenLake-v1-4x4-nslippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/morrowind_rtf | huggingtweets | 2022-05-21T18:30:32Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-21T18:29:56Z | ---
language: en
thumbnail: http://www.huggingtweets.com/morrowind_rtf/1653157827665/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/1260443885102411779/DMPXS0hi_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">morrowind.rtf</div>
<div style="text-align: center; font-size: 14px;">@morrowind_rtf</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 morrowind.rtf.
| Data | morrowind.rtf |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 26 |
| Tweets kept | 3224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3sgyg1y6/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 @morrowind_rtf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hz9ik0o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hz9ik0o/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/morrowind_rtf')
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)
|
Astronomy88/LunarLander-v2-ppo_mlppolicy | Astronomy88 | 2022-05-21T18:20:54Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T18:20:22Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo_mlppolicy
results:
- metrics:
- type: mean_reward
value: 218.67 +/- 66.35
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo_mlppolicy** Agent playing **LunarLander-v2**
This is a trained model of a **ppo_mlppolicy** 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
...
```
|
sanjay-m1/informal-to-formal | sanjay-m1 | 2022-05-21T16:57:38Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-21T16:35:57Z | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
|
rajistics/q-FrozenLake-v1-8x8-slippery | rajistics | 2022-05-21T16:16:12Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T16:16:06Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rshah/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
amrahmed/q-FrozenLake-v1-4x4-non-slippery | amrahmed | 2022-05-21T16:01:45Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T16:01:35Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-non-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="amrahmed/q-FrozenLake-v1-4x4-non-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
wooihen/TEST2ppo-LunarLander-v2 | wooihen | 2022-05-21T15:06:47Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T15:06:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 218.95 +/- 19.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
smc/electric_2 | smc | 2022-05-21T14:38:26Z | 61 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-05-15T00:56:43Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: electric pole classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
Find whether an electric pole has a transformer or not
|
GKPro/PPO-LunarLander-v2 | GKPro | 2022-05-21T14:38:06Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T14:03:57Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 242.71 +/- 13.55
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
KrusHan/DQN-LunarLander-v2 | KrusHan | 2022-05-21T14:30:50Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-15T15:57:14Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 225.63 +/- 80.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
forsc/unit12ppo-LunarLander-v2 | forsc | 2022-05-21T14:09:17Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T14:08:46Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 278.19 +/- 17.91
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Photons/q-FrozenLake-v1-8x8-slippery | Photons | 2022-05-21T14:00:32Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T14:00:26Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Photons/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Akshat/distilbert-base-uncased-finetuned-emotion | Akshat | 2022-05-21T13:37:58Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-21T13:11:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9216312760504648
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2246
- Accuracy: 0.922
- F1: 0.9216
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8424 | 1.0 | 250 | 0.3246 | 0.9025 | 0.8989 |
| 0.2533 | 2.0 | 500 | 0.2246 | 0.922 | 0.9216 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
DBusAI/q-Taxi-v3-v2 | DBusAI | 2022-05-21T13:32:59Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T13:32:41Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v2
results:
- metrics:
- type: mean_reward
value: 9.12 +/- 2.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DBusAI/q-Taxi-v3-v1 | DBusAI | 2022-05-21T13:30:58Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T13:30:51Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- metrics:
- type: mean_reward
value: 7.80 +/- 2.82
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Dani-91/bert-finetuned-ner | Dani-91 | 2022-05-21T13:25:58Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-05-21T12:47:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9325062034739454
- name: Recall
type: recall
value: 0.9486704813194211
- name: F1
type: f1
value: 0.9405188954700927
- name: Accuracy
type: accuracy
value: 0.9859745687878966
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0618
- Precision: 0.9325
- Recall: 0.9487
- F1: 0.9405
