modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 06:27:01
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 542
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-06 06:26:44
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
fxmarty/20220713-h14m38s16_example_conll2003
|
fxmarty
| 2022-07-13T14:38:21Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-13T14:38:16Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **quantization_approach**: `static`
* **operators_to_quantize**: `['Add', 'MatMul']`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
# Evaluation
## Non-time metrics
| node_exclusion | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :------------------------------------------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 0.936 | 0.904 | \| | 0.944 | 0.921 | \| | 0.940 | 0.912 | \| | 0.988 | 0.984 |
| `[]` | \| | 0.936 | 0.065 | \| | 0.944 | 0.243 | \| | 0.940 | 0.103 | \| | 0.988 | 0.357 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 4, input length = 64.
| node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 114.51 | 53.59 | \| | 8.73 | 18.67 |
| `[]` | \| | 90.67 | 59.55 | \| | 11.07 | 16.87 |
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3
|
nawta
| 2022-07-13T14:03:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-13T11:47:57Z |
---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5350
- Cer: 1.2730
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4243 | 4.67 | 500 | 2.6901 | 1.1259 |
| 2.4282 | 9.35 | 1000 | 2.7495 | 1.1563 |
| 2.3377 | 14.02 | 1500 | 2.2475 | 0.9617 |
| 2.2434 | 18.69 | 2000 | 2.2765 | 1.1908 |
| 2.2731 | 23.36 | 2500 | 2.2574 | 1.1669 |
| 2.3436 | 28.04 | 3000 | 2.5350 | 1.2730 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ArneD/distilbert-base-uncased-finetuned-emotion
|
ArneD
| 2022-07-13T13:43:21Z | 5 | 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
| 2022-06-21T06:42:29Z |
---
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: 0.9218894133133121
---
<!-- 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: 0.2147
- Accuracy: 0.922
- F1: 0.9219
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8205 | 1.0 | 250 | 0.3028 | 0.909 | 0.9061 |
| 0.245 | 2.0 | 500 | 0.2147 | 0.922 | 0.9219 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
fxmarty/20220713-h13m33s02_example_conll2003
|
fxmarty
| 2022-07-13T13:33:09Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-13T13:33:02Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **quantization_approach**: `static`
* **operators_to_quantize**: `['Add', 'MatMul']`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
# Evaluation
## Non-time metrics
| node_exclusion | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :------------------------------------------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 0.936 | 0.904 | \| | 0.944 | 0.921 | \| | 0.940 | 0.912 | \| | 0.988 | 0.984 |
| `[]` | \| | 0.936 | 0.065 | \| | 0.944 | 0.243 | \| | 0.940 | 0.103 | \| | 0.988 | 0.357 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 4, input length = 64.
| node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 103.46 | 53.77 | \| | 9.67 | 18.60 |
| `[]` | \| | 90.62 | 65.86 | \| | 11.07 | 15.20 |
|
hossay/distilbert-base-uncased-finetuned-ner
|
hossay
| 2022-07-13T13:32:51Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-10T00:51:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9263064854712186
- name: Recall
type: recall
value: 0.9379125181787672
- name: F1
type: f1
value: 0.9320733740967203
- name: Accuracy
type: accuracy
value: 0.9838117781625813
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9263
- Recall: 0.9379
- F1: 0.9321
- Accuracy: 0.9838
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2418 | 1.0 | 878 | 0.0709 | 0.9168 | 0.9242 | 0.9204 | 0.9806 |
| 0.0514 | 2.0 | 1756 | 0.0622 | 0.9175 | 0.9338 | 0.9255 | 0.9826 |
| 0.0306 | 3.0 | 2634 | 0.0614 | 0.9263 | 0.9379 | 0.9321 | 0.9838 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chris1/q-FrozenLake-v1-4x4-noSlippery
|
Chris1
| 2022-07-13T13:15:57Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-13T10:45:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="Chris1/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
xichenn/distilbert-base-uncased-finetuned-emotion
|
xichenn
| 2022-07-13T12:59:22Z | 16 | 1 |
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
| 2022-06-19T13:16:57Z |
---
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: 0.924
- name: F1
type: f1
value: 0.924047984825329
---
<!-- 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: 0.2294
- Accuracy: 0.924
- F1: 0.9240
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3316 | 0.9025 | 0.8985 |
| No log | 2.0 | 500 | 0.2294 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
frahman/distilbert-base-uncased-finetuned-emotion
|
frahman
| 2022-07-13T12:58:49Z | 4 | 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
| 2022-03-02T23:29:05Z |
---
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: 0.9205
- name: F1
type: f1
value: 0.9206660865871332
---
<!-- 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: 0.2202
- Accuracy: 0.9205
- F1: 0.9207
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8234 | 1.0 | 250 | 0.3185 | 0.9025 | 0.8992 |
| 0.2466 | 2.0 | 500 | 0.2202 | 0.9205 | 0.9207 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
jordyvl/udpos28-sm-first-POS
|
jordyvl
| 2022-07-13T12:53:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:udpos28",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-13T12:33:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: udpos28-sm-first-POS
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: udpos28
type: udpos28
args: en
metrics:
- name: Precision
type: precision
value: 0.9511089206505667
- name: Recall
type: recall
value: 0.9546093116207286
- name: F1
type: f1
value: 0.9528559014062253
- name: Accuracy
type: accuracy
value: 0.9559133601686793
---
<!-- 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. -->
# udpos28-sm-first-POS
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1896
- Precision: 0.9511
- Recall: 0.9546
- F1: 0.9529
- Accuracy: 0.9559
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1696 | 1.0 | 4978 | 0.1700 | 0.9440 | 0.9464 | 0.9452 | 0.9472 |
| 0.0973 | 2.0 | 9956 | 0.1705 | 0.9487 | 0.9533 | 0.9510 | 0.9543 |
| 0.0508 | 3.0 | 14934 | 0.1896 | 0.9511 | 0.9546 | 0.9529 | 0.9559 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Sreevishnu/funnel-transformer-small-imdb
|
Sreevishnu
| 2022-07-13T12:17:17Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"funnel",
"text-classification",
"sentiment-analysis",
"en",
"dataset:imdb",
"arxiv:2006.03236",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-15T18:48:18Z |
---
license: apache-2.0
language: en
widget:
- text: "In the garden of wonderment that is the body of work by the animation master Hayao Miyazaki, his 2001 gem 'Spirited Away' is at once one of his most accessible films to a Western audience and the one most distinctly rooted in Japanese culture and lore. The tale of Chihiro, a 10 year old girl who resents being moved away from all her friends, only to find herself working in a bathhouse for the gods, doesn't just use its home country's fraught relationship with deities as a backdrop. Never remotely didactic, the film is ultimately a self-fulfilment drama that touches on religious, ethical, ecological and psychological issues.
It's also a fine children's film, the kind that elicits a deepening bond across repeat viewings and the passage of time, mostly because Miyazaki refuses to talk down to younger viewers. That's been a constant in all of his filmography, but it's particularly conspicuous here because the stakes for its young protagonist are bigger than in most of his previous features aimed at younger viewers. It involves conquering fears and finding oneself in situations where safety is not a given.
There are so many moving parts in Spirited Away, from both a thematic and technical point of view, that pinpointing what makes Spirited Away stand out from an already outstanding body of work becomes as challenging as a meeting with Yubaba. But I think it comes down to an ability to deal with heady, complex subject matter from a young girl's perspective without diluting or lessening its resonance. Miyazaki has made a loopy, demanding work of art that asks your inner child to come out and play. There are few high-wire acts in all of movie-dom as satisfying as that."
datasets:
- imdb
tags:
- sentiment-analysis
---
# Funnel Transformer small (B4-4-4 with decoder) fine-tuned on IMDB for Sentiment Analysis
These are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis with `max_position_embeddings=1024`.
The original model weights for English language are from [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) and it uses a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English.
## Fine-tuning Results
| | Accuracy | Precision | Recall | F1 |
|-------------------------------|----------|-----------|----------|----------|
| funnel-transformer-small-imdb | 0.956530 | 0.952286 | 0.961075 | 0.956661 |
## Model description (from [funnel-transformer/small](https://huggingface.co/funnel-transformer/small))
Funnel Transformer is a transformers model pretrained on a large corpus of English 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, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
# How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(
"Sreevishnu/funnel-transformer-small-imdb",
use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(
"Sreevishnu/funnel-transformer-small-imdb",
num_labels=2,
max_position_embeddings=1024)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
# Example App
https://lazy-film-reviews-7gif2bz4sa-ew.a.run.app/
Project repo: https://github.com/akshaydevml/lazy-film-reviews
|
facebook/deit-base-patch16-384
|
facebook
| 2022-07-13T11:41:03Z | 349 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained at resolution 224 and fine-tuned at resolution 384 on a large collection of images in a supervised fashion, namely ImageNet-1k.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-384')
model = ViTForImageClassification.from_pretrained('facebook/deit-base-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| **DeiT-base 384** | **82.9** | **96.2** | **87M** | **https://huggingface.co/facebook/deit-base-patch16-384** |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
facebook/deit-base-patch16-224
|
facebook
| 2022-07-13T11:40:44Z | 144,060 | 13 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-base-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| **DeiT-base** | **81.8** | **95.6** | **86M** | **https://huggingface.co/facebook/deit-base-patch16-224** |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
matjesg/deepflash2_demo
|
matjesg
| 2022-07-13T10:54:35Z | 0 | 2 | null |
[
"onnx",
"image-segmentation",
"semantic-segmentation",
"deepflash2",
"arxiv:2111.06693",
"license:apache-2.0",
"region:us"
] |
image-segmentation
| 2022-05-31T09:43:39Z |
---
tags:
- image-segmentation
- semantic-segmentation
- deepflash2
license: apache-2.0
datasets:
- "cFOS in HC"
- "YFP in CTX"
---
# Demo models for
**Try in [Hugging Face Spaces](https://huggingface.co/spaces/matjesg/deepflash2)** 🤗🤗🤗
- **Task**: Image Segmentation / Semantic Segmentation
- **Paper**: The preprint of our paper is available on [arXiv](https://arxiv.org/pdf/2111.06693.pdf)
- **Data**: The cFOS in HC dataset ([Article](https://doi.org/10.7554/eLife.59780), [Data](https://doi.org/10.5061/dryad.4b8gtht9d)) describes the indirect immunofluorescent labeling of the transcription factor cFOS in different subregions of the hippocampus after behavioral testing of the mice.
- **Library**: See [github](https://github.com/matjesg/deepflash2/)
|
fxmarty/20220713-h10m20s05_example_conll2003
|
fxmarty
| 2022-07-13T10:20:11Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-13T10:20:05Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **quantization_approach**: `static`
* **operators_to_quantize**: `['Add', 'MatMul']`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
# Evaluation
## Non-time metrics
| node_exclusion | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :------------------------------------------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 0.936 | 0.904 | \| | 0.944 | 0.921 | \| | 0.940 | 0.912 | \| | 0.988 | 0.984 |
| `[]` | \| | 0.936 | 0.065 | \| | 0.944 | 0.243 | \| | 0.940 | 0.103 | \| | 0.988 | 0.357 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 4, input length = 64.
