modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 00:36:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 535
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-03 00:36:49
| card
stringlengths 11
1.01M
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|---|---|---|---|---|---|---|---|---|---|
swl-models/Cetus-Mix-Coda2
|
swl-models
| 2023-06-21T17:31:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T17:26:39Z |
---
license: creativeml-openrail-m
---
|
swl-models/Cetus-Mix-v4
|
swl-models
| 2023-06-21T17:28:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T17:25:21Z |
---
license: creativeml-openrail-m
---
|
swl-models/ShyakuJXMix-v1.0
|
swl-models
| 2023-06-21T16:59:03Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T16:03:58Z |
---
license: creativeml-openrail-m
---
|
antokprasetyo/Anggittt
|
antokprasetyo
| 2023-06-21T16:57:52Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T16:55:58Z |
---
license: creativeml-openrail-m
---
|
liliaciolite/my_awesome_eli5_clm-model
|
liliaciolite
| 2023-06-21T16:53:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T15:10:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7555
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8633 | 1.0 | 1137 | 3.7752 |
| 3.7624 | 2.0 | 2274 | 3.7588 |
| 3.7252 | 3.0 | 3411 | 3.7555 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Curiolearner/dqn-SpaceInvadersNoFrameskip-v4
|
Curiolearner
| 2023-06-21T16:49:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T16:49:21Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 549.50 +/- 96.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Curiolearner -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Curiolearner -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Curiolearner
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
a01137646/finetuning-sentiment-model-amazonbaby5000
|
a01137646
| 2023-06-21T16:45:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-15T23:25:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-amazonbaby5000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-amazonbaby5000
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: 0.2992
- Accuracy: 0.9133
- F1: 0.9435
## 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
deepghs/anime_ch_hair_length
|
deepghs
| 2023-06-21T16:41:02Z | 0 | 2 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_ch_hair_length",
"license:mit",
"region:us"
] |
image-classification
| 2023-06-15T01:06:31Z |
---
license: mit
datasets:
- deepghs/anime_ch_hair_length
metrics:
- accuracy
- f1
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------:|:-------:|:--------:|:----------:|:------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------:|
| caformer_s36_raw | 22.10G | 37.22M | 66.85% | 0.9133 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_length/blob/main/caformer_s36_raw/plot_confusion.png) | `bald`, `very_short`, `short`, `medium`, `long`, `very_long`, `absurdly_long` |
| caformer_s36_v0 | 22.10G | 37.22M | 68.60% | 0.9095 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_length/blob/main/caformer_s36_v0/plot_confusion.png) | `O`, `XS`, `S`, `M`, `L`, `XL` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 65.91% | 0.9026 | [confusion](https://huggingface.co/deepghs/anime_ch_hair_length/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `O`, `XS`, `S`, `M`, `L`, `XL` |
|
Nacholmo/Counterfeit-V2.5-vae-swapped
|
Nacholmo
| 2023-06-21T16:34:48Z | 34 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-10T20:29:47Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
# Counterfeit-V2.5 vae swapped, converted to diffusers for your enjoyment.
1. Safetensors to ckpt
2. Swap vae
3. Ckpt to diffusers
4. ??