- Accuracy: 0.9860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0874 | 1.0 | 1756 | 0.0645 | 0.9194 | 0.9382 | 0.9287 | 0.9835 |
| 0.0384 | 2.0 | 3512 | 0.0614 | 0.9297 | 0.9463 | 0.9379 | 0.9845 |
| 0.0186 | 3.0 | 5268 | 0.0618 | 0.9325 | 0.9487 | 0.9405 | 0.9860 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DBusAI/q-FrozenLake-v1-8x8-slippery-v3 | DBusAI | 2022-05-21T12:45:08Z | 0 | 0 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T12:45:01Z | ---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery-v3
results:
- metrics:
- type: mean_reward
value: 0.93 +/- 0.25
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-8x8-slippery-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DBusAI/q-FrozenLake-v1-4x4-slippery | DBusAI | 2022-05-21T12:29:11Z | 0 | 0 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T12:29:04Z | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ruselkomp/deep-pavlov-framebank-hidesize-1 | ruselkomp | 2022-05-21T12:19:16Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-21T08:04:30Z | ---
tags:
- generated_from_trainer
model-index:
- name: deep-pavlov-framebank-hidesize-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. -->
# deep-pavlov-framebank-hidesize-1
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0967
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.073 | 1.0 | 2827 | 1.0101 |
| 0.7856 | 2.0 | 5654 | 1.0367 |
| 0.5993 | 3.0 | 8481 | 1.0967 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
DBusAI/q-FrozenLake-v1-8x8 | DBusAI | 2022-05-21T12:15:51Z | 0 | 0 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T12:15:44Z | ---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8
results:
- metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-8x8", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
imamnurby/rob2rand_chen_w_prefix_tc | imamnurby | 2022-05-21T12:14:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-21T12:11:26Z | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: rob2rand_chen_w_prefix_tc
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. -->
# rob2rand_chen_w_prefix_tc
This model is a fine-tuned version of [imamnurby/rob2rand_chen_w_prefix](https://huggingface.co/imamnurby/rob2rand_chen_w_prefix) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
- Bleu: 83.9120
- Em: 86.2159
- Bleu Em: 85.0639
## 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-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Em | Bleu Em |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|
| 0.6922 | 0.71 | 500 | 0.2425 | 68.5819 | 79.7927 | 74.1873 |
| 0.086 | 1.42 | 1000 | 0.2480 | 70.9791 | 79.5855 | 75.2823 |
| 0.0865 | 2.13 | 1500 | 0.2567 | 68.7037 | 78.8256 | 73.7646 |
| 0.0758 | 2.84 | 2000 | 0.2483 | 69.4605 | 80.2418 | 74.8512 |
| 0.0683 | 3.55 | 2500 | 0.2662 | 68.3732 | 78.4456 | 73.4094 |
| 0.0643 | 4.26 | 3000 | 0.2700 | 66.5413 | 78.3765 | 72.4589 |
| 0.0596 | 4.97 | 3500 | 0.2611 | 67.4313 | 78.9637 | 73.1975 |
| 0.0519 | 5.68 | 4000 | 0.2697 | 68.3717 | 79.1019 | 73.7368 |
| 0.0478 | 6.39 | 4500 | 0.2914 | 69.7507 | 77.7202 | 73.7354 |
| 0.0461 | 7.1 | 5000 | 0.2776 | 68.5387 | 79.1019 | 73.8203 |
| 0.04 | 7.81 | 5500 | 0.2975 | 67.6316 | 78.1693 | 72.9004 |
| 0.0373 | 8.52 | 6000 | 0.2922 | 68.0161 | 79.4473 | 73.7317 |
| 0.0345 | 9.23 | 6500 | 0.3032 | 69.4580 | 79.2401 | 74.3490 |
| 0.032 | 9.94 | 7000 | 0.3104 | 67.2595 | 79.0328 | 73.1462 |
| 0.0294 | 10.65 | 7500 | 0.3077 | 65.8142 | 78.4801 | 72.1472 |
| 0.0269 | 11.36 | 8000 | 0.3092 | 70.2072 | 78.8601 | 74.5337 |
| 0.026 | 12.07 | 8500 | 0.3117 | 70.4504 | 79.4473 | 74.9489 |
| 0.0229 | 12.78 | 9000 | 0.3114 | 69.4635 | 79.2401 | 74.3518 |
| 0.0215 | 13.49 | 9500 | 0.3143 | 67.3601 | 79.3092 | 73.3346 |
| 0.0205 | 14.2 | 10000 | 0.3176 | 68.4031 | 78.9983 | 73.7007 |
| 0.0195 | 14.91 | 10500 | 0.3253 | 66.5673 | 78.9637 | 72.7655 |
| 0.0173 | 15.62 | 11000 | 0.3377 | 68.7553 | 78.7219 | 73.7386 |
| 0.0164 | 16.34 | 11500 | 0.3377 | 69.2474 | 79.1364 | 74.1919 |
| 0.0161 | 17.05 | 12000 | 0.3371 | 69.0846 | 79.6200 | 74.3523 |
| 0.0148 | 17.76 | 12500 | 0.3457 | 70.8330 | 79.3782 | 75.1056 |
| 0.0137 | 18.47 | 13000 | 0.3516 | 69.5576 | 79.2401 | 74.3988 |
| 0.0135 | 19.18 | 13500 | 0.3573 | 70.3232 | 79.1364 | 74.7298 |
| 0.0127 | 19.89 | 14000 | 0.3574 | 70.2481 | 79.1019 | 74.6750 |
| 0.0115 | 20.6 | 14500 | 0.3694 | 65.7587 | 78.3765 | 72.0676 |
| 0.0107 | 21.31 | 15000 | 0.3696 | 68.7923 | 78.5838 | 73.6880 |
| 0.0107 | 22.02 | 15500 | 0.3607 | 69.4452 | 78.8256 | 74.1354 |
| 0.0101 | 22.73 | 16000 | 0.3770 | 68.6731 | 78.5492 | 73.6112 |
| 0.0095 | 23.44 | 16500 | 0.3648 | 69.8402 | 79.7237 | 74.7819 |
| 0.0088 | 24.15 | 17000 | 0.3822 | 69.6238 | 79.0328 | 74.3283 |
| 0.0088 | 24.86 | 17500 | 0.3816 | 68.5422 | 79.1364 | 73.8393 |
| 0.0079 | 25.57 | 18000 | 0.3822 | 69.1359 | 79.2401 | 74.1880 |
| 0.0073 | 26.28 | 18500 | 0.3742 | 69.8331 | 79.6891 | 74.7611 |
| 0.007 | 26.99 | 19000 | 0.3849 | 69.5048 | 79.2746 | 74.3897 |
| 0.0072 | 27.7 | 19500 | 0.3881 | 69.6135 | 79.2055 | 74.4095 |
| 0.0059 | 28.41 | 20000 | 0.3922 | 70.2656 | 79.2746 | 74.7701 |
| 0.0069 | 29.12 | 20500 | 0.3936 | 68.2044 | 78.7910 | 73.4977 |
| 0.0059 | 29.83 | 21000 | 0.3983 | 69.6257 | 79.4473 | 74.5365 |
| 0.0055 | 30.54 | 21500 | 0.3973 | 70.4039 | 79.5509 | 74.9774 |
| 0.0057 | 31.25 | 22000 | 0.3960 | 70.3015 | 79.6546 | 74.9780 |