| node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 120.53 | 46.41 | \| | 8.33 | 21.60 |
| `[]` | \| | 119.97 | 59.50 | \| | 8.40 | 16.87 |
|
jordyvl/bert-base-cased_conll2003-sm-all-ner
|
jordyvl
| 2022-07-13T10:13:33Z | 3 | 0 |
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
| 2022-07-13T09:59:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased_conll2003-sm-all-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9487479131886477
- name: Recall
type: recall
value: 0.9564119824974756
- name: F1
type: f1
value: 0.9525645323499833
- name: Accuracy
type: accuracy
value: 0.9916085822203186
---
<!-- 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-cased_conll2003-sm-all-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: 0.0489
- Precision: 0.9487
- Recall: 0.9564
- F1: 0.9526
- Accuracy: 0.9916
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.052 | 1.0 | 3511 | 0.0510 | 0.9374 | 0.9456 | 0.9415 | 0.9898 |
| 0.0213 | 2.0 | 7022 | 0.0497 | 0.9484 | 0.9519 | 0.9501 | 0.9911 |
| 0.0099 | 3.0 | 10533 | 0.0489 | 0.9487 | 0.9564 | 0.9526 | 0.9916 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nickcpk/distilbert-base-uncased-finetuned-squad-d5716d28
|
nickcpk
| 2022-07-13T09:51:40Z | 0 | 0 | null |
[
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2022-07-13T09:51:27Z |
---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
casasdorjunior/t5-small-finetuned-cc-news-es-titles
|
casasdorjunior
| 2022-07-13T08:52:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cc-news-es-titles",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-13T07:38:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cc-news-es-titles
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cc-news-es-titles
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cc-news-es-titles
type: cc-news-es-titles
args: default
metrics:
- name: Rouge1
type: rouge
value: 16.701
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cc-news-es-titles
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cc-news-es-titles dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6383
- Rouge1: 16.701
- Rouge2: 4.1265
- Rougel: 14.8175
- Rougelsum: 14.8193
- Gen Len: 18.9159
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 2.8439 | 1.0 | 23133 | 2.6383 | 16.701 | 4.1265 | 14.8175 | 14.8193 | 18.9159 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fxmarty/20220713-h08m45s49_example_squad
|
fxmarty
| 2022-07-13T08:46:02Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"region:us"
] |
question-answering
| 2022-07-13T08:45:49Z |
---
pipeline_tag: question-answering
datasets:
- squad
metrics:
- exact_match
- f1
tags:
- distilbert
---
**task**: `question-answering`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `1000`
Fixed parameters:
* **model_name_or_path**: `distilbert-base-uncased-distilled-squad`
* **dataset**:
* **path**: `squad`
* **eval_split**: `validation`
* **data_keys**: `{'question': 'question', 'context': 'context'}`
* **ref_keys**: `['answers']`
* **calibration_split**: `train`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add']`, `['Add', 'MatMul']`
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | node_exclusion | | exact_match (original) | exact_match (optimized) | | f1 (original) | f1 (optimized) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :--------------------: | :---------------------: | :-: | :-----------: | :------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 82.300 | 80.600 | \| | 87.232 | 86.097 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 82.300 | 80.600 | \| | 87.232 | 86.097 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 82.300 | 82.300 | \| | 87.232 | 87.232 |
| `dynamic` | `['Add']` | `[]` | \| | 82.300 | 82.300 | \| | 87.232 | 87.232 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 82.300 | 72.900 | \| | 87.232 | 79.964 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 82.300 | 54.500 | \| | 87.232 | 64.292 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 82.300 | 76.900 | \| | 87.232 | 83.014 |
| `static` | `['Add']` | `[]` | \| | 82.300 | 59.800 | \| | 87.232 | 69.217 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 1, input length = 32.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 47.87 | 7.23 | \| | 20.93 | 138.40 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 48.10 | 7.14 | \| | 20.80 | 140.13 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 43.83 | 17.16 | \| | 22.87 | 58.33 |
| `dynamic` | `['Add']` | `[]` | \| | 34.13 | 17.02 | \| | 29.33 | 58.80 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 35.07 | 9.21 | \| | 28.53 | 108.53 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 48.27 | 11.62 | \| | 20.73 | 86.13 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 34.11 | 19.23 | \| | 29.33 | 52.00 |
| `static` | `['Add']` | `[]` | \| | 48.54 | 21.18 | \| | 20.67 | 47.27 |
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 59.92 | 12.60 | \| | 16.73 | 79.40 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 59.64 | 13.25 | \| | 16.80 | 75.47 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 60.13 | 29.65 | \| | 16.67 | 33.73 |
| `dynamic` | `['Add']` | `[]` | \| | 59.62 | 29.51 | \| | 16.80 | 33.93 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 58.94 | 15.13 | \| | 17.00 | 66.13 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 60.49 | 18.62 | \| | 16.53 | 53.73 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 43.32 | 28.00 | \| | 23.13 | 35.73 |
| `static` | `['Add']` | `[]` | \| | 44.19 | 32.72 | \| | 22.67 | 30.60 |
Below, time metrics for batch size = 1, input length = 128.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 73.39 | 26.56 | \| | 13.67 | 37.67 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 57.64 | 23.42 | \| | 17.40 | 42.73 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 64.04 | 50.14 | \| | 15.67 | 20.00 |
| `dynamic` | `['Add']` | `[]` | \| | 72.81 | 57.05 | \| | 13.80 | 17.53 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 70.57 | 27.59 | \| | 14.20 | 36.27 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 71.04 | 37.94 | \| | 14.13 | 26.40 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 57.65 | 57.95 | \| | 17.40 | 17.27 |
| `static` | `['Add']` | `[]` | \| | 71.66 | 58.67 | \| | 14.00 | 17.07 |
Below, time metrics for batch size = 4, input length = 32.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 72.11 | 21.80 | \| | 13.93 | 45.93 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 73.15 | 20.70 | \| | 13.73 | 48.33 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 72.05 | 53.68 | \| | 13.93 | 18.67 |
| `dynamic` | `['Add']` | `[]` | \| | 55.97 | 53.60 | \| | 17.87 | 18.67 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 70.46 | 24.88 | \| | 14.20 | 40.20 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 56.57 | 30.90 | \| | 17.73 | 32.40 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 62.38 | 53.64 | \| | 16.07 | 18.67 |
| `static` | `['Add']` | `[]` | \| | 60.19 | 67.29 | \| | 16.67 | 14.87 |
Below, time metrics for batch size = 4, input length = 64.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 121.20 | 40.12 | \| | 8.27 | 24.93 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 90.97 | 41.51 | \| | 11.00 | 24.13 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 120.85 | 106.50 | \| | 8.33 | 9.40 |
| `dynamic` | `['Add']` | `[]` | \| | 118.58 | 106.55 | \| | 8.47 | 9.40 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 120.57 | 54.25 | \| | 8.33 | 18.47 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 104.93 | 57.90 | \| | 9.60 | 17.33 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 90.85 | 110.46 | \| | 11.07 | 9.07 |
| `static` | `['Add']` | `[]` | \| | 120.57 | 103.62 | \| | 8.33 | 9.67 |
Below, time metrics for batch size = 4, input length = 128.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 172.14 | 94.78 | \| | 5.87 | 10.60 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 220.38 | 84.18 | \| | 4.60 | 11.93 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 221.22 | 221.37 | \| | 4.53 | 4.53 |
| `dynamic` | `['Add']` | `[]` | \| | 203.90 | 175.16 | \| | 4.93 | 5.73 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 192.63 | 113.82 | \| | 5.20 | 8.80 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 220.32 | 122.36 | \| | 4.60 | 8.20 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 220.58 | 207.51 | \| | 4.60 | 4.87 |
| `static` | `['Add']` | `[]` | \| | 221.94 | 246.87 | \| | 4.53 | 4.07 |
Below, time metrics for batch size = 8, input length = 32.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 112.67 | 43.26 | \| | 8.93 | 23.13 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 95.78 | 40.66 | \| | 10.47 | 24.60 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 117.38 | 104.28 | \| | 8.53 | 9.60 |
| `dynamic` | `['Add']` | `[]` | \| | 89.81 | 91.00 | \| | 11.20 | 11.00 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 89.14 | 52.09 | \| | 11.27 | 19.20 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 92.77 | 64.21 | \| | 10.80 | 15.60 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 119.10 | 114.43 | \| | 8.40 | 8.80 |
| `static` | `['Add']` | `[]` | \| | 119.28 | 127.79 | \| | 8.40 | 7.87 |
Below, time metrics for batch size = 8, input length = 64.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 215.03 | 78.03 | \| | 4.67 | 12.87 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 214.76 | 87.19 | \| | 4.67 | 11.53 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 216.48 | 162.64 | \| | 4.67 | 6.20 |
| `dynamic` | `['Add']` | `[]` | \| | 204.29 | 212.33 | \| | 4.93 | 4.73 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 215.47 | 104.45 | \| | 4.67 | 9.60 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 209.66 | 106.43 | \| | 4.80 | 9.40 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 166.13 | 220.92 | \| | 6.07 | 4.53 |
| `static` | `['Add']` | `[]` | \| | 214.69 | 209.01 | \| | 4.67 | 4.80 |
Below, time metrics for batch size = 8, input length = 128.
| quantization_approach | operators_to_quantize | node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 407.90 | 151.49 | \| | 2.47 | 6.67 |
| `dynamic` | `['Add', 'MatMul']` | `[]` | \| | 407.34 | 154.55 | \| | 2.47 | 6.53 |
| `dynamic` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 406.51 | 394.85 | \| | 2.47 | 2.60 |
| `dynamic` | `['Add']` | `[]` | \| | 309.53 | 445.24 | \| | 3.27 | 2.27 |
| `static` | `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 407.54 | 224.46 | \| | 2.47 | 4.47 |
| `static` | `['Add', 'MatMul']` | `[]` | \| | 408.14 | 236.94 | \| | 2.47 | 4.27 |
| `static` | `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 309.91 | 357.87 | \| | 3.27 | 2.80 |
| `static` | `['Add']` | `[]` | \| | 310.00 | 406.54 | \| | 3.27 | 2.47 |
|
huggingartists/queen
|
huggingartists
| 2022-07-13T06:52:09Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/queen",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/queen
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/97bcb5755cb9780d76b37726a0ce4bef.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Queen</div>
<a href="https://genius.com/artists/queen">
<div style="text-align: center; font-size: 14px;">@queen</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Queen.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/queen).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/queen")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1jdprwq2/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 Queen's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2lvkoamo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2lvkoamo/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='huggingartists/queen')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/queen")
model = AutoModelWithLMHead.from_pretrained("huggingartists/queen")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
FelipeAD/mt5-small-SENTENCE_COMPRESSION
|
FelipeAD
| 2022-07-13T06:44:19Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-12T21:29:25Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: FelipeAD/mt5-small-SENTENCE_COMPRESSION
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. -->
# FelipeAD/mt5-small-SENTENCE_COMPRESSION
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1433
- Validation Loss: 0.9768
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 179848, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.6046 | 1.1992 | 0 |
| 1.3586 | 1.0826 | 1 |
| 1.2178 | 1.0241 | 2 |
| 1.1433 | 0.9768 | 3 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
abx/bert-finetuned-ner
|
abx
| 2022-07-13T06:15:23Z | 5 | 0 |
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
| 2022-07-13T06:04:39Z |
---
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: 0.9341713529606351
- name: Recall
type: recall
value: 0.9505217098619994
- name: F1
type: f1
value: 0.9422756089422756
- name: Accuracy
type: accuracy
value: 0.9861070230176017
---
<!-- 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: 0.0623
- Precision: 0.9342
- Recall: 0.9505
- F1: 0.9423
- Accuracy: 0.9861
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0865 | 1.0 | 1756 | 0.0667 | 0.9166 | 0.9379 | 0.9271 | 0.9829 |
| 0.0397 | 2.0 | 3512 | 0.0560 | 0.9337 | 0.9522 | 0.9428 | 0.9867 |
| 0.0194 | 3.0 | 5268 | 0.0623 | 0.9342 | 0.9505 | 0.9423 | 0.9861 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu116
- Datasets 2.3.2
- Tokenizers 0.12.1
|
NimaBoscarino/STPushToHub-test2
|
NimaBoscarino
| 2022-07-13T05:57:37Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-07-13T05:49:12Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# NimaBoscarino/STPushToHub-test2
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('NimaBoscarino/STPushToHub-test2')
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('NimaBoscarino/STPushToHub-test2')
model = AutoModel.from_pretrained('NimaBoscarino/STPushToHub-test2')
# 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=NimaBoscarino/STPushToHub-test2)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, '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 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"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 -->
|
sun1638650145/Reinforce-Pong-PLE-v0
|
sun1638650145
| 2022-07-13T05:32:26Z | 0 | 0 | null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-13T05:32:13Z |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pong-PLE-v0
results:
- metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
---
# 使用**Reinforce**智能体来玩**Pong-PLE-v0**
这是一个使用**Reinforce**训练有素的模型玩**Pong-PLE-v0**.
要学习使用这个模型并训练你的模型, 请查阅深度强化学习课程第5单元: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
huggingtweets/majigglydoobers
|
huggingtweets
| 2022-07-13T02:58:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-13T02:56:45Z |
---
language: en
thumbnail: http://www.huggingtweets.com/majigglydoobers/1657681081092/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/1542204712455241729/6E7rxSrt_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">doobers 👻❤️🩹</div>
<div style="text-align: center; font-size: 14px;">@majigglydoobers</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 doobers 👻❤️🩹.
| Data | doobers 👻❤️🩹 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 2046 |
| Short tweets | 199 |
| Tweets kept | 1004 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36h6xok5/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 @majigglydoobers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/emkivtny) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/emkivtny/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/majigglydoobers')
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)
|
ariesutiono/scibert-lm-const-finetuned-20
|
ariesutiono
| 2022-07-13T00:15:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-12T23:32:22Z |
---
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: scibert-lm-const-finetuned-20
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. -->
# scibert-lm-const-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0099
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6081 | 1.0 | 118 | 2.9156 |
| 2.7954 | 2.0 | 236 | 2.5940 |
| 2.5762 | 3.0 | 354 | 2.5017 |
| 2.4384 | 4.0 | 472 | 2.3923 |
| 2.3391 | 5.0 | 590 | 2.2996 |
| 2.2417 | 6.0 | 708 | 2.3180 |
| 2.2161 | 7.0 | 826 | 2.2336 |
| 2.1918 | 8.0 | 944 | 2.2465 |
| 2.1494 | 9.0 | 1062 | 2.1871 |
| 2.1215 | 10.0 | 1180 | 2.1566 |
| 2.1015 | 11.0 | 1298 | 2.1849 |
| 2.05 | 12.0 | 1416 | 2.1092 |
| 2.0653 | 13.0 | 1534 | 2.2221 |
| 2.0261 | 14.0 | 1652 | 2.1572 |
| 2.0117 | 15.0 | 1770 | 2.1452 |
| 1.9845 | 16.0 | 1888 | 2.1433 |
| 1.9791 | 17.0 | 2006 | 2.1225 |
| 1.9979 | 18.0 | 2124 | 2.0777 |
| 1.9688 | 19.0 | 2242 | 2.1765 |
| 1.9873 | 20.0 | 2360 | 2.0099 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
hugginglearners/fastai-style-transfer
|
hugginglearners
| 2022-07-13T00:15:26Z | 0 | 5 |
fastai
|
[
"fastai",
"pytorch",
"image-to-image",
"region:us"
] |
image-to-image
| 2022-07-09T14:16:38Z |
---
tags:
- fastai
- pytorch
- image-to-image
---
## Model description
This repo contains the trained model for Style transfer using vgg16 as the backbone.