5. profit
Original model: https://huggingface.co/gsdf/Counterfeit-V2.5
|
koreadaeil/finetuned-bert-piqa
|
koreadaeil
| 2023-06-21T16:33:07Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T16:27:46Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: koreadaeil/finetuned-bert-piqa
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. -->
# koreadaeil/finetuned-bert-piqa
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8264
- Validation Loss: 2.6491
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8757 | 2.7555 | 0 |
| 2.8434 | 2.7213 | 1 |
| 2.8264 | 2.6491 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
snailgood/NWsnail
|
snailgood
| 2023-06-21T16:19:43Z | 0 | 2 | null |
[
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:38:23Z |
---
license: creativeml-openrail-m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nolanspecter/LunaLander-v2
|
nolanspecter
| 2023-06-21T16:05:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T16:05:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.83 +/- 16.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
swl-models/Sakuramochimix-v1.0
|
swl-models
| 2023-06-21T16:02:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:59:02Z |
---
license: creativeml-openrail-m
---
|
pellucid/my_awesome_opus100_model
|
pellucid
| 2023-06-21T15:57:28Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"longt5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-21T07:37:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: my_awesome_opus100_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
config: en-ko
split: train
args: en-ko
metrics:
- name: Bleu
type: bleu
value: 0.0
---
<!-- 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. -->
# my_awesome_opus100_model
This model is a fine-tuned version of [KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko](https://huggingface.co/KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 0.0
- Gen Len: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 250 | nan | 2.9676 | 12.146 |
| 2.5985 | 2.0 | 500 | nan | 0.0 | 0.0 |
| 2.5985 | 3.0 | 750 | nan | 0.0 | 0.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
AV-117/finetuning-sentiment-model-amazonbaby5000
|
AV-117
| 2023-06-21T15:53:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T03:42:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-amazonbaby5000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-amazonbaby5000
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: 0.3174
- Accuracy: 0.9105
- F1: 0.9415
## 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
UnHolyTrinity/trinity_eng_quotes_model
|
UnHolyTrinity
| 2023-06-21T15:51:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T07:58:02Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: trinity_eng_quotes_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trinity_eng_quotes_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5092
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7419 | 1.0 | 2494 | 1.6046 |
| 1.6009 | 2.0 | 4988 | 1.5299 |
| 1.5646 | 3.0 | 7482 | 1.5092 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
bemc22/ppo-luna-lander-mark-i
|
bemc22
| 2023-06-21T15:46:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-20T14:07:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 288.66 +/- 12.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
agostini/q-FrozenLake-v1-4x4-noSlippery
|
agostini
| 2023-06-21T15:46:01Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T15:46:00Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="agostini/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"])
```
|
swl-models/Shanzhagao-v1
|
swl-models
| 2023-06-21T15:45:04Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:42:34Z |
---
license: creativeml-openrail-m
---
|
awj/distilbert-base-uncased-finetuned-emotion
|
awj
| 2023-06-21T15:41:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T15:12:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9395
- name: F1
type: f1
value: 0.9395633500318104
---
<!-- 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: 0.1433
- Accuracy: 0.9395
- F1: 0.9396
## 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: 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 | 0.1782 | 0.929 | 0.9285 |
| No log | 2.0 | 500 | 0.1433 | 0.9395 | 0.9396 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ellemac/dqn-SpaceInvadersNoFrameskip-v4
|
ellemac
| 2023-06-21T15:39:30Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T15:38:52Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 602.50 +/- 163.34
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ellemac -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ellemac -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ellemac
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
d0rj/ru-mbart-large-summ
|
d0rj
| 2023-06-21T15:39:13Z | 34 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"dialogue-summarization",
"mbart-50",
"ru",
"en",