| 0.0056 | 31.96 | 22500 | 0.3945 | 69.9785 | 79.5855 | 74.7820 |
| 0.0049 | 32.67 | 23000 | 0.3947 | 70.1822 | 79.6546 | 74.9184 |
| 0.0049 | 33.38 | 23500 | 0.3957 | 69.1207 | 79.3437 | 74.2322 |
| 0.0048 | 34.09 | 24000 | 0.4097 | 68.8815 | 78.9292 | 73.9053 |
| 0.0043 | 34.8 | 24500 | 0.4039 | 70.0982 | 79.4473 | 74.7727 |
| 0.0044 | 35.51 | 25000 | 0.4080 | 69.3472 | 79.5164 | 74.4318 |
| 0.0042 | 36.22 | 25500 | 0.4066 | 69.0213 | 79.0674 | 74.0443 |
| 0.0038 | 36.93 | 26000 | 0.4128 | 69.1452 | 79.3092 | 74.2272 |
| 0.0037 | 37.64 | 26500 | 0.4134 | 69.2672 | 79.5164 | 74.3918 |
| 0.0034 | 38.35 | 27000 | 0.4161 | 69.7751 | 79.5509 | 74.6630 |
| 0.0038 | 39.06 | 27500 | 0.4037 | 69.4092 | 79.6546 | 74.5319 |
| 0.0031 | 39.77 | 28000 | 0.4041 | 69.3912 | 79.6546 | 74.5229 |
| 0.0032 | 40.48 | 28500 | 0.4185 | 69.1159 | 79.4473 | 74.2816 |
| 0.0031 | 41.19 | 29000 | 0.4245 | 68.6867 | 78.9983 | 73.8425 |
| 0.003 | 41.9 | 29500 | 0.4202 | 69.4091 | 79.3092 | 74.3591 |
| 0.0027 | 42.61 | 30000 | 0.4249 | 68.7400 | 79.0328 | 73.8864 |
| 0.0026 | 43.32 | 30500 | 0.4175 | 69.9729 | 79.8273 | 74.9001 |
| 0.0027 | 44.03 | 31000 | 0.4189 | 69.6688 | 79.5855 | 74.6271 |
| 0.0027 | 44.74 | 31500 | 0.4203 | 69.4071 | 79.5855 | 74.4963 |
| 0.0025 | 45.45 | 32000 | 0.4265 | 69.3197 | 79.1019 | 74.2108 |
| 0.0023 | 46.16 | 32500 | 0.4255 | 69.7513 | 79.3437 | 74.5475 |
| 0.0023 | 46.88 | 33000 | 0.4227 | 69.2893 | 79.5509 | 74.4201 |
| 0.0023 | 47.59 | 33500 | 0.4233 | 69.6060 | 79.5509 | 74.5785 |
| 0.002 | 48.3 | 34000 | 0.4239 | 69.0113 | 79.4819 | 74.2466 |
| 0.0024 | 49.01 | 34500 | 0.4239 | 68.9754 | 79.4128 | 74.1941 |
| 0.0019 | 49.72 | 35000 | 0.4228 | 68.9220 | 79.3782 | 74.1501 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Tobias/bert-base-uncased_German_MultiLable_classification | Tobias | 2022-05-21T12:05:42Z | 7 | 1 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-21T12:00:43Z | ---
language: de
tags:
- bert
license: apache-2.0
widget:
- text: "Das Frühstück ist sehr gut, es gibt auch Laktosefreie Produkte."
example_title: "Example 1"
- text: "Das Personal ist sehr kompetent und sehr freundlich."
example_title: "Example 2"
- text: "Die Zimmer sind wie beschrieben sehr klein, vergleichbar mit einer Kreuzfahrtschiffkabine. "
example_title: "Example 3"
- text: "Scheinwerfer vor dem Zimmer ganze Nacht an und zu hell"
example_title: "Example 4"
---
# German Hotel Review Sentiment Classification
A model trained on German Hotel Reviews from Switzerland. The base model is the [bert-base-german-cased](https://huggingface.co/bert-base-german-cased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset.
# Model Performance
| Classes | Precision | Recall | F1 Score |
| :--- | :---: | :---: |:---: |
| Room | 84.62% | 88.00% | 86.27% |
| Food | 79.17% | 82.61% | 80.85% |
| Staff | 63.64% | 70.00% | 66.67% |
| Location | 83.33% | 62.50% | 71.43% |
| GeneralUtilitys | 76.92% | 76.92% | 76.92% |
| HotelOrganisation | 26.67% | 30.77% | 28.57% |
| Unknown | 25.00% | 16.67% | 20.00% |
| ReasonForStay | 100.00% | 50.00% | 66.67% |
| Accuracy | | | 69.00% |
| Macro Average | 67.42% | 59.68% | 62.17% |
| Weighted Average | 69.36% | 69.00% | 68.79% |
## Confusion Matrix
 |
DBusAI/q-Taxi-v3 | DBusAI | 2022-05-21T11:57:31Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T11:57:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CWhy/q-FrozenLake-v1-8x8-slippery | CWhy | 2022-05-21T11:44:52Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T11:44:44Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CWhy/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
questgen/msmarco-distilbert-base-v4-feature-extraction-pipeline | questgen | 2022-05-21T11:15:42Z | 11 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-05-21T11:11:17Z | ---
pipeline_tag: feature-extraction
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-v4
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-v4')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-distilbert-base-v4')
model = AutoModel.from_pretrained('sentence-transformers/msmarco-distilbert-base-v4')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-v4)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
Riverdayspa/bodymassagechennai | Riverdayspa | 2022-05-21T10:53:24Z | 0 | 0 | null | [
"region:us"
] | null | 2022-05-21T10:53:01Z | Riverdayspa™ is one of the Top Luxury Massage Center in Chennai. We offer Quality massage therapy all over the bustling city of Chennai.
https://www.riverdayspa.com/ |
amareelez/ppo-LunarLander-v2 | amareelez | 2022-05-21T09:30:43Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T09:01:03Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 280.46 +/- 18.03
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
linker81/q-learning-Taxi-v3 | linker81 | 2022-05-21T09:20:26Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T09:20:18Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="linker81/q-learning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
linker81/q-learning-FrozenLake-v1-4x4-no-slippery | linker81 | 2022-05-21T09:16:57Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T09:15:44Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-FrozenLake-v1-4x4-no-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1-4x4-no-slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-no-slippery** .