Full credits go to [Nhu Hoang](https://www.linkedin.com/in/nhu-hoang/)
Motivation: Style transfer is an interesting task with an amazing outcome.
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 3e-5 |
| training_precision | float16 |
|
hugginglearners/multi-object-classification
|
hugginglearners
| 2022-07-13T00:14:55Z | 0 | 2 |
fastai
|
[
"fastai",
"image-classification",
"region:us"
] |
image-classification
| 2022-07-04T04:34:10Z |
---
tags:
- fastai
- image-classification
---
## Model description
This repo contains the trained model for Multi-object classification
Full credits go to [Nhu Hoang](https://www.linkedin.com/in/nhu-hoang/)
Motivation: Classifying multiple objects is a challenging task without using an object detection algorithm. This model was trained on resnet34 backbone and achieved a good accuracy.
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 3e-3 |
| training_precision | float16 |
|
andrewzhang505/quad-swarm-rl-1
|
andrewzhang505
| 2022-07-13T00:02:06Z | 5 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] |
reinforcement-learning
| 2022-07-12T21:09:52Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
---
A(n) **APPO** model trained on the **quadrotor_multi** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
AntiSquid/Reinforce-model-666
|
AntiSquid
| 2022-07-12T21:52:02Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T21:51:51Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-model-666
results:
- metrics:
- type: mean_reward
value: 117.10 +/- 4.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Shaier/medqa_fine_tuned_generic_bert
|
Shaier
| 2022-07-12T20:33:17Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-07-12T19:49:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: medqa_fine_tuned_generic_bert
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. -->
# medqa_fine_tuned_generic_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4239
- Accuracy: 0.2869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.3851 | 0.2594 |
| 1.3896 | 2.0 | 636 | 1.3805 | 0.2807 |
| 1.3896 | 3.0 | 954 | 1.3852 | 0.2948 |
| 1.3629 | 4.0 | 1272 | 1.3996 | 0.2980 |
| 1.3068 | 5.0 | 1590 | 1.4239 | 0.2869 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0
|
huggingtweets/dylanfromsf
|
huggingtweets
| 2022-07-12T20:29:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-12T20:29:12Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dylanfromsf/1657657784578/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/1384643526772678657/O7Sz_ZxW_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">dylan</div>
<div style="text-align: center; font-size: 14px;">@dylanfromsf</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 dylan.
| Data | dylan |
| --- | --- |
| Tweets downloaded | 1288 |
| Retweets | 116 |
| Short tweets | 420 |
| Tweets kept | 752 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2526mmm1/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 @dylanfromsf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ds3020w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ds3020w/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/dylanfromsf')
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)
|
ychenNLP/arabic-ner-ace
|
ychenNLP
| 2022-07-12T20:02:24Z | 15 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"BERT",
"token-classification",
"sequence-tagger-model",
"ar",
"en",
"dataset:ACE2005",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T18:45:26Z |
---
tags:
- BERT
- token-classification
- sequence-tagger-model
language:
- ar
- en
license: mit
datasets:
- ACE2005
---
# Arabic NER Model
- [Github repo](https://github.com/edchengg/GigaBERT)
- NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
- ACE2005 Training data: English + Arabic
- [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC
## Hyperparameters
- learning_rate=2e-5
- num_train_epochs=10
- weight_decay=0.01
## ACE2005 Evaluation results (F1)
| Language | Arabic | English |
|:----:|:-----------:|:----:|
| | 89.4 | 88.8 |
## How to use
```python
>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
>>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace")
>>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace")
>>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
>>> output = ner_pip('Protests break out across the US after Supreme Court overturns.')
>>> print(output)
[{'entity_group': 'GPE', 'score': 0.9979881, 'word': 'us', 'start': 30, 'end': 32}, {'entity_group': 'ORG', 'score': 0.99898684, 'word': 'supreme court', 'start': 39, 'end': 52}]
>>> output = ner_pip('قال وزير العدل التركي بكير بوزداغ إن أنقرة تريد 12 مشتبهاً بهم من فنلندا و 21 من السويد')
>>> print(output)
[{'entity_group': 'PER', 'score': 0.9996214, 'word': 'وزير', 'start': 4, 'end': 8}, {'entity_group': 'ORG', 'score': 0.9952383, 'word': 'العدل', 'start': 9, 'end': 14}, {'entity_group': 'GPE', 'score': 0.9996675, 'word': 'التركي', 'start': 15, 'end': 21}, {'entity_group': 'PER', 'score': 0.9978992, 'word': 'بكير بوزداغ', 'start': 22, 'end': 33}, {'entity_group': 'GPE', 'score': 0.9997154, 'word': 'انقرة', 'start': 37, 'end': 42}, {'entity_group': 'PER', 'score': 0.9946885, 'word': 'مشتبها بهم', 'start': 51, 'end': 62}, {'entity_group': 'GPE', 'score': 0.99967396, 'word': 'فنلندا', 'start': 66, 'end': 72}, {'entity_group': 'PER', 'score': 0.99694425, 'word': '21', 'start': 75, 'end': 77}, {'entity_group': 'GPE', 'score': 0.99963355, 'word': 'السويد', 'start': 81, 'end': 87}]
```
### BibTeX entry and citation info
```bibtex
@inproceedings{lan2020gigabert,
author = {Lan, Wuwei and Chen, Yang and Xu, Wei and Ritter, Alan},
title = {Giga{BERT}: Zero-shot Transfer Learning from {E}nglish to {A}rabic},
booktitle = {Proceedings of The 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP)},
year = {2020}
}
```
|
didi27/bloom-edu
|
didi27
| 2022-07-12T17:57:21Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-07-12T17:57:16Z |
---
license: bigscience-bloom-rail-1.0
---
|
huggingtweets/masonhaggerty
|
huggingtweets
| 2022-07-12T17:17:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-12T16:48:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/masonhaggerty/1657646221015/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/1410026132121047041/LiYev7vQ_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">Mason Haggerty</div>
<div style="text-align: center; font-size: 14px;">@masonhaggerty</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 Mason Haggerty.
| Data | Mason Haggerty |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 71 |
| Short tweets | 82 |
| Tweets kept | 632 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jpav9nmg/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 @masonhaggerty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz/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/masonhaggerty')
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)
|
Li-Tang/rare-puppers
|
Li-Tang
| 2022-07-12T16:57:55Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-12T16:57:42Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
zluvolyote/s288cExpressionPrediction_k6
|
zluvolyote
| 2022-07-12T16:54:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-12T16:02:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: s288cExpressionPrediction_k6
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. -->
# s288cExpressionPrediction_k6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4418
- Accuracy: 0.8067
- F1: 0.7882
## 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: 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 58 | 0.5315 | 0.7278 | 0.7572 |
| No log | 2.0 | 116 | 0.4604 | 0.7853 | 0.7841 |
| No log | 3.0 | 174 | 0.4418 | 0.8067 | 0.7882 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
reachrkr/TEST2ppo-LunarLander-v2
|
reachrkr
| 2022-07-12T16:20:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T16:20:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 266.96 +/- 25.94
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
...
```
|
fxmarty/20220712-h16m02s58_example_beans
|
fxmarty
| 2022-07-12T16:03:03Z | 0 | 0 | null |
[
"tensorboard",
"vit",
"image-classification",
"dataset:beans",
"region:us"
] |
image-classification
| 2022-07-12T16:02:58Z |
---
pipeline_tag: image-classification
datasets:
- beans
metrics:
- accuracy
tags:
- vit
---
**task**: `image-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `nateraw/vit-base-beans`
* **dataset**:
* **path**: `beans`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'image'}`
* **ref_keys**: `['labels']`
* **calibration_split**: `train`
* **quantization_approach**: `dynamic`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **operators_to_quantize**: `['Add']`, `['Add', 'MatMul']`
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
* **per_channel**: `False`, `True`
# Evaluation
## Non-time metrics
| operators_to_quantize | node_exclusion | per_channel | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-----------------: | :------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 0.980 | 0.980 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 0.980 | 0.980 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 0.980 | 0.980 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 0.980 | 0.980 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 0.980 | 0.980 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 0.980 | 0.980 |
| `['Add']` | `[]` | `False` | \| | 0.980 | 0.980 |
| `['Add']` | `[]` | `True` | \| | 0.980 | 0.980 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 1, input length = 32.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 200.50 | 63.00 | \| | 5.00 | 15.93 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 198.19 | 72.65 | \| | 5.07 | 13.80 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 191.44 | 63.27 | \| | 5.27 | 15.87 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 154.84 | 72.51 | \| | 6.47 | 13.80 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 155.84 | 130.95 | \| | 6.47 | 7.67 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 201.76 | 131.25 | \| | 5.00 | 7.67 |
| `['Add']` | `[]` | `False` | \| | 198.96 | 128.82 | \| | 5.07 | 7.80 |
| `['Add']` | `[]` | `True` | \| | 163.76 | 129.62 | \| | 6.13 | 7.73 |
Below, time metrics for batch size = 1, input length = 64.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 162.75 | 67.18 | \| | 6.20 | 14.93 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 159.69 | 72.77 | \| | 6.33 | 13.80 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 183.10 | 64.02 | \| | 5.47 | 15.67 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 157.21 | 64.16 | \| | 6.40 | 15.60 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 155.32 | 130.74 | \| | 6.47 | 7.67 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 198.56 | 162.51 | \| | 5.07 | 6.20 |
| `['Add']` | `[]` | `False` | \| | 186.58 | 163.38 | \| | 5.40 | 6.13 |
| `['Add']` | `[]` | `True` | \| | 199.75 | 131.46 | \| | 5.07 | 7.67 |
Below, time metrics for batch size = 1, input length = 128.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 160.58 | 67.65 | \| | 6.27 | 14.80 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 158.60 | 72.53 | \| | 6.33 | 13.80 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 200.46 | 62.95 | \| | 5.00 | 15.93 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 195.39 | 72.28 | \| | 5.13 | 13.87 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 197.59 | 128.80 | \| | 5.07 | 7.80 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 156.24 | 162.63 | \| | 6.47 | 6.20 |
| `['Add']` | `[]` | `False` | \| | 157.25 | 129.13 | \| | 6.40 | 7.80 |
| `['Add']` | `[]` | `True` | \| | 176.08 | 161.79 | \| | 5.73 | 6.20 |
Below, time metrics for batch size = 4, input length = 32.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 503.83 | 219.62 | \| | 2.00 | 4.60 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 603.26 | 266.15 | \| | 1.67 | 3.80 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 654.79 | 217.45 | \| | 1.53 | 4.60 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 654.33 | 219.54 | \| | 1.53 | 4.60 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 654.20 | 481.61 | \| | 1.53 | 2.13 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 609.81 | 632.73 | \| | 1.67 | 1.60 |
| `['Add']` | `[]` | `False` | \| | 588.86 | 602.91 | \| | 1.73 | 1.67 |
| `['Add']` | `[]` | `True` | \| | 666.98 | 655.32 | \| | 1.53 | 1.53 |
Below, time metrics for batch size = 4, input length = 64.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 656.87 | 216.32 | \| | 1.53 | 4.67 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 507.24 | 265.62 | \| | 2.00 | 3.80 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 655.36 | 219.61 | \| | 1.53 | 4.60 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 613.28 | 220.96 | \| | 1.67 | 4.53 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 656.30 | 652.72 | \| | 1.53 | 1.53 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 521.09 | 472.90 | \| | 1.93 | 2.13 |
| `['Add']` | `[]` | `False` | \| | 655.37 | 473.77 | \| | 1.53 | 2.13 |
| `['Add']` | `[]` | `True` | \| | 653.62 | 468.82 | \| | 1.53 | 2.13 |
Below, time metrics for batch size = 4, input length = 128.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 654.24 | 216.82 | \| | 1.53 | 4.67 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 657.16 | 240.11 | \| | 1.53 | 4.20 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 504.14 | 217.47 | \| | 2.00 | 4.60 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 655.94 | 220.12 | \| | 1.53 | 4.60 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 653.99 | 479.06 | \| | 1.53 | 2.13 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 642.48 | 666.28 | \| | 1.60 | 1.53 |
| `['Add']` | `[]` | `False` | \| | 656.34 | 661.24 | \| | 1.53 | 1.53 |
| `['Add']` | `[]` | `True` | \| | 661.86 | 472.49 | \| | 1.53 | 2.13 |
Below, time metrics for batch size = 8, input length = 32.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 1294.07 | 472.54 | \| | 0.80 | 2.13 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 1287.58 | 542.72 | \| | 0.80 | 1.87 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 1033.37 | 433.32 | \| | 1.00 | 2.33 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 1030.14 | 542.36 | \| | 1.00 | 1.87 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 953.27 | 926.14 | \| | 1.07 | 1.13 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 1173.01 | 995.22 | \| | 0.87 | 1.07 |
| `['Add']` | `[]` | `False` | \| | 1280.07 | 926.97 | \| | 0.80 | 1.13 |
| `['Add']` | `[]` | `True` | \| | 1283.70 | 927.87 | \| | 0.80 | 1.13 |
Below, time metrics for batch size = 8, input length = 64.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 1273.61 | 435.27 | \| | 0.80 | 2.33 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 1157.00 | 542.75 | \| | 0.87 | 1.87 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 968.85 | 537.65 | \| | 1.07 | 1.87 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 1107.66 | 472.53 | \| | 0.93 | 2.13 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 1270.30 | 1092.10 | \| | 0.80 | 0.93 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 1263.29 | 1012.66 | \| | 0.80 | 1.00 |
| `['Add']` | `[]` | `False` | \| | 1007.19 | 1331.12 | \| | 1.07 | 0.80 |
| `['Add']` | `[]` | `True` | \| | 1286.51 | 1317.96 | \| | 0.80 | 0.80 |
Below, time metrics for batch size = 8, input length = 128.