"dataset:d0rj/samsum-ru",
"dataset:IlyaGusev/gazeta",
"dataset:zjkarina/matreshka",
"dataset:rcp-meetings/rudialogsum_v2",
"dataset:GEM/wiki_lingua",
"dataset:mlsum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-06-09T17:26:52Z |
---
license: mit
language:
- ru
- en
datasets:
- d0rj/samsum-ru
- IlyaGusev/gazeta
- zjkarina/matreshka
- rcp-meetings/rudialogsum_v2
- GEM/wiki_lingua
- mlsum
tags:
- summarization
- dialogue-summarization
- text2text-generation
- mbart
- mbart-50
widget:
- text: >
Актуальность проблемы. Электронная информация играет все большую роль во всех сферах жизни современного общества. В последние годы объем научно-технической текстовой информации в электронном виде возрос настолько, что возникает угроза обесценивания этой информации в связи с трудностями поиска необходимых сведений среди множества доступных текстов. Развитие информационных ресурсов Интернет многократно усугубило проблему информационной перегрузки. В этой ситуации особенно актуальными становятся методы автоматизации реферирования текстовой информации, то есть методы получения сжатого представления текстовых документов–рефератов (аннотаций). Постановка проблемы автоматического реферирования текста и соответственно попытки ее решения с использованием различных подходов предпринимались многими исследователями. История применения вычислительной техники для реферирования насчитывает уже более 50 лет и связана с именами таких исследователей, как Г.П. Лун, В.Е. Берзон, И.П. Cевбо, Э.Ф. Скороходько, Д.Г. Лахути, Р.Г. Пиотровский и др. За эти годы выработаны многочисленные подходы к решению данной проблемы, которые достаточно четко подразделяются на два направления: автоматическое реферирование, основанное на экстрагировании из первичных документов с помощью определенных формальных признаков «наиболее информативных» фраз (фрагментов), совокупность которых образует некоторый экстракт; автоматическое реферирование, основанное на выделении из текстов с помощью специальных информационных языков наиболее существенной информации и порождении новых текстов (рефератов), содержательно обобщающих первичные документы.
- text: >
Первую многоножку, у которой более тысячи ног, обнаружили в австралийских пещерах биологи, изучавшие там подземные воды. Предыдущей рекордсменкой по количеству ног была 700-ногая многоножка. Новый вид имеет длинное тонкое тело, похожее на нить, и большое количество конечностей, по-видимому, дает преимущества для быстрого перемещения и проникновения в труднодоступные места — ученые полагают, такая многоножка может спокойно перемещаться по трещинам в камнях. Австралия известна своими огромными и жутковатыми животными вроде 25-сантиметровых пауков. Теперь список пугающих членистоногих пополнился самой «многоногой» в мире многоножкой, у которой более тысячи ног. Необычное животное обнаружила группа исследователей из Австралии и США в пещерах на западе страны. Подробнее многоножку ученые описали в статье в журнале Scientific Reports. Исследователи занимались оценкой воздействия подземных вод на окружающую среду в зоне добычи полезных ископаемых на западе страны, когда наткнулись на новый вид многоножек. В отличие от большинства сородичей, живущих на поверхности, эти многоножки обитали в пещерах на глубине до 60 метров. Новый вид исследователи назвали Eumillipes persephone, в честь Персефоны — древнегреческой богини подземного мира. У многоножки оказалось 1306 ног — больше, чем у любого другого известного вида. Предыдущей рекордсменкой была калифорнийская Illacme plenipes, у которой насчитывалось до 750 ног. «Эти животные были настолько уникальны, — говорит биолог Бруно Бузатто. — Как только я понял, какой длины они были... Стало ясно, что это что-то совершенно новое». У Е. persephone нитевидное тело длиной около 9,5 см и шириной всего миллиметр, состоящее из 330 сегментов, короткие ноги и конусообразная голова. Как и другие животные, живущие в постоянной темноте, эти многоножки бледны и слепы. Энтомолог Пол Марек сравнивает ее с белой нитью, выдернутой из рубашки. Чтобы посчитать количество ног, ученым пришлось сначала снять многоножку в высоком разрешении, а затем закрашивать на фото каждый десяток ног другим цветом. (https://www.gazeta.ru/science/2021/12/17_a_14325355.shtml)
---
# ru-mbart-large-summ
## Model
Model forked from [ru-bart-large](https://huggingface.co/sn4kebyt3/ru-bart-large) which is smaller version of the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) with only Russian and English embeddings.
## Data
- [d0rj/samsum-ru](https://huggingface.co/datasets/d0rj/samsum-ru)
- [IlyaGusev/gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta)
- [zjkarina/matreshka](https://huggingface.co/datasets/zjkarina/matreshka)
- [rcp-meetings/rudialogsum_v2](https://huggingface.co/datasets/rcp-meetings/rudialogsum_v2)
- [GEM/wiki_lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [mlsum](https://huggingface.co/datasets/mlsum)
All 'train' subsets was concatenated and shuffled with seed `1000 - 7`.