## Usage
```python
model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-no-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
linker81/QLearning-FrozenLake-v1 | linker81 | 2022-05-21T09:09:08Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-21T09:09:00Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: QLearning-FrozenLake-v1
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="linker81/QLearning-FrozenLake-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
anas-awadalla/bert-tiny-finetuned-squad | anas-awadalla | 2022-05-21T08:11:40Z | 60 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-tiny-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-tiny-finetuned-squad
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/albert-xxl-v2-finetuned-squad | anas-awadalla | 2022-05-21T08:02:10Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T23:34:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-xxl-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-xxl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
kabelomalapane/en_nso_ukuxhumana_model | kabelomalapane | 2022-05-21T01:17:17Z | 69 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-05-20T00:42:35Z | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en_nso_ukuxhumana_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# en_nso_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8482
- Bleu (before training): 12.2324
- Bleu: 18.9287
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
kabelomalapane/nso_en_ukuxhumana_model | kabelomalapane | 2022-05-21T01:15:15Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-05-20T11:20:16Z | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nso_en_ukuxhumana_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nso_en_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-nso-en](https://huggingface.co/Helsinki-NLP/opus-mt-nso-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9349
- Bleu (before training): 9.3297
- Bleu: 18.1161
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
huggingtweets/annebottz | huggingtweets | 2022-05-21T00:49:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-21T00:48:35Z | ---
language: en
thumbnail: http://www.huggingtweets.com/annebottz/1653094143094/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/1526210961031548935/59jbyuut_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">Anne Bot</div>
<div style="text-align: center; font-size: 14px;">@annebottz</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 Anne Bot.
| Data | Anne Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 590 |
| Tweets kept | 2660 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/263xyaa3/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 @annebottz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/edyr41r2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/edyr41r2/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/annebottz')
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)
|
huggingtweets/darcywubot | huggingtweets | 2022-05-21T00:27:43Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-21T00:27:13Z | ---
language: en
thumbnail: http://www.huggingtweets.com/darcywubot/1653092857463/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/1520965807374835712/oz5XZFva_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">Darcy Bot</div>
<div style="text-align: center; font-size: 14px;">@darcywubot</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 Darcy Bot.
| Data | Darcy Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 6 |
| Short tweets | 413 |
| Tweets kept | 2831 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ou05gm6/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 @darcywubot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p4xvqb6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p4xvqb6/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/darcywubot')
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)
|
anas-awadalla/albert-xl-v2-finetuned-squad | anas-awadalla | 2022-05-20T23:29:59Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T18:16:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-xl-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-xl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2) 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
fmcurti/q-FrozenLake-v1-8x8-non-slippery | fmcurti | 2022-05-20T23:14:25Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T23:14:19Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-non-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fmcurti/q-FrozenLake-v1-8x8-non-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
btsas/q-Taxi-v3 | btsas | 2022-05-20T21:47:36Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T21:47:30Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="btsas/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ericklerouge123/distilbert-base-uncased-finetuned-emotion | ericklerouge123 | 2022-05-20T20:35:39Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-20T14:23:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dmitry-np/q-FrozenLake-v1-4x4-non-slippery | dmitry-np | 2022-05-20T20:27:12Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T20:26:38Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-non-slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dmitry-np/q-FrozenLake-v1-4x4-non-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT | Ukhushn | 2022-05-20T20:07:14Z | 18 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-05-20T20:07:06Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT')
model = AutoModel.from_pretrained('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6661 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2665,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
subhasisj/xlm-roberta-base-squad-32 | subhasisj | 2022-05-20T19:13:21Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T14:05:13Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: xlm-roberta-base-squad-32
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. -->
# xlm-roberta-base-squad-32
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0083
## 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: 1.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 350 | 1.2339 |
| 2.3864 | 2.0 | 700 | 1.0571 |
| 1.0541 | 3.0 | 1050 | 1.0246 |
| 1.0541 | 4.0 | 1400 | 0.9947 |
| 0.9214 | 5.0 | 1750 | 1.0083 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Battu007/V3_PPO_LunarLander_v2 | Battu007 | 2022-05-20T18:05:48Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T18:05:31Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 216.14 +/- 67.68
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Photons/TEST2ppo-LunarLander-v2 | Photons | 2022-05-20T17:58:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T17:57:42Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 12.57 +/- 37.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
umangchaudhry/bert-emotion | umangchaudhry | 2022-05-20T16:56:12Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-20T15:59:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7081377380103309
- name: Recall
type: recall
value: 0.709386945441909
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2350
- Precision: 0.7081
- Recall: 0.7094
- Fscore: 0.7082
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8442 | 1.0 | 815 | 0.8653 | 0.7642 | 0.6192 | 0.6363 |
| 0.5488 | 2.0 | 1630 | 0.9330 | 0.7116 | 0.6838 | 0.6912 |
| 0.2713 | 3.0 | 2445 | 1.2350 | 0.7081 | 0.7094 | 0.7082 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
pm390/q-Taxi-v3 | pm390 | 2022-05-20T16:35:48Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T16:35:43Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pm390/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
HueyNemud/das22-42-camembert_finetuned_ref | HueyNemud | 2022-05-20T16:25:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-05-20T16:22:44Z | ---
tags:
- generated_from_trainer
model-index:
- name: CamemBERT pretrained on french trade directories from the XIXth century
results: []
---
# CamemBERT trained for NER on french trade directories from the XIXth century [GOLD training set]
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in Historical
> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida,
> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022.
> Lecture Notes in Computer Science, vol 13237. Springer, Cham.
> https://doi.org/10.1007/978-3-031-06555-2_30
The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/40_experiment_2.ipynb`.
## Model description
This model adapts the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for NER on 6004 manually annotated directory entries referred as the "reference dataset" in the paper.
Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g:
```
Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée-
```
## Intended uses & limitations
This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper.
Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset :
- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected.
- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR.
- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract.