| operators_to_quantize | node_exclusion | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :------------------------------------------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 1188.98 | 537.58 | \| | 0.87 | 1.87 |
| `['Add', 'MatMul']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 951.31 | 489.40 | \| | 1.07 | 2.07 |
| `['Add', 'MatMul']` | `[]` | `False` | \| | 1278.73 | 537.52 | \| | 0.80 | 1.87 |
| `['Add', 'MatMul']` | `[]` | `True` | \| | 1005.38 | 440.01 | \| | 1.07 | 2.33 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `False` | \| | 1265.55 | 1304.51 | \| | 0.80 | 0.80 |
| `['Add']` | `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | `True` | \| | 1186.54 | 934.09 | \| | 0.87 | 1.13 |
| `['Add']` | `[]` | `False` | \| | 1276.38 | 1319.84 | \| | 0.80 | 0.80 |
| `['Add']` | `[]` | `True` | \| | 981.81 | 940.69 | \| | 1.07 | 1.07 |
|
MarLac/wav2vec2-base-timit-demo-google-colab
|
MarLac
| 2022-07-12T15:41:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-12T08:24:30Z |
---
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: 0.5816
- Wer: 0.3533
## 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: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.243 | 0.5 | 500 | 1.0798 | 0.7752 |
| 0.834 | 1.01 | 1000 | 0.6206 | 0.5955 |
| 0.5503 | 1.51 | 1500 | 0.5387 | 0.5155 |
| 0.4548 | 2.01 | 2000 | 0.4660 | 0.4763 |
| 0.3412 | 2.51 | 2500 | 0.8381 | 0.4836 |
| 0.3128 | 3.02 | 3000 | 0.4818 | 0.4519 |
| 0.2547 | 3.52 | 3500 | 0.4415 | 0.4230 |
| 0.2529 | 4.02 | 4000 | 0.4624 | 0.4219 |
| 0.2103 | 4.52 | 4500 | 0.4714 | 0.4096 |
| 0.2102 | 5.03 | 5000 | 0.4968 | 0.4087 |
| 0.1838 | 5.53 | 5500 | 0.4643 | 0.4131 |
| 0.1721 | 6.03 | 6000 | 0.4676 | 0.3979 |
| 0.1548 | 6.53 | 6500 | 0.4765 | 0.4085 |
| 0.1595 | 7.04 | 7000 | 0.4797 | 0.3941 |
| 0.1399 | 7.54 | 7500 | 0.4753 | 0.3902 |
| 0.1368 | 8.04 | 8000 | 0.4697 | 0.3945 |
| 0.1276 | 8.54 | 8500 | 0.5438 | 0.3869 |
| 0.1255 | 9.05 | 9000 | 0.5660 | 0.3841 |
| 0.1077 | 9.55 | 9500 | 0.4964 | 0.3947 |
| 0.1197 | 10.05 | 10000 | 0.5349 | 0.3849 |
| 0.1014 | 10.55 | 10500 | 0.5558 | 0.3883 |
| 0.0949 | 11.06 | 11000 | 0.5673 | 0.3785 |
| 0.0882 | 11.56 | 11500 | 0.5589 | 0.3955 |
| 0.0906 | 12.06 | 12000 | 0.5752 | 0.4120 |
| 0.1064 | 12.56 | 12500 | 0.5080 | 0.3727 |
| 0.0854 | 13.07 | 13000 | 0.5398 | 0.3798 |
| 0.0754 | 13.57 | 13500 | 0.5237 | 0.3816 |
| 0.0791 | 14.07 | 14000 | 0.4967 | 0.3725 |
| 0.0731 | 14.57 | 14500 | 0.5287 | 0.3744 |
| 0.0719 | 15.08 | 15000 | 0.5633 | 0.3596 |
| 0.062 | 15.58 | 15500 | 0.5399 | 0.3752 |
| 0.0681 | 16.08 | 16000 | 0.5151 | 0.3759 |
| 0.0559 | 16.58 | 16500 | 0.5564 | 0.3709 |
| 0.0533 | 17.09 | 17000 | 0.5933 | 0.3743 |
| 0.0563 | 17.59 | 17500 | 0.5381 | 0.3670 |
| 0.0527 | 18.09 | 18000 | 0.5685 | 0.3731 |
| 0.0492 | 18.59 | 18500 | 0.5728 | 0.3725 |
| 0.0509 | 19.1 | 19000 | 0.6074 | 0.3807 |
| 0.0436 | 19.6 | 19500 | 0.5762 | 0.3628 |
| 0.0434 | 20.1 | 20000 | 0.6721 | 0.3729 |
| 0.0416 | 20.6 | 20500 | 0.5842 | 0.3700 |
| 0.0431 | 21.11 | 21000 | 0.5374 | 0.3607 |
| 0.037 | 21.61 | 21500 | 0.5556 | 0.3667 |
| 0.036 | 22.11 | 22000 | 0.5608 | 0.3592 |
| 0.04 | 22.61 | 22500 | 0.5272 | 0.3637 |
| 0.047 | 23.12 | 23000 | 0.5234 | 0.3625 |
| 0.0506 | 23.62 | 23500 | 0.5427 | 0.3629 |
| 0.0418 | 24.12 | 24000 | 0.5590 | 0.3626 |
| 0.037 | 24.62 | 24500 | 0.5615 | 0.3555 |
| 0.0429 | 25.13 | 25000 | 0.5806 | 0.3616 |
| 0.045 | 25.63 | 25500 | 0.5777 | 0.3639 |
| 0.0283 | 26.13 | 26000 | 0.5987 | 0.3617 |
| 0.0253 | 26.63 | 26500 | 0.5671 | 0.3551 |
| 0.032 | 27.14 | 27000 | 0.5464 | 0.3582 |
| 0.0321 | 27.64 | 27500 | 0.5634 | 0.3573 |
| 0.0274 | 28.14 | 28000 | 0.5513 | 0.3575 |
| 0.0245 | 28.64 | 28500 | 0.5745 | 0.3537 |
| 0.0251 | 29.15 | 29000 | 0.5759 | 0.3547 |
| 0.0222 | 29.65 | 29500 | 0.5816 | 0.3533 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
andreaschandra/xlm-roberta-base-finetuned-panx-fr
|
andreaschandra
| 2022-07-12T15:30:15Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-12T15:15:58Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.9275221167113059
---
<!-- 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-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1059
- F1: 0.9275
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5416 | 1.0 | 191 | 0.2322 | 0.8378 |
| 0.2614 | 2.0 | 382 | 0.1544 | 0.8866 |
| 0.1758 | 3.0 | 573 | 0.1059 | 0.9275 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Kuro96/q-FrozenLake-v1-4x4-noSlippery
|
Kuro96
| 2022-07-12T14:35:27Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T14:35:21Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="Kuro96/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Kuro96/q-Taxi-v3
|
Kuro96
| 2022-07-12T14:25:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T14:25:52Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.76
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="/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"])
```
|
hugginglearners/pokemon-card-checker
|
hugginglearners
| 2022-07-12T13:21:57Z | 0 | 1 |
fastai
|
[
"fastai",
"resnet",
"computer-vision",
"classification",
"image-classification",
"binary-classification",
"license:cc0-1.0",
"region:us"
] |
image-classification
| 2022-06-20T14:02:07Z |
---
tags:
- fastai
- resnet
- computer-vision
- classification
- image-classification
- binary-classification
license:
- cc0-1.0
---
# Resnet34 Pokemon Card Classifier
## Model Description
This is a resnet34 model fine-tuned with fastai to [classify real and fake Pokemon cards (dataset)](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
Here is a colab notebook that shows how the model was trained and pushed to the hub: [link](https://github.com/mindwrapped/pokemon-card-checker/blob/main/pokemon_card_checker.ipynb).
## Intended uses & limitation
This model is trained to identify real vs fake cards based on the backs of the cards, not the front.
## How to use
```python
from huggingface_hub import from_pretrained_fastai
# Pull model from hub
learn = from_pretrained_fastai('hugginglearners/pokemon-card-checker')
# Get prediction for this image
pred_label, _, scores = learn.predict(img)
```
## Training data
Dataset located here: [link](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
|
cffl/bart-base-styletransfer-subjective-to-neutral
|
cffl
| 2022-07-12T11:58:08Z | 286 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1911.09709",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-01T18:41:46Z |
---
license: apache-2.0
---
# bart-base-styletransfer-subjective-to-neutral
## Model description
This [facebook/bart-base](https://huggingface.co/facebook/bart-base) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to transfer style in text from subjectively biased to neutrally toned.
The development and modeling efforts that produced this model are documented in detail through [this blog series](https://blog.fastforwardlabs.com/2022/05/05/neutralizing-subjectivity-bias-with-huggingface-transformers.html).
## Intended uses & limitations
The model is intended purely as a research output for NLP and data science communities. We imagine this model will be used by researchers to better understand the limitations, robustness, and generalization of text style transfer models. Ultimately, we hope this model will inspire future work on text style transfer and serve as a benchmarking tool for the style attribute of subjectivity bias, specifically.
Any production use of this model - whether commercial or not - is currently not intended. This is because, as [the team at OpenAI points out](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases), large langauge models like BART reflect biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans, unless the deployers first carry out a study of biases relevant to the intended use-case. Neither the model nor the WNC dataset has been sufficiently evaluated for performance and bias. Our efforts quantified model performance using two custom evaluation metrics, neither of which have been correlated to human evaluation for the task.
As we discuss in the blog series, since the WNC is a parallel dataset and we formulate the learning task as a supervised problem, the model indirectly adopts Wikipedia's NPOV policy as the definition for "neutrality" and "subjectivity". The NPOV policy may not fully reflect an end users assumed/intended meaning of subjectivity because the notion of subjectivity itself can be...well, subjective.
We discovered through our exploratory work that the WNC does contain data quality issues that will contribute to unintended bias in the model. For example, some NPOV revisions introduce factual information outside the context of the prompt as a means to correct bias. We believe these factual based edits are out of scope for a subjective-to-neutral style transfer modeling task, but exist here nonetheless.
## How to use
This model can be used directly with a HuggingFace pipeline for `text2text-generation`.
```python
>>> from transformers import pipeline
>>> styletransfer = pipeline(
task="text2text-generation",
model="cffl/bart-base-styletransfer-subjective-to-neutral",
max_length=200,
)
>>> input_text = "chemical abstracts service (cas), a prominent division of the american chemical society, is the world's leading source of chemical information."
>>> styletransfer(input_text)
[{'generated_text': 'chemical abstracts service (cas), a division of the american chemical society, is a source of chemical information.'}]
```
## Training procedure
For modeling, we made extensive use of the Huggingface transformers library by initializing the [BartForConditionalGeneration](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartForConditionalGeneration) model with [facebook/bart-base](https://huggingface.co/facebook/bart-base) pretrained weights and adapting the [summarization fine-tuning script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) for our TST-specific needs. We fine-tune the model for 15 epochs on an NVIDIA Tesla V100 GPU with a batch size of 32. (Note that when fine-tuning the model with the parallel examples, the noising function is turned off so an uncorrupted document is passed to BART's encoder and decoder.)
Please refer to [our blog series](https://blog.fastforwardlabs.com/2022/05/05/neutralizing-subjectivity-bias-with-huggingface-transformers.html) for a discussion of evaluation metrics and results.
|
cffl/bert-base-styleclassification-subjective-neutral
|
cffl
| 2022-07-12T11:57:42Z | 2,297 | 8 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1911.09709",
"arxiv:1703.01365",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-01T19:35:53Z |
---
license: apache-2.0
---
# bert-base-styleclassification-subjective-neutral
## Model description
This [bert-base-uncased](https://huggingface.co/bert-base-uncased) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to classify text as subjectively biased vs. neutrally toned.
The development and modeling efforts that produced this model are documented in detail through [this blog series](https://blog.fastforwardlabs.com/2022/05/05/neutralizing-subjectivity-bias-with-huggingface-transformers.html).
## Intended uses & limitations
The model is intended purely as a research output for NLP and data science communities. We developed this model for the purpose of evaluating text style transfer output. Specifically, we derive a Style Transfer Intensity (STI) metric from the classifier's output distributions. We also extract feautre importances from the model via [Integrated Gradients](https://arxiv.org/pdf/1703.01365.pdf) with support a Content Preservation Score (CPS).
We imagine this model will be used by researchers to better understand the limitations, robustness, and generalization of text style transfer models. Ultimately, we hope this model will inspire future work on text style transfer and serve as a benchmarking tool for the style attribute of subjectivity bias, specifically.