Train subset = 155678 rows.
## Metrics
Evaluation on 10% of concatenated 'validation' subsets = 1458 rows.
See [WandB logs](https://wandb.ai/d0rj/summarization/runs/q8w8frta).
See report at **REPORT WIP**.
## Usage
```python
from transformers import pipeline
pipe = pipeline('summarization', model='d0rj/ru-mbart-large-summ')
pipe(text)
```
```python
import torch
from transformers import AutoTokenizer, MBartModel
tokenizer = AutoTokenizer.from_pretrained('d0rj/ru-mbart-large-summ')
model = MBartModel.from_pretrained('d0rj/ru-mbart-large-summ')
inputs = tokenizer('Всё в порядке, мимо двигал Утром прозвенел будильник', return_tensors='pt')
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
|
filypo/distilgpt2-finetuned-wikitext2
|
filypo
| 2023-06-21T15:27:12Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-20T15:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6890
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7849 | 1.0 | 2334 | 3.6890 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
abhishek-ignite/gpt-neo-1.3b-ignite-3
|
abhishek-ignite
| 2023-06-21T15:18:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T15:18:41Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
swl-models/really-zwy-v1.0
|
swl-models
| 2023-06-21T15:16:50Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T15:14:06Z |
---
license: creativeml-openrail-m
---
|
IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese
|
IDEA-CCNL
| 2023-06-21T15:05:49Z | 39 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-21T10:08:07Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# Erlangshen-TCBert-110M-Classification-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
110M参数的Topic Classification BERT (TCBert)。
The TCBert with 110M parameters is pre-trained for, not limited to, Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | TCBert | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上的效果，我们收集了大量话题分类数据进行基于prompts的预训练。
To improve the model performance on the topic classification task, we collected numerous topic classification datasets for pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板：
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻： |
| CSLDCP | 这一句描述__的内容如下： |
| IFLYTEK | 这一句描述__的内容如下： |
第一个prompt模板的微调实验结果：
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板：
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻，是跟__相关的内容： |
| CSLDCP | 接下来的学科，是跟__相关： |
| IFLYTEK | 接下来的生活内容，是跟__相关： |
第二个prompt模板的微调结果：
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese
|
IDEA-CCNL
| 2023-06-21T15:03:22Z | 47 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-21T10:27:40Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
110M参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 110M parameters is pre-trained for sentence representation for Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板：
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻： |
| CSLDCP | 这一句描述__的内容如下： |
| IFLYTEK | 这一句描述__的内容如下： |
第一个prompt模板的微调实验结果：
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板：
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻，是跟__相关的内容： |
| CSLDCP | 接下来的学科，是跟__相关： |
| IFLYTEK | 接下来的生活内容，是跟__相关： |
第二个prompt模板的微调结果：
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese
|
IDEA-CCNL
| 2023-06-21T15:02:41Z | 15 | 8 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"region:us"
] | null | 2022-10-21T10:31:48Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
1.3BM参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 1.3BM parameters is pre-trained for sentence representation for Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 1.3BM | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板：
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻： |
| CSLDCP | 这一句描述__的内容如下： |
| IFLYTEK | 这一句描述__的内容如下： |
第一个prompt模板的微调实验结果：
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板：
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻，是跟__相关的内容： |
| CSLDCP | 接下来的学科，是跟__相关： |
| IFLYTEK | 接下来的生活内容，是跟__相关： |
第二个prompt模板的微调结果：
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese
|
IDEA-CCNL
| 2023-06-21T15:01:26Z | 344 | 9 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"classification",
"zh",
"arxiv:2211.11304",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-10-22T05:47:52Z |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
330M参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 330M parameters is pre-trained for sentence representation for Chinese topic classification tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 330M | Chinese |
## 模型信息 Model Information
为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
### 下游效果 Performance
我们为每个数据集设计了两个prompt模板。
We customize two prompts templates for each dataset.