### Training hyperparameters
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
pm390/q-FrozenLake-v1-4x4-no_slippery | pm390 | 2022-05-20T16:08:40Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T16:08:34Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no_slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pm390/q-FrozenLake-v1-4x4-no_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
MaryaAI/opus-mt-ar-en-finetunedQAdata-v1-ar-to-en | MaryaAI | 2022-05-20T15:50:42Z | 5 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-20T14:36:45Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MaryaAI/opus-mt-ar-en-finetunedQAdata-v1-ar-to-en
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. -->
# MaryaAI/opus-mt-ar-en-finetunedQAdata-v1-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0053
- Validation Loss: 8.2764
- Epoch: 14
## 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 |
|:----------:|:---------------:|:-----:|
| 0.0090 | 12.3530 | 0 |
| 0.0134 | 11.3018 | 1 |
| 0.0110 | 10.5958 | 2 |
| 0.0083 | 9.7381 | 3 |
| 0.0068 | 8.9434 | 4 |
| 0.0080 | 12.7723 | 5 |
| 0.0071 | 11.5191 | 6 |
| 0.0077 | 10.6246 | 7 |
| 0.0101 | 10.3368 | 8 |
| 0.0092 | 8.7824 | 9 |
| 0.0070 | 7.7344 | 10 |
| 0.0070 | 8.2180 | 11 |
| 0.0079 | 7.8572 | 12 |
| 0.0070 | 9.3053 | 13 |
| 0.0053 | 8.2764 | 14 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
ruselkomp/deep-pavlov-framebank-5epochs-2 | ruselkomp | 2022-05-20T15:09:32Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T11:12:22Z | ---
tags:
- generated_from_trainer
model-index:
- name: deep-pavlov-framebank-5epochs-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. -->
# deep-pavlov-framebank-5epochs-2
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4667 | 1.0 | 2827 | 1.3508 |
| 0.3114 | 2.0 | 5654 | 1.5341 |
| 0.1941 | 3.0 | 8481 | 1.8772 |
| 0.1185 | 4.0 | 11308 | 2.1496 |
| 0.0795 | 5.0 | 14135 | 2.4205 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2.dev0
- Tokenizers 0.12.1
|
tiana-organics/natural-and-organic-skincare | tiana-organics | 2022-05-20T14:02:50Z | 0 | 2 | null | [
"region:us"
] | null | 2022-05-20T13:43:56Z | TIANA Fairtrade Organics gets over 16 years' experience growing elite execution, veggie lover, normal and natural excellence items to convey every one of the advantages in an extraordinary plant improved equation for more youthful looking and solid skin.
TIANA Organic Clean Ethical Skincare just holds back clean <a href="https://tiana-organics.com/organic-beauty">organic beauty products</a> that are non-poisonous and ok for skin. A total all-natural and organic skincare that is liberated from possibly hurtful fixings. No engineered fixings or hurtful poisons. Our perfect natural magnificence items just incorporate morally obtained and harmless to the ecosystem fixings. |
gulteng/distilbert-base-uncased-finetuned-squad | gulteng | 2022-05-20T13:44:24Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-20T11:58:43Z | ---
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: 1.2131
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2672 | 1.0 | 5533 | 1.2131 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Tanapon/TEST2ppo-LunarLander-v2-03 | Tanapon | 2022-05-20T12:58:52Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T12:58:24Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.09 +/- 20.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Sixtch/distilbert-base-uncased-finetuned-cola | Sixtch | 2022-05-20T12:10:43Z | 3 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-20T11:05:03Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Sixtch/distilbert-base-uncased-finetuned-cola
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. -->
# Sixtch/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1766
- Validation Loss: 0.5678
- Train Matthews Correlation: 0.5127
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5183 | 0.4703 | 0.4722 | 0 |
| 0.3174 | 0.4709 | 0.5227 | 1 |
| 0.1766 | 0.5678 | 0.5127 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
ThomasSimonini/q-Taxi-v3 | ThomasSimonini | 2022-05-20T11:44:47Z | 0 | 2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-19T07:24:26Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```
pickle_model = load_from_hub(repo_id="ThomasSimonini/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
laurens88/finetuning-crypto-tweet-sentiment-test | laurens88 | 2022-05-20T11:14:25Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-10T15:18:58Z | ---
tags:
- generated_from_trainer
model-index:
- name: finetuning-crypto-tweet-sentiment-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-crypto-tweet-sentiment-test
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
familiesportrait/portraitzeichnenlassen | familiesportrait | 2022-05-20T10:35:24Z | 0 | 0 | null | [
"region:us"
] | null | 2022-05-20T10:34:07Z | Und wenn Sie es jemals satt haben, Ihr eigenes Bild zu zeichnen, können Sie sich jederzeit mit einem Freund treffen und üben, Porträts voneinander zu zeichnen.