Any production use of this model - whether commercial or not - is currently not intended. This is because, as [the team at OpenAI points out](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases), large langauge models like BERT reflect biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans, unless the deployers first carry out a study of biases relevant to the intended use-case. Neither the model nor the WNC dataset has been sufficiently evaluated for performance and bias.
As we discuss in the blog series, since the WNC is a parallel dataset and we formulate the learning task as a supervised problem, the model indirectly adopts Wikipedia's NPOV policy as the definition for "neutrality" and "subjectivity". The NPOV policy may not fully reflect an end users assumed/intended meaning of subjectivity because the notion of subjectivity itself can be...well, subjective.
We discovered through our exploratory work that the WNC does contain data quality issues that will contribute to unintended bias in the model. For example, some NPOV revisions introduce factual information outside the context of the prompt as a means to correct bias. We believe these factual based edits are out of scope for a subjective-to-neutral style transfer modeling task, but exist here nonetheless.
## How to use
This model can be used directly with a HuggingFace pipeline for `text2text-generation`.
```python
>>> from transformers import pipeline
>>> classify = pipeline(
task="text-classification",
model="cffl/bert-base-styleclassification-subjective-neutral",
return_all_scores=True,
)
>>> input_text = "chemical abstracts service (cas), a prominent division of the american chemical society, is the world's leading source of chemical information."
>>> classify(input_text)
[[{'label': 'SUBJECTIVE', 'score': 0.9765084385871887},
{'label': 'NEUTRAL', 'score': 0.023491567000746727}]]
```
## Training procedure
For training, we initialize HuggingFace’s [AutoModelforSequenceClassification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSequenceClassification) with [bert-base-uncased](https://huggingface.co/bert-base-uncased) pre-trained weights and perform a hyperparameter search over: batch size [16, 32], learning rate [3e-05, 3e-06, 3e-07], weight decay [0, 0.01, 0.1] and batch shuffling [True, False] while training for 15 epochs.
We monitor performance using accuracy as we have a perfectly balanced dataset and assign equal cost to false positives and false negatives. The best performing model produces an overall accuracy of 72.50% -- please reference our [training script](https://github.com/fastforwardlabs/text-style-transfer/blob/main/scripts/train/classifier/train_classifier.py) and [classifier evaluation notebook](https://github.com/fastforwardlabs/text-style-transfer/blob/main/notebooks/WNC_full_style_classifier_evaluation.ipynb) for further details.
|
Vikasbhandari/wav2vec2-train
|
Vikasbhandari
| 2022-07-12T11:51:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-12T11:11:37Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-large-960h-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.9
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.9
---
# Wav2Vec2-Large-960h-Lv60 + Self-Training
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-large-960h-lv60-self** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
def map_to_pred(batch):
inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 1.9 | 3.9 |
|
MiguelCosta/finetuning-sentiment-model-24000-samples
|
MiguelCosta
| 2022-07-12T10:48:14Z | 5 | 0 |
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
| 2022-07-12T06:17:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-24000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9266666666666666
- name: F1
type: f1
value: 0.9273927392739274
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-24000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3505
- Accuracy: 0.9267
- F1: 0.9274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
paola-md/recipe-distilbert-upper-tIs
|
paola-md
| 2022-07-12T10:28:07Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-12T07:36:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-distilbert-upper-tIs
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. -->
# recipe-distilbert-upper-tIs
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8746
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.67 | 1.0 | 1353 | 1.2945 |
| 1.2965 | 2.0 | 2706 | 1.1547 |
| 1.1904 | 3.0 | 4059 | 1.0846 |
| 1.1272 | 4.0 | 5412 | 1.0407 |
| 1.0857 | 5.0 | 6765 | 1.0039 |
| 1.0549 | 6.0 | 8118 | 0.9802 |
| 1.03 | 7.0 | 9471 | 0.9660 |
| 1.01 | 8.0 | 10824 | 0.9474 |
| 0.9931 | 9.0 | 12177 | 0.9365 |
| 0.9807 | 10.0 | 13530 | 0.9252 |
| 0.9691 | 11.0 | 14883 | 0.9105 |
| 0.9601 | 12.0 | 16236 | 0.9079 |
| 0.9503 | 13.0 | 17589 | 0.8979 |
| 0.9436 | 14.0 | 18942 | 0.8930 |
| 0.9371 | 15.0 | 20295 | 0.8875 |
| 0.9322 | 16.0 | 21648 | 0.8851 |
| 0.9279 | 17.0 | 23001 | 0.8801 |
| 0.9254 | 18.0 | 24354 | 0.8812 |
| 0.9227 | 19.0 | 25707 | 0.8768 |
| 0.9232 | 20.0 | 27060 | 0.8746 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained
|
nawta
| 2022-07-12T10:20:53Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-12T05:31:38Z |
---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained
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-onomatopoeia-finetune_smalldata_ESC50pretrained
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2963
- Cer: 0.9002
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3287 | 23.81 | 500 | 2.2963 | 0.9002 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
luke-thorburn/suggest-objections-bias-only
|
luke-thorburn
| 2022-07-12T10:08:02Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate objections to a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
List objections to the claim that: [original claim]
Objections:
* [objection 1]
* [objection 2]
...
* [objection n]
* [generated objection]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
|
luke-thorburn/suggest-reasons-bias-only
|
luke-thorburn
| 2022-07-12T10:07:19Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate reasons that support a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
List reasons why: [original claim]
Reasons:
* [reason 1]
* [reason 2]
...
* [reason n]
* [generated reason]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
|
luke-thorburn/suggest-intermediary-claims-full-finetune
|
luke-thorburn
| 2022-07-12T09:56:47Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate a chain of reasoning from one claim to another
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
Input: [start claim] -> [end claim]
Output: [start claim] -> [generated intermediate claim 1] -> ... -> [generated intermediate claim n] -> [end claim]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
|
luke-thorburn/suggest-intermediary-claims-soft
|
luke-thorburn
| 2022-07-12T09:48:47Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate a chain of reasoning from one claim to another
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
[prepended soft prompt][start claim] -> [end claim]
Answer: [start claim] -> [generated intermediate claim 1] -> ... -> [generated intermediate claim n] -> [end claim]
```
# Dataset
The soft prompt was trained using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
|
luke-thorburn/suggest-objections-soft
|
luke-thorburn
| 2022-07-12T09:43:28Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate objections to a claim
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
[prepended soft prompt][original claim]
Cons:
- [objection 1]
- [objection 2]
...
- [objection n]
- [generated objection]
```
# Dataset
The soft prompt was trained using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
|
robingeibel/led-base-16384-finetuned-big_patent
|
robingeibel
| 2022-07-12T09:28:33Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"led",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-10T08:18:34Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: led-base-16384-finetuned-big_patent
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. -->
# led-base-16384-finetuned-big_patent
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fxmarty/20220712-h08m05s32_
|
fxmarty
| 2022-07-12T08:05:37Z | 0 | 0 | null |
[
"tensorboard",
"vit",
"image-classification",
"dataset:beans",
"region:us"
] |
image-classification
| 2022-07-12T08:05:32Z |
---
pipeline_tag: image-classification
datasets:
- beans
metrics:
- accuracy
tags:
- vit
---
**task**: `image-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `nateraw/vit-base-beans`
* **dataset**:
* **path**: `beans`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'image'}`
* **ref_keys**: `['labels']`
* **quantization_approach**: `dynamic`
* **node_exclusion**: `[]`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`, `[]`
* **per_channel**: `False`, `True`
# Evaluation
## Non-time metrics
| operators_to_quantize | per_channel | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :---------: | :-: | :-----------------: | :------------------: |
| `['Add', 'MatMul']` | `False` | \| | 0.980 | 0.980 |
| `['Add', 'MatMul']` | `True` | \| | 0.980 | 0.980 |
| `['Add']` | `False` | \| | 0.980 | 0.980 |
| `['Add']` | `True` | \| | 0.980 | 0.980 |
| `[]` | `False` | \| | 0.980 | 0.980 |
| `[]` | `True` | \| | 0.980 | 0.980 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 1, input length = 32.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 201.25 | 70.30 | \| | 5.00 | 14.27 |
| `['Add', 'MatMul']` | `True` | \| | 203.52 | 72.48 | \| | 4.93 | 13.80 |
| `['Add']` | `False` | \| | 166.03 | 150.93 | \| | 6.07 | 6.67 |
| `['Add']` | `True` | \| | 200.82 | 163.17 | \| | 5.00 | 6.13 |
| `[]` | `False` | \| | 190.99 | 162.06 | \| | 5.27 | 6.20 |
| `[]` | `True` | \| | 155.15 | 162.52 | \| | 6.47 | 6.20 |
Below, time metrics for batch size = 1, input length = 64.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 165.85 | 70.60 | \| | 6.07 | 14.20 |
| `['Add', 'MatMul']` | `True` | \| | 161.41 | 72.71 | \| | 6.20 | 13.80 |
| `['Add']` | `False` | \| | 200.45 | 129.40 | \| | 5.00 | 7.73 |
| `['Add']` | `True` | \| | 154.68 | 136.42 | \| | 6.47 | 7.40 |
| `[]` | `False` | \| | 166.97 | 162.15 | \| | 6.00 | 6.20 |
| `[]` | `True` | \| | 166.32 | 162.81 | \| | 6.07 | 6.20 |
Below, time metrics for batch size = 1, input length = 128.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 199.48 | 70.98 | \| | 5.07 | 14.13 |
| `['Add', 'MatMul']` | `True` | \| | 199.65 | 71.78 | \| | 5.07 | 13.93 |
| `['Add']` | `False` | \| | 199.08 | 137.97 | \| | 5.07 | 7.27 |
| `['Add']` | `True` | \| | 189.93 | 162.45 | \| | 5.33 | 6.20 |
| `[]` | `False` | \| | 191.63 | 162.54 | \| | 5.27 | 6.20 |
| `[]` | `True` | \| | 200.38 | 162.55 | \| | 5.00 | 6.20 |
Below, time metrics for batch size = 4, input length = 32.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 655.84 | 243.33 | \| | 1.53 | 4.13 |
| `['Add', 'MatMul']` | `True` | \| | 661.27 | 221.16 | \| | 1.53 | 4.53 |
| `['Add']` | `False` | \| | 662.84 | 529.28 | \| | 1.53 | 1.93 |
| `['Add']` | `True` | \| | 512.47 | 470.66 | \| | 2.00 | 2.13 |
| `[]` | `False` | \| | 562.81 | 501.77 | \| | 1.80 | 2.00 |
| `[]` | `True` | \| | 505.81 | 521.20 | \| | 2.00 | 1.93 |
Below, time metrics for batch size = 4, input length = 64.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 654.58 | 258.54 | \| | 1.53 | 3.93 |
| `['Add', 'MatMul']` | `True` | \| | 617.44 | 234.05 | \| | 1.67 | 4.33 |
| `['Add']` | `False` | \| | 661.51 | 478.81 | \| | 1.53 | 2.13 |
| `['Add']` | `True` | \| | 657.01 | 660.23 | \| | 1.53 | 1.53 |
| `[]` | `False` | \| | 661.64 | 474.28 | \| | 1.53 | 2.13 |
| `[]` | `True` | \| | 661.29 | 471.09 | \| | 1.53 | 2.13 |
Below, time metrics for batch size = 4, input length = 128.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 654.80 | 219.38 | \| | 1.53 | 4.60 |
| `['Add', 'MatMul']` | `True` | \| | 663.50 | 222.37 | \| | 1.53 | 4.53 |
| `['Add']` | `False` | \| | 625.56 | 529.02 | \| | 1.60 | 1.93 |
| `['Add']` | `True` | \| | 655.08 | 499.41 | \| | 1.53 | 2.07 |
| `[]` | `False` | \| | 655.92 | 473.01 | \| | 1.53 | 2.13 |
| `[]` | `True` | \| | 505.54 | 659.92 | \| | 2.00 | 1.53 |
Below, time metrics for batch size = 8, input length = 32.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 968.83 | 443.80 | \| | 1.07 | 2.27 |
| `['Add', 'MatMul']` | `True` | \| | 1255.70 | 489.55 | \| | 0.80 | 2.07 |
| `['Add']` | `False` | \| | 1301.35 | 938.14 | \| | 0.80 | 1.07 |
| `['Add']` | `True` | \| | 1279.54 | 931.91 | \| | 0.80 | 1.13 |
| `[]` | `False` | \| | 1292.66 | 1318.07 | \| | 0.80 | 0.80 |
| `[]` | `True` | \| | 1290.35 | 1314.74 | \| | 0.80 | 0.80 |
Below, time metrics for batch size = 8, input length = 64.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 1305.45 | 438.06 | \| | 0.80 | 2.33 |
| `['Add', 'MatMul']` | `True` | \| | 1296.68 | 450.40 | \| | 0.80 | 2.27 |
| `['Add']` | `False` | \| | 968.21 | 949.81 | \| | 1.07 | 1.07 |
| `['Add']` | `True` | \| | 1012.35 | 1317.46 | \| | 1.00 | 0.80 |
| `[]` | `False` | \| | 1213.91 | 961.79 | \| | 0.87 | 1.07 |
| `[]` | `True` | \| | 956.39 | 945.41 | \| | 1.07 | 1.07 |
Below, time metrics for batch size = 8, input length = 128.