第一个prompt模板：
For ***prompt template 1***:
| Dataset | Prompt template 1 |
|---------|:------------------------:|
| TNEWS | 下面是一则关于__的新闻： |
| CSLDCP | 这一句描述__的内容如下： |
| IFLYTEK | 这一句描述__的内容如下： |
第一个prompt模板的微调实验结果：
The **fine-tuning** results for prompt template 1:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 55.02 | 57.37 | 51.34 |
| Macbert-large | 55.77 | 58.99 | 50.31 |
| Erlangshen-1.3B | 57.36 | 62.35 | 53.23 |
| TCBert-base<sub>110M-Classification-Chinese | 55.57 | 58.60 | 49.63 |
| TCBert-large<sub>330M-Classification-Chinese | 56.17 | 60.06 | 51.34 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 |
第一个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 1:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 |
| Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 |
| Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 |
| TCBert-base<sub>110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 |
| TCBert-large<sub>330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 |
第二个prompt模板：
For ***prompt template 2***:
| Dataset | Prompt template 2 |
|---------|:------------------------:|
| TNEWS | 接下来的新闻，是跟__相关的内容： |
| CSLDCP | 接下来的学科，是跟__相关： |
| IFLYTEK | 接下来的生活内容，是跟__相关： |
第二个prompt模板的微调结果：
The **fine-tuning** results for prompt template 2:
| Model | TNEWS | CLSDCP | IFLYTEK |
|-----------------|:------:|:------:|:-------:|
| Macbert-base | 54.78 | 58.38 | 50.83 |
| Macbert-large | 56.77 | 60.22 | 51.63 |
| Erlangshen-1.3B | 57.81 | 62.80 | 52.77 |
| TCBert-base<sub>110M-Classification-Chinese | 54.58 | 59.16 | 49.80 |
| TCBert-large<sub>330M-Classification-Chinese | 56.22 | 61.23 | 50.77 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 |
第二个prompt模板的句子相似度结果：
The **sentence similarity** results for prompt template 2:
| | TNEWS | | CSLDCP | | IFLYTEK | |
|-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| Model | referece | whitening | reference | whitening | reference | whitening |
| Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 |
| Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 |
| Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 |
| TCBert-base<sub>110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 |
| TCBert-large<sub>330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 |
| TCBert-1.3B<sub>1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 |
| TCBert-base<sub>110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 |
| TCBert-large<sub>330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 |
| TCBert-1.3B<sub>1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 |
更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。
For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo!
## 使用 Usage
### 使用示例 Usage Examples
```python
# Prompt-based MLM fine-tuning
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
# Prepare the data
inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt")
labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"]
labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
# Output the loss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
```
```python
# Prompt-based Sentence Similarity
# To extract sentence representations.
from transformers import BertForMaskedLM, BertTokenizer
import torch
# Loading models
tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese")
# Cosine similarity function
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
with torch.no_grad():
# To extract sentence representations for training data
training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt")
training_output = BertForMaskedLM(**token_text, output_hidden_states=True)
training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# To extract sentence representations for training data
test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt")
test_output = BertForMaskedLM(**token_text, output_hidden_states=True)
test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0)
# Calculate similarity scores
similarity_score = cos(training_representation, test_representation)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304):
If you use for your work, please cite the following paper
```
@article{han2022tcbert,
title={TCBERT: A Technical Report for Chinese Topic Classification BERT},
author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing},
journal={arXiv preprint arXiv:2211.11304},
year={2022}
}
```
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
34ronker/Dreamshaper-V5-baked-vae
|
34ronker
| 2023-06-21T14:45:25Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-04-25T08:58:52Z |
---
license: creativeml-openrail-m
---
|
KoRiF/codeparrot-ds
|
KoRiF
| 2023-06-21T14:43:58Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T13:33:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
gokuls/model_v1_complete_training_wt_init_48_small
|
gokuls
| 2023-06-21T14:41:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-20T10:10:42Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: model_v1_complete_training_wt_init_48_small
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. -->
# model_v1_complete_training_wt_init_48_small
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5728
- Accuracy: 0.5268
## 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: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7567 | 0.25 | 30000 | 3.5433 | 0.4111 |
| 3.3711 | 0.49 | 60000 | 3.1762 | 0.4519 |
| 3.1874 | 0.74 | 90000 | 3.0125 | 0.4709 |
| 3.0895 | 0.98 | 120000 | 2.9136 | 0.4832 |
| 3.0097 | 1.23 | 150000 | 2.8447 | 0.4919 |
| 2.9567 | 1.47 | 180000 | 2.7906 | 0.4985 |
| 2.9172 | 1.72 | 210000 | 2.7519 | 0.5034 |
| 2.8781 | 1.97 | 240000 | 2.7183 | 0.5079 |
| 2.8467 | 2.21 | 270000 | 2.6916 | 0.5113 |
| 2.8156 | 2.46 | 300000 | 2.6691 | 0.5142 |
| 2.7975 | 2.7 | 330000 | 2.6503 | 0.5168 |
| 2.7806 | 2.95 | 360000 | 2.6341 | 0.5189 |
| 2.7637 | 3.2 | 390000 | 2.6222 | 0.5204 |
| 2.7629 | 3.44 | 420000 | 2.6085 | 0.5223 |
| 2.7457 | 3.69 | 450000 | 2.5993 | 0.5235 |
| 2.7393 | 3.93 | 480000 | 2.5901 | 0.5247 |
| 2.7285 | 4.18 | 510000 | 2.5829 | 0.5256 |
| 2.7204 | 4.42 | 540000 | 2.5792 | 0.5260 |
| 2.7213 | 4.67 | 570000 | 2.5758 | 0.5266 |
| 2.716 | 4.92 | 600000 | 2.5728 | 0.5268 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
|
elshehawy/Reinforce-Pixelcopter-1
|
elshehawy
| 2023-06-21T14:40:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T14:40:35Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 49.60 +/- 40.19
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AdrienBin/my_awesome_qa_model
|
AdrienBin
| 2023-06-21T14:37:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T14:20:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5212
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | 5.6234 |
| No log | 2.0 | 6 | 5.5520 |
| No log | 3.0 | 9 | 5.5212 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
tux/LunarLanderV2_ppo_from_scratch
|
tux
| 2023-06-21T14:36:38Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T14:32:40Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -151.29 +/- 39.57
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
VarunD/ppo-LunarLander-v2
|
VarunD
| 2023-06-21T14:23:50Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T14:23:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.22 +/- 12.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
orkg/orkgnlp-research-fields-classification
|
orkg
| 2023-06-21T14:20:50Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-06-07T13:53:34Z |
---
license: mit
---
This Repository includes the files required to run the `Research Fields Classification` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service.
This model is converted into a [TorchScript](https://pytorch.org/docs/stable/jit.html) (ScriptModule) using [torch.jit.trace](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html).