[https://familiesportrait.de/products/portrait-zeichnen-lassen](https://familiesportrait.de/products/portrait-zeichnen-lassen)
|
ejembere/opus-mt-en-ro-finetuned-en-to-ro | ejembere | 2022-05-20T10:32:09Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-20T10:26:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
model-index:
- name: opus-mt-en-ro-finetuned-en-to-ro
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
PontifexMaximus/opus-mt-tr-en-finetuned-az-to-en | PontifexMaximus | 2022-05-20T08:13:37Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:turkic_xwmt",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-20T05:44:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- turkic_xwmt
metrics:
- bleu
model-index:
- name: opus-mt-tr-en-finetuned-az-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: turkic_xwmt
type: turkic_xwmt
args: az-en
metrics:
- name: Bleu
type: bleu
value: 0.0002
---
<!-- 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. -->
# opus-mt-tr-en-finetuned-az-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) on the turkic_xwmt dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 0.0002
- Gen Len: 511.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.2
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 38 | nan | 0.0002 | 511.0 |
| No log | 2.0 | 76 | nan | 0.0002 | 511.0 |
| No log | 3.0 | 114 | nan | 0.0002 | 511.0 |
| No log | 4.0 | 152 | nan | 0.0002 | 511.0 |
| No log | 5.0 | 190 | nan | 0.0002 | 511.0 |
| No log | 6.0 | 228 | nan | 0.0002 | 511.0 |
| No log | 7.0 | 266 | nan | 0.0002 | 511.0 |
| No log | 8.0 | 304 | nan | 0.0002 | 511.0 |
| No log | 9.0 | 342 | nan | 0.0002 | 511.0 |
| No log | 10.0 | 380 | nan | 0.0002 | 511.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
imamnurby/rob2rand_chen_w_prefix | imamnurby | 2022-05-20T08:11:12Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-05-20T08:06:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: rob2rand_chen_w_prefix
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. -->
# rob2rand_chen_w_prefix
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0686
- eval_bleu: 84.3905
- eval_em: 50.0650
- eval_bleu_em: 67.2278
- eval_runtime: 20.8187
- eval_samples_per_second: 36.938
- eval_steps_per_second: 0.624
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
aricibo/swin-tiny-patch4-window7-224-finetuned-eurosat | aricibo | 2022-05-20T07:48:24Z | 73 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-05-20T06:38:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9725925925925926
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0657
- Accuracy: 0.9726
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.18 | 1.0 | 190 | 0.0844 | 0.9689 |
| 0.1347 | 2.0 | 380 | 0.0657 | 0.9726 |
| 0.1459 | 3.0 | 570 | 0.0657 | 0.9726 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
himanshusrtekbox/distilbert-base-uncased-finetuned-cola | himanshusrtekbox | 2022-05-20T06:48:20Z | 5 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-20T05:52:20Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: himanshusrtekbox/distilbert-base-uncased-finetuned-cola
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. -->
# himanshusrtekbox/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1911
- Validation Loss: 0.5605
- Train Matthews Correlation: 0.5106
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5185 | 0.4556 | 0.4728 | 0 |
| 0.3247 | 0.4570 | 0.5093 | 1 |
| 0.1911 | 0.5605 | 0.5106 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
ShreyaR/finetuned-roberta-depression | ShreyaR | 2022-05-20T04:38:42Z | 300 | 7 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
widget:
- text: "I feel so low and numb, don't feel like doing anything. Just passing my days"
- text: "Sleep is my greatest and most comforting escape whenever I wake up these days. The literal very first emotion I feel is just misery and reminding myself of all my problems."
- text: "I went to a movie today. It was below my expectations but the day was fine."
- text: "The first day of work was a little hectic but met pretty good colleagues, we went for a team dinner party at the end of the day."
metrics:
- accuracy
model-index:
- name: finetuned-roberta-depression
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. -->
# finetuned-roberta-depression
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: 0.1385
- Accuracy: 0.9745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0238 | 1.0 | 625 | 0.1385 | 0.9745 |
| 0.0333 | 2.0 | 1250 | 0.1385 | 0.9745 |
| 0.0263 | 3.0 | 1875 | 0.1385 | 0.9745 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/connorhvnsen | huggingtweets | 2022-05-20T03:52:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-20T03:51:49Z | ---
language: en
thumbnail: http://www.huggingtweets.com/connorhvnsen/1653018744349/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/1524595130031915009/JbJeqNFJ_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">HɅNSΞN ™</div>
<div style="text-align: center; font-size: 14px;">@connorhvnsen</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 HɅNSΞN ™.
| Data | HɅNSΞN ™ |
| --- | --- |
| Tweets downloaded | 1253 |
| Retweets | 317 |
| Short tweets | 309 |
| Tweets kept | 627 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qz1rz5ej/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 @connorhvnsen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/aeaa7tfg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/aeaa7tfg/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/connorhvnsen')
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)
|
mindwrapped/ppo-FrozenLake-v1 | mindwrapped | 2022-05-20T01:02:42Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-20T00:57:19Z | ---
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 0.78 +/- 0.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
---
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Remicm/sentiment-analysis-model-for-socialmedia | Remicm | 2022-05-19T22:46:09Z | 15 | 5 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-19T21:58:14Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: sentiment-analysis-model-for-socialmedia
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9297083333333334
- name: F1
type: f1
value: 0.9298923658729169
---
<!-- 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. -->
# sentiment-analysis-model-for-socialmedia
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- Accuracy: 0.9297
- F1: 0.9299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Dizzykong/gpt2-medium-commands | Dizzykong | 2022-05-19T22:45:13Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-17T19:05:55Z | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-medium-commands
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. -->
# gpt2-medium-commands
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 10
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
osanseviero/q-FrozenLake-v1-noSlippery1 | osanseviero | 2022-05-19T21:12:11Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-19T20:40:31Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-noSlippery1
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
pickle_model = load_from_hub(repo_id="osanseviero/q-FrozenLake-v1-noSlippery1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
fangyuan/lfqa_role_classification | fangyuan | 2022-05-19T20:21:02Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-02T23:40:39Z | ---
license: cc-by-nc-sa-4.0
---
|
fabiochiu/ppo-CartPole-v1 | fabiochiu | 2022-05-19T18:51:09Z | 5 | 0 | stable-baselines3 | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-19T18:47:30Z | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Aymene/Burnout-Danger-Prediction | Aymene | 2022-05-19T18:48:59Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2022-05-19T18:13:28Z | ---
title: Burnout Danger Prediction
emoji: ⚡
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
withU/Koelectra-five-sentiment-classification | withU | 2022-05-19T16:08:31Z | 0 | 0 | null | [
"region:us"
] | null | 2022-05-12T05:44:01Z | # Koelectra-five-sentiment-classification
Koelectra on hugging face Transformers for Psychological Counseling
- [full project link](https://github.com/jiminAn/Capstone_2022)
## how to use
```
from transformers import ElectraModel, ElectraTokenizer
model = ElectraModel.from_pretrained("withU/Koelectra-five-sentiment-classification")
tokenizer = ElectraTokenizer.from_pretrained("withU/Koelectra-five-sentiment-classification")
categories = "withU/Koelectra-five-sentiment-classification" # 카테고리, index 파일
sentence = "나는 방금 밥을 먹었다."