| operators_to_quantize | per_channel | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :---------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['Add', 'MatMul']` | `False` | \| | 1120.12 | 497.17 | \| | 0.93 | 2.07 |
| `['Add', 'MatMul']` | `True` | \| | 1289.50 | 443.46 | \| | 0.80 | 2.27 |
| `['Add']` | `False` | \| | 1294.65 | 930.97 | \| | 0.80 | 1.13 |
| `['Add']` | `True` | \| | 1181.21 | 933.82 | \| | 0.87 | 1.13 |
| `[]` | `False` | \| | 1245.61 | 1318.07 | \| | 0.87 | 0.80 |
| `[]` | `True` | \| | 1285.81 | 1318.82 | \| | 0.80 | 0.80 |
|
fxmarty/20220712-h08m02s04_example
|
fxmarty
| 2022-07-12T08:02:09Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-12T08:02:04Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 0.936 | 0.935 | \| | 0.944 | 0.943 | \| | 0.940 | 0.939 | \| | 0.988 | 0.988 |
| `dynamic` | `['Add']` | \| | 0.936 | 0.936 | \| | 0.944 | 0.944 | \| | 0.940 | 0.940 | \| | 0.988 | 0.988 |
| `static` | `['Add', 'MatMul']` | \| | 0.936 | 0.063 | \| | 0.944 | 0.246 | \| | 0.940 | 0.100 | \| | 0.988 | 0.343 |
| `static` | `['Add']` | \| | 0.936 | 0.050 | \| | 0.944 | 0.160 | \| | 0.940 | 0.076 | \| | 0.988 | 0.311 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 1, input length = 32.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 46.38 | 9.96 | \| | 21.60 | 100.47 |
| `dynamic` | `['Add']` | \| | 36.59 | 13.98 | \| | 27.33 | 71.60 |
| `static` | `['Add', 'MatMul']` | \| | 33.84 | 14.46 | \| | 29.60 | 69.20 |
| `static` | `['Add']` | \| | 33.23 | 20.11 | \| | 30.13 | 49.73 |
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 58.92 | 19.68 | \| | 17.00 | 50.87 |
| `dynamic` | `['Add']` | \| | 58.59 | 24.81 | \| | 17.13 | 40.33 |
| `static` | `['Add', 'MatMul']` | \| | 51.41 | 29.36 | \| | 19.47 | 34.07 |
| `static` | `['Add']` | \| | 44.22 | 38.56 | \| | 22.67 | 25.93 |
Below, time metrics for batch size = 1, input length = 128.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 72.38 | 36.47 | \| | 13.87 | 27.47 |
| `dynamic` | `['Add']` | \| | 70.21 | 46.30 | \| | 14.27 | 21.60 |
| `static` | `['Add', 'MatMul']` | \| | 70.76 | 48.24 | \| | 14.13 | 20.80 |
| `static` | `['Add']` | \| | 72.47 | 71.10 | \| | 13.80 | 14.07 |
Below, time metrics for batch size = 4, input length = 32.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 69.76 | 38.50 | \| | 14.40 | 26.00 |
| `dynamic` | `['Add']` | \| | 56.02 | 51.32 | \| | 17.87 | 19.53 |
| `static` | `['Add', 'MatMul']` | \| | 55.05 | 46.80 | \| | 18.20 | 21.40 |
| `static` | `['Add']` | \| | 71.03 | 56.82 | \| | 14.13 | 17.67 |
Below, time metrics for batch size = 4, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 119.91 | 61.51 | \| | 8.40 | 16.27 |
| `dynamic` | `['Add']` | \| | 108.43 | 105.65 | \| | 9.27 | 9.47 |
| `static` | `['Add', 'MatMul']` | \| | 119.89 | 86.76 | \| | 8.40 | 11.53 |
| `static` | `['Add']` | \| | 96.99 | 102.03 | \| | 10.33 | 9.87 |
Below, time metrics for batch size = 4, input length = 128.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 219.78 | 123.71 | \| | 4.60 | 8.13 |
| `dynamic` | `['Add']` | \| | 220.13 | 187.21 | \| | 4.60 | 5.40 |
| `static` | `['Add', 'MatMul']` | \| | 186.39 | 176.99 | \| | 5.40 | 5.67 |
| `static` | `['Add']` | \| | 219.57 | 203.71 | \| | 4.60 | 4.93 |
Below, time metrics for batch size = 8, input length = 32.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 118.32 | 59.22 | \| | 8.47 | 16.93 |
| `dynamic` | `['Add']` | \| | 116.52 | 80.17 | \| | 8.60 | 12.53 |
| `static` | `['Add', 'MatMul']` | \| | 116.59 | 83.55 | \| | 8.60 | 12.00 |
| `static` | `['Add']` | \| | 115.81 | 126.53 | \| | 8.67 | 7.93 |
Below, time metrics for batch size = 8, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 172.71 | 117.89 | \| | 5.80 | 8.53 |
| `dynamic` | `['Add']` | \| | 166.05 | 156.99 | \| | 6.07 | 6.40 |
| `static` | `['Add', 'MatMul']` | \| | 215.00 | 148.93 | \| | 4.67 | 6.73 |
| `static` | `['Add']` | \| | 214.55 | 200.16 | \| | 4.67 | 5.00 |
Below, time metrics for batch size = 8, input length = 128.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 403.69 | 307.36 | \| | 2.53 | 3.27 |
| `dynamic` | `['Add']` | \| | 372.85 | 317.53 | \| | 2.73 | 3.20 |
| `static` | `['Add', 'MatMul']` | \| | 352.18 | 320.85 | \| | 2.87 | 3.13 |
| `static` | `['Add']` | \| | 403.55 | 410.17 | \| | 2.53 | 2.47 |
|
ArneD/xlm-roberta-base-finetuned-panx-all
|
ArneD
| 2022-07-12T07:50:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-12T06:47:20Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset (EN, FR, DE, IT).
It achieves the following results on the evaluation set:
- Loss: 0.1769
- F1: 0.8535
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2934 | 1.0 | 835 | 0.1853 | 0.8250 |
| 0.1569 | 2.0 | 1670 | 0.1714 | 0.8438 |
| 0.1008 | 3.0 | 2505 | 0.1769 | 0.8535 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
fxmarty/20220712-h07m20s32_example_conll2003
|
fxmarty
| 2022-07-12T07:20:37Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-12T07:20:32Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': 'avx512_vnni'}`
**Number of evaluation samples:** `1000`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 0.937 | 0.937 | \| | 0.953 | 0.953 | \| | 0.945 | 0.945 | \| | 0.988 | 0.988 |
| `dynamic` | `['Add']` | \| | 0.937 | 0.937 | \| | 0.953 | 0.953 | \| | 0.945 | 0.945 | \| | 0.988 | 0.988 |
| `static` | `['Add', 'MatMul']` | \| | 0.937 | 0.074 | \| | 0.953 | 0.253 | \| | 0.945 | 0.114 | \| | 0.988 | 0.363 |
| `static` | `['Add']` | \| | 0.937 | 0.065 | \| | 0.953 | 0.186 | \| | 0.945 | 0.096 | \| | 0.988 | 0.340 |
## Time metrics
Time benchmarks were run for 3 seconds per config.
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 57.64 | 12.30 | \| | 17.67 | 81.33 |
| `dynamic` | `['Add']` | \| | 43.51 | 29.42 | \| | 23.00 | 34.00 |
| `static` | `['Add', 'MatMul']` | \| | 43.05 | 21.11 | \| | 23.33 | 47.67 |
| `static` | `['Add']` | \| | 43.50 | 37.93 | \| | 23.00 | 26.67 |
Below, time metrics for batch size = 4, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 119.50 | 39.92 | \| | 8.67 | 25.33 |
| `dynamic` | `['Add']` | \| | 119.62 | 107.42 | \| | 8.67 | 9.33 |
| `static` | `['Add', 'MatMul']` | \| | 120.23 | 56.94 | \| | 8.33 | 17.67 |
| `static` | `['Add']` | \| | 119.10 | 130.78 | \| | 8.67 | 7.67 |
Below, time metrics for batch size = 8, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 165.84 | 75.45 | \| | 6.33 | 13.33 |
| `dynamic` | `['Add']` | \| | 214.65 | 211.41 | \| | 4.67 | 5.00 |
| `static` | `['Add', 'MatMul']` | \| | 166.53 | 129.00 | \| | 6.33 | 8.00 |
| `static` | `['Add']` | \| | 214.81 | 256.95 | \| | 4.67 | 4.00 |
|
AntiSquid/TEST2ppo-LunarLander-v2
|
AntiSquid
| 2022-07-12T07:10:57Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T21:53:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 285.66 +/- 15.86
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
...
```
|
sun1638650145/Reinforce-Pixelcopter-PLE-v0
|
sun1638650145
| 2022-07-12T07:06:35Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T07:06:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 13.30 +/- 9.12
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# 使用**Reinforce**智能体来玩**Pixelcopter-PLE-v0**
这是一个使用**Reinforce**训练有素的模型玩**Pixelcopter-PLE-v0**.
要学习使用这个模型并训练你的模型, 请查阅深度强化学习课程第5单元: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
botika/Indobert-QA-finetuned-squad
|
botika
| 2022-07-12T06:55:08Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-27T08:19:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Indobert-QA-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. -->
# Indobert-QA-finetuned-squad
This model is a fine-tuned version of [Rifky/Indobert-QA](https://huggingface.co/Rifky/Indobert-QA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 15.2477
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.1181 | 1.0 | 5510 | 4.8523 |
| 0.9746 | 2.0 | 11020 | 5.4560 |
| 0.8135 | 3.0 | 16530 | 5.7017 |
| 0.6964 | 4.0 | 22040 | 6.2898 |
| 0.6052 | 5.0 | 27550 | 6.0962 |
| 0.512 | 6.0 | 33060 | 6.4996 |
| 0.4303 | 7.0 | 38570 | 6.9570 |
| 0.3532 | 8.0 | 44080 | 7.4206 |
| 0.3199 | 9.0 | 49590 | 7.4004 |
| 0.4247 | 10.0 | 55100 | 6.9846 |
| 0.3641 | 11.0 | 60610 | 6.8940 |
| 0.3277 | 12.0 | 66120 | 7.0796 |
| 0.2899 | 13.0 | 71630 | 7.4511 |
| 0.2794 | 14.0 | 77140 | 7.2660 |
| 0.2496 | 15.0 | 82650 | 7.9774 |
| 0.2299 | 16.0 | 88160 | 7.6985 |
| 0.2082 | 17.0 | 93670 | 7.8321 |
| 0.1975 | 18.0 | 99180 | 8.1735 |
| 0.1784 | 19.0 | 104690 | 8.5620 |
| 0.1675 | 20.0 | 110200 | 8.7616 |
| 0.1613 | 21.0 | 115710 | 8.8350 |
| 0.1484 | 22.0 | 121220 | 8.9582 |
| 0.1482 | 23.0 | 126730 | 9.0406 |
| 0.1381 | 24.0 | 132240 | 8.9652 |
| 0.1411 | 25.0 | 137750 | 9.4613 |
| 0.1236 | 26.0 | 143260 | 9.6738 |
| 0.1216 | 27.0 | 148770 | 9.8708 |
| 0.1192 | 28.0 | 154280 | 10.3220 |
| 0.12 | 29.0 | 159790 | 10.0470 |
| 0.1041 | 30.0 | 165300 | 10.6753 |
| 0.1055 | 31.0 | 170810 | 10.2775 |
| 0.1083 | 32.0 | 176320 | 10.4515 |
| 0.0924 | 33.0 | 181830 | 10.2080 |
| 0.0959 | 34.0 | 187340 | 10.8958 |
| 0.0978 | 35.0 | 192850 | 10.8256 |
| 0.0865 | 36.0 | 198360 | 11.6631 |
| 0.0825 | 37.0 | 203870 | 11.9017 |
| 0.0807 | 38.0 | 209380 | 11.4407 |
| 0.0674 | 39.0 | 214890 | 11.5917 |
| 0.0809 | 40.0 | 220400 | 11.4535 |
| 0.0708 | 41.0 | 225910 | 12.1592 |
| 0.0778 | 42.0 | 231420 | 12.0278 |
| 0.0726 | 43.0 | 236930 | 11.7701 |
| 0.0627 | 44.0 | 242440 | 12.2976 |
| 0.0681 | 45.0 | 247950 | 12.7727 |
| 0.0672 | 46.0 | 253460 | 12.8623 |
| 0.0608 | 47.0 | 258970 | 12.9669 |
| 0.067 | 48.0 | 264480 | 13.4741 |
| 0.0625 | 49.0 | 269990 | 13.6245 |
| 0.0585 | 50.0 | 275500 | 13.4891 |
| 0.0568 | 51.0 | 281010 | 13.4374 |
| 0.0583 | 52.0 | 286520 | 12.8947 |
| 0.0467 | 53.0 | 292030 | 13.6060 |
| 0.0416 | 54.0 | 297540 | 14.3267 |
| 0.0504 | 55.0 | 303050 | 13.7715 |
| 0.0431 | 56.0 | 308560 | 13.8461 |
| 0.0412 | 57.0 | 314070 | 13.7060 |
| 0.0383 | 58.0 | 319580 | 14.3548 |
| 0.0327 | 59.0 | 325090 | 14.4535 |
| 0.0448 | 60.0 | 330600 | 14.2505 |
| 0.0409 | 61.0 | 336110 | 13.8177 |
| 0.0332 | 62.0 | 341620 | 13.0098 |
| 0.0345 | 63.0 | 347130 | 13.8678 |
| 0.0267 | 64.0 | 352640 | 14.3916 |
| 0.0302 | 65.0 | 358150 | 14.1668 |
| 0.0292 | 66.0 | 363660 | 13.6313 |
| 0.0302 | 67.0 | 369170 | 14.1120 |
| 0.0265 | 68.0 | 374680 | 15.0709 |
| 0.0276 | 69.0 | 380190 | 14.6093 |
| 0.0223 | 70.0 | 385700 | 15.0999 |
| 0.0306 | 71.0 | 391210 | 15.1224 |
| 0.0281 | 72.0 | 396720 | 15.5029 |
| 0.019 | 73.0 | 402230 | 15.3474 |
| 0.02 | 74.0 | 407740 | 14.7976 |
| 0.018 | 75.0 | 413250 | 15.3104 |
| 0.0184 | 76.0 | 418760 | 15.3137 |
| 0.0171 | 77.0 | 424270 | 14.8188 |
| 0.0164 | 78.0 | 429780 | 15.4378 |
| 0.0165 | 79.0 | 435290 | 15.1186 |
| 0.0168 | 80.0 | 440800 | 14.7998 |
| 0.0115 | 81.0 | 446310 | 14.4591 |
| 0.0138 | 82.0 | 451820 | 15.2517 |
| 0.0117 | 83.0 | 457330 | 14.7899 |
| 0.0118 | 84.0 | 462840 | 15.5304 |
| 0.0119 | 85.0 | 468350 | 14.6794 |
| 0.0134 | 86.0 | 473860 | 14.5271 |
| 0.0076 | 87.0 | 479370 | 15.7098 |
| 0.0076 | 88.0 | 484880 | 14.2286 |
| 0.01 | 89.0 | 490390 | 15.2608 |
| 0.0094 | 90.0 | 495900 | 14.9055 |
| 0.0069 | 91.0 | 501410 | 14.8540 |
| 0.0082 | 92.0 | 506920 | 15.2562 |
| 0.0068 | 93.0 | 512430 | 14.9342 |
| 0.0047 | 94.0 | 517940 | 15.3755 |
| 0.0062 | 95.0 | 523450 | 15.2753 |
| 0.0046 | 96.0 | 528960 | 15.0191 |
| 0.0057 | 97.0 | 534470 | 14.9508 |
| 0.0033 | 98.0 | 539980 | 15.4440 |
| 0.0045 | 99.0 | 545490 | 15.4171 |
| 0.0048 | 100.0 | 551000 | 15.2477 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
thunlp/Lawformer
|
thunlp
| 2022-07-12T06:23:13Z | 155 | 19 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org/legal/Lawformer.zip).