|
gsarti/opus-mt-tc-base-en-ja
|
gsarti
| 2023-06-21T14:12:24Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ja",
"multilingual",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-09T21:54:27Z |
---
language:
- en
- ja
- multilingual
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-base-en-ja
results:
- task:
type: translation
name: Translation eng-jpg
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-jpg
metrics:
- type: bleu
value: 15.2
name: BLEU
---
# Opus Tatoeba English-Japanese
*This model was obtained by running the script [convert_marian_to_pytorch.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/marian/convert_marian_to_pytorch.py). The original models were trained by [J�rg Tiedemann](https://blogs.helsinki.fi/tiedeman/) using the [MarianNMT](https://marian-nmt.github.io/) library. See all available `MarianMTModel` models on the profile of the [Helsinki NLP](https://huggingface.co/Helsinki-NLP) group.*
* dataset: opus+bt
* model: transformer-align
* source language(s): eng
* target language(s): jpn
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download: [opus+bt-2021-04-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.zip)
* test set translations: [opus+bt-2021-04-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.test.txt)
* test set scores: [opus+bt-2021-04-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-jpn/opus+bt-2021-04-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---------|-------|-------|-------|--------|----|
| Tatoeba-test.eng-jpn | 15.2 | 0.258 | 10000 | 99206 | 1.000 |
|
VMware/vinilm-2021-qa-evaluator
|
VMware
| 2023-06-21T14:08:52Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"dataset:iarfmoose/qa_evaluator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-31T18:53:12Z |
---
license: apache-2.0
datasets: iarfmoose/qa_evaluator
---
# viniLM-2021-qa-evaluator
This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [viniLM-2021](https://huggingface.co/VMware/vinilm-2021-from-large) with a sequence classification head.
Observationally, this model produces similar results as the original [BERT-Base-cased QA Evaluator](https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator), but inference is twice as fast.
## Intended uses
The QA evaluator was originally designed to be used with the [t5-base-question-generator](https://huggingface.co/iarfmoose/t5-base-question-generator) for evaluating the quality of generated questions.
The input for the QA evaluator follows the format for `BertForSequenceClassification`, but using the question and answer as the two sequences. Inputs should take the following format:
```
[CLS] <question> [SEP] <answer> [SEP]
```
## Limitations and bias
The model is trained to evaluate if a question and answer are semantically related, but cannot determine whether an answer is actually true/correct or not.
## Training data
This model was trained with the same [dataset](https://huggingface.co/datasets/iarfmoose/qa_evaluator) as the original [BERT-Base-cased QA Evaluator](https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator), which is made up of question-answer pairs from the following datasets:
- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
- [RACE](http://www.cs.cmu.edu/~glai1/data/race/)
- [CoQA](https://stanfordnlp.github.io/coqa/)
- [MSMARCO](https://microsoft.github.io/msmarco/)
## Training procedure
The question and answer were concatenated 50% of the time. In the other 50% of the time a corruption operation was performed (either swapping the answer for an unrelated answer, or by copying part of the question into the answer). The model was then trained to predict whether the input sequence represented one of the original QA pairs or a corrupted input.
|
surajp/albert-base-sanskrit
|
surajp
| 2023-06-21T13:56:27Z | 12 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"albert",
"feature-extraction",
"sa",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: sa
---
# ALBERT-base-Sanskrit
Explaination Notebook Colab: [SanskritALBERT.ipynb](https://colab.research.google.com/github/parmarsuraj99/suraj-parmar/blob/master/_notebooks/2020-05-02-SanskritALBERT.ipynb)
Size of the model is **46MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained("surajp/albert-base-sanskrit")
model = AutoModel.from_pretrained("surajp/albert-base-sanskrit")
enc=tokenizer.encode("ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥")
print(tokenizer.decode(enc))
ps = model(torch.tensor(enc).unsqueeze(1))
print(ps[0].shape)
```
```
'''
Output:
--------
[CLS] ॐ सर्वे भवन्तु सुखिनः सर्वे सन्तु निरामयाः । सर्वे भद्राणि पश्यन्तु मा कश्चिद्दुःखभाग्भवेत् । ॐ शान्तिः शान्तिः शान्तिः ॥[SEP]
torch.Size([28, 1, 768])
```
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99)
> Made with <span style="color: #e25555;">♥</span> in India
|
surajp/RoBERTa-hindi-guj-san
|
surajp
| 2023-06-21T13:56:15Z | 63 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"Indic",
"hi",
"sa",
"gu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- hi
- sa
- gu
tags:
- Indic
license: mit
datasets:
- Wikipedia (Hindi, Sanskrit, Gujarati)
metrics:
- perplexity
---
# RoBERTa-hindi-guj-san
## Model description
Multillingual RoBERTa like model trained on Wikipedia articles of Hindi, Sanskrit, Gujarati languages. The tokenizer was trained on combined text.