inputs = tokenizer.encode(sentence, return_tensors="pt")
outputs = model(**inputs)
softmax_logit = nn.Softmax(outputs).dim
softmax_logit = softmax_logit[0].squeeze()
max_index = torch.argmax(softmax_logit).item()
prediction = max_index
print(sentence, categories[prediction])
```
## dataset finetuned on
- [wellness dataset](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-006)
- [chatbot data](https://jeongukjae.github.io/tfds-korean/datasets/korean_chatbot_qa_data.html)
- [korean-hate-speech](https://github.com/kocohub/korean-hate-speech)
## references
- [WelllnessConversation-LanguageModel](https://github.com/nawnoes/WellnessConversation-LanguageModel)
- [KoELECTRA](https://github.com/monologg/KoELECTRA)
|
linker81/PPO-LunarLander-v2 | linker81 | 2022-05-19T15:34:39Z | 24 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-08T06:37:39Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 279.25 +/- 16.69
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 10,
gamma = 0.999,
gae_lambda = 0.98,
ent_coef = 0.01,
verbose=1)
|
AndyGo/speechbrain-asr-crdnn-rnnlm-buriy-audiobooks-2-val | AndyGo | 2022-05-19T14:53:31Z | 40 | 0 | speechbrain | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"ru",
"dataset:buriy-audiobooks-2-val",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2022-05-11T11:08:46Z | ---
language: "ru"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- buriy-audiobooks-2-val
metrics:
- wer
- cer
---
| Release | Test WER | GPUs |
|:-------------:|:--------------:| :--------:|
| 22-05-11 | - | 1xK80 24GB |
after 9 epochs training - valid %WER: 4.09e+02
after 12 epochs training - valid %WER: 2.07e+02, test WER: 1.78e+02
## Pipeline description
(by SpeechBrain text)
This ASR system is composed with 3 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions of LibriSpeech.
- Neural language model (RNNLM) trained on the full (380K) words dataset.
- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
N blocks of convolutional neural networks with normalisation and pooling on the
frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
the final acoustic representation that is given to the CTC and attention decoders.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that SpeechBrain encourage you to read tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files (in Russian)
```python
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="AndyGo/speechbrain-asr-crdnn-rnnlm-buriy-audiobooks-2-val", savedir="pretrained_models/speech-brain-asr-crdnn-rnnlm-buriy-audiobooks-2-val")
asr_model.transcribe_file('speechbrain-asr-crdnn-rnnlm-buriy-audiobooks-2-val/example.wav')
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Russian Speech Datasets
Russian Speech Datasets are provided by Microsoft Corporation with CC BY-NC license.
Instructions by downloading - https://github.com/snakers4/open_stt
The CC BY-NC license requires that the original copyright owner be listed as the author and the work be used only for non-commercial purposes
We used buriy-audiobooks-2-val dataset
## About SpeechBrain
Website: https://speechbrain.github.io/
Code: https://github.com/speechbrain/speechbrain/
HuggingFace: https://huggingface.co/speechbrain/
## Citing SpeechBrain
Please, cite SpeechBrain if you use it for your research or business.
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
} |
tugrulhkarabulut/rl-course | tugrulhkarabulut | 2022-05-19T13:51:25Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-19T13:22:49Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.29 +/- 18.80
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
HueyNemud/das22-10-camembert_pretrained | HueyNemud | 2022-05-19T12:05:12Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model-index:
- name: CamemBERT pretrained on french trade directories from the XIXth century
results: []
---
# CamemBERT pretrained on french trade directories from the XIXth century
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in Historical
> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida,
> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022.
> Lecture Notes in Computer Science, vol 13237. Springer, Cham.
> https://doi.org/10.1007/978-3-031-06555-2_30
The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/10-camembert_pretraining.ipynb`.
## Model description
This model pre-train the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on a set of ~845k entries from Paris trade directories from the XIXth century extracted with OCR.
Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g:
```
Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée-
```
## Intended uses & limitations
This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper.
Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset :
- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected.
- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR.
- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.9603 | 1.0 | 100346 | 1.8005 |
| 1.7032 | 2.0 | 200692 | 1.6460 |
| 1.5879 | 3.0 | 301038 | 1.5570 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Yozh2/ppo-LunarLander-v2 | Yozh2 | 2022-05-19T10:52:04Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-05-19T10:51:09Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -8.17 +/- 59.21
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
proseph/ctrlv-speechrecognition-model | proseph | 2022-05-19T09:59:57Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-22T04:30:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ctrlv-speechrecognition-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ctrlv-speechrecognition-model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4730
- Wer: 0.3031
## Test WER in TIMIT dataset
- Wer: 0.189
[Google Colab Notebook](https://colab.research.google.com/drive/1M9ZbqvoRqshEccIlpTQGsgptpiGVgauH)
## 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: 32
- 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: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.53 | 3.45 | 500 | 1.4021 | 0.9307 |
| 0.6077 | 6.9 | 1000 | 0.4255 | 0.4353 |
| 0.2331 | 10.34 | 1500 | 0.3887 | 0.3650 |
| 0.1436 | 13.79 | 2000 | 0.3579 | 0.3393 |
| 0.1021 | 17.24 | 2500 | 0.4447 | 0.3440 |
| 0.0797 | 20.69 | 3000 | 0.4041 | 0.3291 |
| 0.0657 | 24.14 | 3500 | 0.4262 | 0.3368 |
| 0.0525 | 27.59 | 4000 | 0.4937 | 0.3429 |
| 0.0454 | 31.03 | 4500 | 0.4449 | 0.3244 |
| 0.0373 | 34.48 | 5000 | 0.4363 | 0.3288 |
| 0.0321 | 37.93 | 5500 | 0.4519 | 0.3204 |
| 0.0288 | 41.38 | 6000 | 0.4440 | 0.3145 |
| 0.0259 | 44.83 | 6500 | 0.4691 | 0.3182 |
| 0.0203 | 48.28 | 7000 | 0.5062 | 0.3162 |
| 0.0171 | 51.72 | 7500 | 0.4762 | 0.3129 |
| 0.0166 | 55.17 | 8000 | 0.4772 | 0.3090 |
| 0.0147 | 58.62 | 8500 | 0.4730 | 0.3031 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3 |
Amloii/gpt2-reviewspanish | Amloii | 2022-05-19T08:28:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"GPT-2",
"Spanish",
"review",
"fake",
"es",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-04-26T15:11:07Z | ---
language: es
tags:
- GPT-2
- Spanish
- review
- fake
datasets:
- amazon_reviews_multi
widget:
- text: "Me ha gustado su"
example_title: "Positive review"
- text: "No quiero"
example_title: "Negative review"
license: mit
---
# GPT-2 - reviewspanish
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of text data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
In our case, we created a fined-tunned model of [Spanish GTP-2](https://huggingface.co/DeepESP/gpt2-spanish) combined with
the spanish reviews of Amazon from the HG dataset [Amazon-reviews-multi](https://huggingface.co/datasets/amazon_reviews_multi).