### Easy Start
We have uploaded our model to the huggingface model hub. Make sure you have installed transformers.
```python
>>> from transformers import AutoModel, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("thunlp/Lawformer")
>>> model = AutoModel.from_pretrained("thunlp/Lawformer")
>>> inputs = tokenizer("任某提起诉讼，请求判令解除婚姻关系并对夫妻共同财产进行分割。", return_tensors="pt")
>>> outputs = model(**inputs)
```
### Cite
If you use the pre-trained models, please cite this paper:
```
@article{xiao2021lawformer,
title={Lawformer: A Pre-trained Language Model forChinese Legal Long Documents},
author={Xiao, Chaojun and Hu, Xueyu and Liu, Zhiyuan and Tu, Cunchao and Sun, Maosong},
year={2021}
}
```
|
MiguelCosta/finetuning-sentiment-model-3000-samples
|
MiguelCosta
| 2022-07-12T06:06:41Z | 3 | 0 |
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
| 2022-07-12T04:48:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.8810289389067525
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5805
- Accuracy: 0.8767
- F1: 0.8810
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-6
|
Evelyn18
| 2022-07-12T05:05:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T04:49:13Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-6
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. -->
# legalectra-small-spanish-becasv3-6
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8441
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.6469 |
| No log | 2.0 | 10 | 5.5104 |
| No log | 3.0 | 15 | 5.4071 |
| No log | 4.0 | 20 | 5.3313 |
| No log | 5.0 | 25 | 5.2629 |
| No log | 6.0 | 30 | 5.1972 |
| No log | 7.0 | 35 | 5.1336 |
| No log | 8.0 | 40 | 5.0667 |
| No log | 9.0 | 45 | 5.0030 |
| No log | 10.0 | 50 | 4.9302 |
| No log | 11.0 | 55 | 4.8646 |
| No log | 12.0 | 60 | 4.7963 |
| No log | 13.0 | 65 | 4.7328 |
| No log | 14.0 | 70 | 4.6735 |
| No log | 15.0 | 75 | 4.6258 |
| No log | 16.0 | 80 | 4.5869 |
| No log | 17.0 | 85 | 4.5528 |
| No log | 18.0 | 90 | 4.5177 |
| No log | 19.0 | 95 | 4.4916 |
| No log | 20.0 | 100 | 4.4685 |
| No log | 21.0 | 105 | 4.4371 |
| No log | 22.0 | 110 | 4.4271 |
| No log | 23.0 | 115 | 4.3905 |
| No log | 24.0 | 120 | 4.3931 |
| No log | 25.0 | 125 | 4.3902 |
| No log | 26.0 | 130 | 4.3772 |
| No log | 27.0 | 135 | 4.3981 |
| No log | 28.0 | 140 | 4.4463 |
| No log | 29.0 | 145 | 4.4501 |
| No log | 30.0 | 150 | 4.4654 |
| No log | 31.0 | 155 | 4.4069 |
| No log | 32.0 | 160 | 4.4108 |
| No log | 33.0 | 165 | 4.4394 |
| No log | 34.0 | 170 | 4.4320 |
| No log | 35.0 | 175 | 4.3541 |
| No log | 36.0 | 180 | 4.4534 |
| No log | 37.0 | 185 | 4.2616 |
| No log | 38.0 | 190 | 4.2474 |
| No log | 39.0 | 195 | 4.4358 |
| No log | 40.0 | 200 | 4.3060 |
| No log | 41.0 | 205 | 4.1866 |
| No log | 42.0 | 210 | 4.2735 |
| No log | 43.0 | 215 | 4.2739 |
| No log | 44.0 | 220 | 4.1812 |
| No log | 45.0 | 225 | 4.2484 |
| No log | 46.0 | 230 | 4.3706 |
| No log | 47.0 | 235 | 4.3487 |
| No log | 48.0 | 240 | 4.2805 |
| No log | 49.0 | 245 | 4.3180 |
| No log | 50.0 | 250 | 4.3574 |
| No log | 51.0 | 255 | 4.2823 |
| No log | 52.0 | 260 | 4.0643 |
| No log | 53.0 | 265 | 4.0729 |
| No log | 54.0 | 270 | 4.2368 |
| No log | 55.0 | 275 | 4.2845 |
| No log | 56.0 | 280 | 4.1009 |
| No log | 57.0 | 285 | 4.0629 |
| No log | 58.0 | 290 | 4.1250 |
| No log | 59.0 | 295 | 4.2048 |
| No log | 60.0 | 300 | 4.2412 |
| No log | 61.0 | 305 | 4.1653 |
| No log | 62.0 | 310 | 4.1433 |
| No log | 63.0 | 315 | 4.1309 |
| No log | 64.0 | 320 | 4.1381 |
| No log | 65.0 | 325 | 4.2162 |
| No log | 66.0 | 330 | 4.1858 |
| No log | 67.0 | 335 | 4.1342 |
| No log | 68.0 | 340 | 4.1247 |
| No log | 69.0 | 345 | 4.1701 |
| No log | 70.0 | 350 | 4.1915 |
| No log | 71.0 | 355 | 4.1356 |
| No log | 72.0 | 360 | 4.1766 |
| No log | 73.0 | 365 | 4.1296 |
| No log | 74.0 | 370 | 4.0594 |
| No log | 75.0 | 375 | 4.0601 |
| No log | 76.0 | 380 | 4.0328 |
| No log | 77.0 | 385 | 3.9978 |
| No log | 78.0 | 390 | 4.0070 |
| No log | 79.0 | 395 | 4.0519 |
| No log | 80.0 | 400 | 4.1000 |
| No log | 81.0 | 405 | 3.9550 |
| No log | 82.0 | 410 | 3.9159 |
| No log | 83.0 | 415 | 3.9494 |
| No log | 84.0 | 420 | 4.0546 |
| No log | 85.0 | 425 | 4.2223 |
| No log | 86.0 | 430 | 4.2665 |
| No log | 87.0 | 435 | 3.8892 |
| No log | 88.0 | 440 | 3.7763 |
| No log | 89.0 | 445 | 3.8576 |
| No log | 90.0 | 450 | 4.0089 |
| No log | 91.0 | 455 | 4.1495 |
| No log | 92.0 | 460 | 4.1545 |
| No log | 93.0 | 465 | 4.0164 |
| No log | 94.0 | 470 | 3.9175 |
| No log | 95.0 | 475 | 3.9308 |
| No log | 96.0 | 480 | 3.9658 |
| No log | 97.0 | 485 | 3.9856 |
| No log | 98.0 | 490 | 3.9691 |
| No log | 99.0 | 495 | 3.9082 |
| 3.2873 | 100.0 | 500 | 3.8736 |
| 3.2873 | 101.0 | 505 | 3.8963 |
| 3.2873 | 102.0 | 510 | 3.9391 |
| 3.2873 | 103.0 | 515 | 3.9408 |
| 3.2873 | 104.0 | 520 | 3.9075 |
| 3.2873 | 105.0 | 525 | 3.8258 |
| 3.2873 | 106.0 | 530 | 3.7917 |
| 3.2873 | 107.0 | 535 | 3.7981 |
| 3.2873 | 108.0 | 540 | 3.8272 |
| 3.2873 | 109.0 | 545 | 3.8655 |
| 3.2873 | 110.0 | 550 | 3.8234 |
| 3.2873 | 111.0 | 555 | 3.7126 |
| 3.2873 | 112.0 | 560 | 3.6981 |
| 3.2873 | 113.0 | 565 | 3.7327 |
| 3.2873 | 114.0 | 570 | 3.8470 |
| 3.2873 | 115.0 | 575 | 4.0036 |
| 3.2873 | 116.0 | 580 | 4.0412 |
| 3.2873 | 117.0 | 585 | 4.0487 |
| 3.2873 | 118.0 | 590 | 4.0524 |
| 3.2873 | 119.0 | 595 | 4.0375 |
| 3.2873 | 120.0 | 600 | 3.9971 |
| 3.2873 | 121.0 | 605 | 3.8959 |
| 3.2873 | 122.0 | 610 | 3.8834 |
| 3.2873 | 123.0 | 615 | 3.9279 |
| 3.2873 | 124.0 | 620 | 3.9374 |
| 3.2873 | 125.0 | 625 | 3.9515 |
| 3.2873 | 126.0 | 630 | 3.9625 |
| 3.2873 | 127.0 | 635 | 3.9635 |
| 3.2873 | 128.0 | 640 | 3.9596 |
| 3.2873 | 129.0 | 645 | 3.8871 |
| 3.2873 | 130.0 | 650 | 3.8307 |
| 3.2873 | 131.0 | 655 | 3.8318 |
| 3.2873 | 132.0 | 660 | 3.8403 |
| 3.2873 | 133.0 | 665 | 3.8560 |
| 3.2873 | 134.0 | 670 | 3.8650 |
| 3.2873 | 135.0 | 675 | 3.8734 |
| 3.2873 | 136.0 | 680 | 3.8756 |
| 3.2873 | 137.0 | 685 | 3.8613 |
| 3.2873 | 138.0 | 690 | 3.8447 |
| 3.2873 | 139.0 | 695 | 3.8362 |
| 3.2873 | 140.0 | 700 | 3.8328 |
| 3.2873 | 141.0 | 705 | 3.8350 |
| 3.2873 | 142.0 | 710 | 3.8377 |
| 3.2873 | 143.0 | 715 | 3.8399 |
| 3.2873 | 144.0 | 720 | 3.8414 |
| 3.2873 | 145.0 | 725 | 3.8422 |
| 3.2873 | 146.0 | 730 | 3.8435 |
| 3.2873 | 147.0 | 735 | 3.8437 |
| 3.2873 | 148.0 | 740 | 3.8437 |
| 3.2873 | 149.0 | 745 | 3.8440 |
| 3.2873 | 150.0 | 750 | 3.8441 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-5
|
Evelyn18
| 2022-07-12T04:45:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T04:43:31Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-5
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. -->
# legalectra-small-spanish-becasv3-5
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7020
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.7715 |
| No log | 2.0 | 10 | 5.7001 |
| No log | 3.0 | 15 | 5.6206 |
| No log | 4.0 | 20 | 5.5463 |
| No log | 5.0 | 25 | 5.4866 |
| No log | 6.0 | 30 | 5.4369 |
| No log | 7.0 | 35 | 5.3939 |
| No log | 8.0 | 40 | 5.3545 |
| No log | 9.0 | 45 | 5.3168 |
| No log | 10.0 | 50 | 5.2824 |
| No log | 11.0 | 55 | 5.2504 |
| No log | 12.0 | 60 | 5.2193 |
| No log | 13.0 | 65 | 5.1864 |
| No log | 14.0 | 70 | 5.1515 |
| No log | 15.0 | 75 | 5.1174 |
| No log | 16.0 | 80 | 5.0839 |
| No log | 17.0 | 85 | 5.0497 |
| No log | 18.0 | 90 | 5.0188 |
| No log | 19.0 | 95 | 4.9937 |
| No log | 20.0 | 100 | 4.9726 |
| No log | 21.0 | 105 | 4.9483 |
| No log | 22.0 | 110 | 4.9205 |
| No log | 23.0 | 115 | 4.8993 |
| No log | 24.0 | 120 | 4.8802 |
| No log | 25.0 | 125 | 4.8612 |
| No log | 26.0 | 130 | 4.8498 |
| No log | 27.0 | 135 | 4.8294 |
| No log | 28.0 | 140 | 4.8176 |
| No log | 29.0 | 145 | 4.8144 |
| No log | 30.0 | 150 | 4.8012 |
| No log | 31.0 | 155 | 4.7890 |
| No log | 32.0 | 160 | 4.7745 |
| No log | 33.0 | 165 | 4.7641 |
| No log | 34.0 | 170 | 4.7558 |
| No log | 35.0 | 175 | 4.7474 |
| No log | 36.0 | 180 | 4.7384 |
| No log | 37.0 | 185 | 4.7319 |
| No log | 38.0 | 190 | 4.7262 |
| No log | 39.0 | 195 | 4.7225 |
| No log | 40.0 | 200 | 4.7201 |
| No log | 41.0 | 205 | 4.7165 |
| No log | 42.0 | 210 | 4.7129 |
| No log | 43.0 | 215 | 4.7111 |
| No log | 44.0 | 220 | 4.7086 |
| No log | 45.0 | 225 | 4.7060 |
| No log | 46.0 | 230 | 4.7049 |
| No log | 47.0 | 235 | 4.7036 |
| No log | 48.0 | 240 | 4.7028 |
| No log | 49.0 | 245 | 4.7023 |
| No log | 50.0 | 250 | 4.7020 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-4
|
Evelyn18
| 2022-07-12T04:38:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T04:36:14Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-4
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. -->
# legalectra-small-spanish-becasv3-4
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.6625 |
| No log | 2.0 | 10 | 5.4940 |
| No log | 3.0 | 15 | 5.3886 |
| No log | 4.0 | 20 | 5.3004 |
| No log | 5.0 | 25 | 5.2210 |
| No log | 6.0 | 30 | 5.1434 |
| No log | 7.0 | 35 | 5.0546 |
| No log | 8.0 | 40 | 4.9726 |
| No log | 9.0 | 45 | 4.9227 |
| No log | 10.0 | 50 | 4.8344 |
| No log | 11.0 | 55 | 4.7749 |
| No log | 12.0 | 60 | 4.7381 |
| No log | 13.0 | 65 | 4.7016 |
| No log | 14.0 | 70 | 4.6581 |
| No log | 15.0 | 75 | 4.6231 |
| No log | 16.0 | 80 | 4.5900 |
| No log | 17.0 | 85 | 4.5446 |
| No log | 18.0 | 90 | 4.5041 |
| No log | 19.0 | 95 | 4.4635 |
| No log | 20.0 | 100 | 4.4356 |
| No log | 21.0 | 105 | 4.3985 |
| No log | 22.0 | 110 | 4.3650 |
| No log | 23.0 | 115 | 4.3540 |
| No log | 24.0 | 120 | 4.3270 |
| No log | 25.0 | 125 | 4.2873 |
| No log | 26.0 | 130 | 4.2808 |
| No log | 27.0 | 135 | 4.2623 |
| No log | 28.0 | 140 | 4.2466 |
| No log | 29.0 | 145 | 4.2488 |
| No log | 30.0 | 150 | 4.2410 |
| No log | 31.0 | 155 | 4.2187 |
| No log | 32.0 | 160 | 4.2000 |
| No log | 33.0 | 165 | 4.1883 |
| No log | 34.0 | 170 | 4.1803 |
| No log | 35.0 | 175 | 4.1773 |
| No log | 36.0 | 180 | 4.1652 |
| No log | 37.0 | 185 | 4.1614 |
| No log | 38.0 | 190 | 4.1609 |
| No log | 39.0 | 195 | 4.1652 |
| No log | 40.0 | 200 | 4.1560 |
| No log | 41.0 | 205 | 4.1435 |
| No log | 42.0 | 210 | 4.1463 |
| No log | 43.0 | 215 | 4.1434 |
| No log | 44.0 | 220 | 4.1340 |
| No log | 45.0 | 225 | 4.1259 |
| No log | 46.0 | 230 | 4.1212 |
| No log | 47.0 | 235 | 4.1224 |
| No log | 48.0 | 240 | 4.1257 |
| No log | 49.0 | 245 | 4.1284 |
| No log | 50.0 | 250 | 4.1290 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Saraswati/q-FrozenLake-v1-4x4-noSlippery
|
Saraswati
| 2022-07-12T04:25:49Z | 0 | 1 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T04:25:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="Saraswati/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Evelyn18/legalectra-small-spanish-becasv3-1
|
Evelyn18
| 2022-07-12T03:54:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T03:49:49Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-1