With this strategy, we obtain a model for text generation able to create realistic product reviews, useful for bot detection in
fake reviews.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
from transformers import pipeline, set_seed
generator = pipeline('text-generation',
model='Amloii/gpt2-reviewspanish',
tokenizer='Amloii/gpt2-reviewspanish')
set_seed(42)
generator("Me ha gustado su", max_length=30, num_return_sequences=5)
[{'generated_text': 'Me ha gustado su tamaño y la flexibilidad de las correas, al ser de plastico las hebillas que lleva para sujetar las cadenas me han quitado el'},
{'generated_text': 'Me ha gustado su color y calidad. Lo peor de todo, es que las gafas no se pegan nada. La parte de fuera es finita'},
{'generated_text': 'Me ha gustado su rapidez y los ajustes de la correa, lo único que para mí, es poco manejable. Además en el bolso tiene una goma'},
{'generated_text': 'Me ha gustado su diseño y las dimensiones, pero el material es demasiado duro. Se nota bastante el uso pero me parece un poco caro para lo que'},
{'generated_text': 'Me ha gustado su aspecto aunque para lo que yo lo quería no me ha impresionado mucho. Las hojas tienen un tacto muy agradable que hace que puedas'}]
```
|
CherylTSW/bert-finetuned-squad | CherylTSW | 2022-05-19T08:23:55Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-19T07:35:46Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CherylTSW/bert-finetuned-squad
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. -->
# CherylTSW/bert-finetuned-squad
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: 0.6669
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1902, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.9561 | 0 |
| 0.9586 | 1 |
| 0.6669 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
lightonai/RITA_l | lightonai | 2022-05-19T08:23:12Z | 24 | 0 | transformers | [
"transformers",
"pytorch",
"rita",
"text-generation",
"protein",
"custom_code",
"dataset:uniref-100",
"arxiv:2205.05789",
"autotrain_compatible",
"region:us"
] | text-generation | 2022-04-25T23:12:25Z | ---
language: protein
tags:
- protein
datasets:
- uniref-100
---
# RITA-L
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- | --- | --- | --- | --- |
[Small](https://huggingface.co/lightonai/RITA_s) | 85M | 768 | 12 | 2.31
[Medium](https://huggingface.co/lightonai/RITA_m) | 300M | 1024 | 24 | 2.01
[**Large**](https://huggingface.co/lightonai/RITA_l)| 680M | 1536 | 24 | 1.82
[XLarge](https://huggingface.co/lightonai/RITA_xl)| 1.2B | 2048 | 24 | 1.70
For full results see our preprint: https://arxiv.org/abs/2205.05789
## Usage
Instantiate a model like so:
``` python
from transformers import AutoModel, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_l, trust_remote_code=True")
tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_l")
```
for generation we support pipelines:
``` python
from transformers import pipeline
rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer)
sequences = rita_gen("MAB", max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2,
num_return_sequences=2, eos_token_id=2)
for seq in sequences:
print(f"seq: {seq['generated_text'].replace(' ', '')}")
```
## How to cite
@article{hesslow2022rita,
title={RITA: a Study on Scaling Up Generative Protein Sequence Models},
author={Hesslow, Daniel and Zanichelli, Niccol{\'o} and Notin, Pascal and Poli, Iacopo and Marks, Debora},
journal={arXiv preprint arXiv:2205.05789},
year={2022}
}
|
jesperjmb/CompundedIntros | jesperjmb | 2022-05-19T08:08:29Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"next-sentence-prediction",
"endpoints_compatible",
"region:us"
] | null | 2022-05-19T08:05:17Z | Fine-tuned KB BERT for identifying compounded introductions in the Riksdagen corpus |
swayam01/hindi-clsril-100 | swayam01 | 2022-05-19T07:45:46Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:cc",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-07T16:08:35Z | ---
language: hi
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: cc
model-index:
- name: Wav2Vec2 Hindi Model by Swayam Mittal
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice hi
type: common_voice
args: hi
metrics:
- name: Test WER
type: wer
value: 24.17
---
# hindi-clsril-100
Fine-tuned [Harveenchadha/wav2vec2-pretrained-clsril-23-10k](https://huggingface.co/Harveenchadha/wav2vec2-pretrained-clsril-23-10k) on Hindi using the [Common Voice](https://huggingface.co/datasets/common_voice), included [openSLR](http://www.openslr.org/103/) Hindi dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Evaluation
The model can be used directly (with or without a language model) as follows:
```python
#!pip install datasets==1.4.1
#!pip install transformers==4.4.0
#!pip install torchaudio
#!pip install jiwer
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("swayam01/hindi-clsril-100")
model = Wav2Vec2ForCTC.from_pretrained("swayam01/hindi-clsril-100")
test_dataset = load_dataset("common_voice", "hi", split="test")
wer = load_metric("wer")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\�\।\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
def evaluate(batch):
inputs = processor_with_lm(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
batch["pred_strings"] = transcription = processor_with_lm.batch_decode(logits.numpy()).text
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 24.17 %
## Training
The Common Voice hi `train`, `validation` were used for training, as well as openSLR hi `train`, `validation` and `test` datasets.
The script used for training can be found here [colab](https://colab.research.google.com/drive/1YL_csb3LRjqWybeyvQhZ-Hem2dtpvq_x?usp=sharing) |
Subsets and Splits