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. -->
# legalectra-small-spanish-becasv3-1
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5694
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 8 | 5.8980 |
| No log | 2.0 | 16 | 5.8136 |
| No log | 3.0 | 24 | 5.7452 |
| No log | 4.0 | 32 | 5.6940 |
| No log | 5.0 | 40 | 5.6554 |
| No log | 6.0 | 48 | 5.6241 |
| No log | 7.0 | 56 | 5.5997 |
| No log | 8.0 | 64 | 5.5830 |
| No log | 9.0 | 72 | 5.5730 |
| No log | 10.0 | 80 | 5.5694 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
paola-md/recipe-distilbert-upper-Is
|
paola-md
| 2022-07-12T03:03:14Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-12T00:16:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-distilbert-upper-Is
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. -->
# recipe-distilbert-upper-Is
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8565
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6309 | 1.0 | 1305 | 1.2607 |
| 1.2639 | 2.0 | 2610 | 1.1291 |
| 1.1592 | 3.0 | 3915 | 1.0605 |
| 1.0987 | 4.0 | 5220 | 1.0128 |
| 1.0569 | 5.0 | 6525 | 0.9796 |
| 1.0262 | 6.0 | 7830 | 0.9592 |
| 1.0032 | 7.0 | 9135 | 0.9352 |
| 0.9815 | 8.0 | 10440 | 0.9186 |
| 0.967 | 9.0 | 11745 | 0.9086 |
| 0.9532 | 10.0 | 13050 | 0.8973 |
| 0.9436 | 11.0 | 14355 | 0.8888 |
| 0.9318 | 12.0 | 15660 | 0.8835 |
| 0.9243 | 13.0 | 16965 | 0.8748 |
| 0.9169 | 14.0 | 18270 | 0.8673 |
| 0.9117 | 15.0 | 19575 | 0.8610 |
| 0.9066 | 16.0 | 20880 | 0.8562 |
| 0.9028 | 17.0 | 22185 | 0.8566 |
| 0.901 | 18.0 | 23490 | 0.8583 |
| 0.8988 | 19.0 | 24795 | 0.8557 |
| 0.8958 | 20.0 | 26100 | 0.8565 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/hhelafifi
|
huggingtweets
| 2022-07-12T02:49:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-12T02:32:46Z |
---
language: en
thumbnail: http://www.huggingtweets.com/hhelafifi/1657594186366/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/1147337070920097793/06CZyryx_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">Hussein</div>
<div style="text-align: center; font-size: 14px;">@hhelafifi</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 Hussein.
| Data | Hussein |
| --- | --- |
| Tweets downloaded | 820 |
| Retweets | 191 |
| Short tweets | 95 |
| Tweets kept | 534 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1j7uxays/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 @hhelafifi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20d5foa3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20d5foa3/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/hhelafifi')
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)
|
nateraw/yolov6s
|
nateraw
| 2022-07-12T02:01:18Z | 0 | 0 |
pytorch
|
[
"pytorch",
"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"region:us"
] |
object-detection
| 2022-07-08T04:01:40Z |
---
language: en
license: gpl-3.0
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6s
---
# Model Card for yolov6s
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6t](https://hf.co/nateraw/yolov6t), [yolov6n](https://hf.co/nateraw/yolov6n)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
nateraw/yolov6t
|
nateraw
| 2022-07-12T02:01:04Z | 0 | 0 |
pytorch
|
[
"pytorch",
"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"region:us"
] |
object-detection
| 2022-07-08T04:19:38Z |
---
language: en
license: gpl-3.0
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6t
---
# Model Card for yolov6t
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6s](https://hf.co/nateraw/yolov6s), [yolov6n](https://hf.co/nateraw/yolov6n)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
ManqingLiu/pegasus-samsum
|
ManqingLiu
| 2022-07-11T22:33:51Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-11T21:16:06Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4858
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7236 | 0.54 | 500 | 1.4858 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.10.3
|
mariastull/testpyramidsrnd
|
mariastull
| 2022-07-11T22:28:45Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-11T22:28:40Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: mariastull/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
camilag/t5-end2end-questions-generation
|
camilag
| 2022-07-11T20:52:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-11T20:12:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7927
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5425 | 0.34 | 100 | 1.9416 |
| 2.0221 | 0.68 | 200 | 1.7927 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_vp-it_s996
|
jonatasgrosman
| 2022-07-11T19:59:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:58:21Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-it_s996
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_r-wav2vec2_s468
|
jonatasgrosman
| 2022-07-11T19:48:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:47:54Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_r-wav2vec2_s468
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xls-r_s657
|
jonatasgrosman
| 2022-07-11T19:45:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:44:32Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xls-r_s657
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xls-r_s689
|
jonatasgrosman
| 2022-07-11T19:41:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:40:50Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xls-r_s689
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-sat_s103
|
jonatasgrosman
| 2022-07-11T19:34:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:33:36Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-sat_s103
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-sat_s756
|
jonatasgrosman
| 2022-07-11T19:26:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:26:24Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-sat_s756
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s6
|
jonatasgrosman
| 2022-07-11T19:17:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:16:53Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s6
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s833
|
jonatasgrosman
| 2022-07-11T19:13:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:12:53Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s833
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-es_s506
|
jonatasgrosman
| 2022-07-11T19:05:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:04:54Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s506
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-fr_s675
|
jonatasgrosman
| 2022-07-11T18:49:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:48:25Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s675
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s808
|
jonatasgrosman
| 2022-07-11T18:31:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:30:46Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s808
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s324
|
jonatasgrosman
| 2022-07-11T18:27:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:26:59Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s324
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wavlm_s118
|
jonatasgrosman
| 2022-07-11T18:23:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:22:59Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s118
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wavlm_s691
|
jonatasgrosman
| 2022-07-11T18:13:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:13:02Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s691
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_no-pretraining_s541
|
jonatasgrosman
| 2022-07-11T18:01:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:01:02Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s541
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_no-pretraining_s84
|
jonatasgrosman
| 2022-07-11T17:57:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:57:34Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s84
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s894
|
jonatasgrosman
| 2022-07-11T17:54:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:54:09Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s894
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s563
|
jonatasgrosman
| 2022-07-11T17:51:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:50:36Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s563
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdiKompella/Reinforce-Pixelcopter-PLE-v0
|
AdiKompella
| 2022-07-11T17:48:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T17:47:44Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 12.70 +/- 11.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s612
|
jonatasgrosman
| 2022-07-11T17:47:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:47:09Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s612
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_hubert_s486
|
jonatasgrosman
| 2022-07-11T17:43:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:42:50Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s486
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ianspektor/reinforce-CartPole-v1
|
ianspektor
| 2022-07-11T17:36:19Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T16:33:35Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPole-v1
results:
- metrics:
- type: mean_reward
value: 359.42 +/- 89.49
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jonatasgrosman/exp_w2v2t_pt_unispeech_s474
|
jonatasgrosman
| 2022-07-11T17:29:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:29:33Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech_s474
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner
|
kinanmartin
| 2022-07-11T17:29:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:toydata",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-11T03:49:46Z |
---
tags:
- generated_from_trainer
datasets:
- toydata
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-hrl-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: toydata
type: toydata
args: SDN
metrics:
- name: Precision
type: precision
value: 0.9132452695465905
- name: Recall
type: recall
value: 0.9205854126679462
- name: F1
type: f1
value: 0.9169006511739053
- name: Accuracy
type: accuracy
value: 0.9784804945824268
---
<!-- 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-large-ner-hrl-finetuned-ner
This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0944
- Precision: 0.9132
- Recall: 0.9206
- F1: 0.9169
- Accuracy: 0.9785
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 408 | 0.0900 | 0.8508 | 0.9303 | 0.8888 | 0.9719 |
| 0.1087 | 2.0 | 816 | 0.0827 | 0.9043 | 0.9230 | 0.9136 | 0.9783 |
| 0.0503 | 3.0 | 1224 | 0.0944 | 0.9132 | 0.9206 | 0.9169 | 0.9785 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.