However, Hindi text was used to pre-train the model and then it was fine-tuned on Sanskrit and Gujarati Text combined hoping that pre-training with Hindi
will help the model learn similar languages.
### Configuration
| Parameter | Value |
|---|---|
| `hidden_size` | 768 |
| `num_attention_heads` | 12 |
| `num_hidden_layers` | 6 |
| `vocab_size` | 30522 |
|`model_type`|`roberta`|
## Intended uses & limitations
#### How to use
```python
# Example usage
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("surajp/RoBERTa-hindi-guj-san")
model = AutoModelWithLMHead.from_pretrained("surajp/RoBERTa-hindi-guj-san")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
# Sanskrit: इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।
# Hindi: अगर आप अब अभ्यास नहीं करते हो तो आप अपने परीक्षा में मूर्खतापूर्ण गलतियाँ करोगे।
# Gujarati: ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.
fill_mask("ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો <mask> હતો.")
'''
Output:
--------
[
{'score': 0.07849744707345963, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો જ હતો.</s>', 'token': 390},
{'score': 0.06273336708545685, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો ન હતો.</s>', 'token': 478},
{'score': 0.05160355195403099, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો થઇ હતો.</s>', 'token': 2075},
{'score': 0.04751499369740486, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો એક હતો.</s>', 'token': 600},
{'score': 0.03788900747895241, 'sequence': '<s> ગુજરાતમાં ૧૯મી માર્ચ સુધી કોઈ સકારાત્મક (પોઝીટીવ) રીપોર્ટ આવ્યો પણ હતો.</s>', 'token': 840}
]
```
## Training data
Cleaned wikipedia articles in Hindi, Sanskrit and Gujarati on Kaggle. It contains training as well as evaluation text.
Used in [iNLTK](https://github.com/goru001/inltk)
- [Hindi](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-172k)
- [Gujarati](https://www.kaggle.com/disisbig/gujarati-wikipedia-articles)
- [Sanskrit](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles)
## Training procedure
- On TPU (using `xla_spawn.py`)
- For language modelling
- Iteratively increasing `--block_size` from 128 to 256 over epochs
- Tokenizer trained on combined text
- Pre-training with Hindi and fine-tuning on Sanskrit and Gujarati texts
```
--model_type distillroberta-base \
--model_name_or_path "/content/SanHiGujBERTa" \
--mlm_probability 0.20 \
--line_by_line \
--save_total_limit 2 \
--per_device_train_batch_size 128 \
--per_device_eval_batch_size 128 \
--num_train_epochs 5 \
--block_size 256 \
--seed 108 \
--overwrite_output_dir \
```
## Eval results
perplexity = 2.920005983224673
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99) | [LinkedIn](https://www.linkedin.com/in/parmarsuraj99/)
> Made with <span style="color: #e25555;">♥</span> in India
|
arcane-impact/gpt_bigcode-santacoder-ggml
|
arcane-impact
| 2023-06-21T13:50:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-06-21T13:40:47Z |
---
license: openrail
---
GGML format of [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
|
reinforceYrWay/ppo-Huggy
|
reinforceYrWay
| 2023-06-21T13:49:25Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-21T13:49:21Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: reinforceYrWay/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kai0226/fine_tuned_bloomz_weather
|
kai0226
| 2023-06-21T13:45:48Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
text-generation
| 2023-06-21T11:14:20Z |
---
language:
- en
pipeline_tag: text-generation
license: bigscience-bloom-rail-1.0
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
AustinCarthy/MixGPT2V2_suffix_100KP_BFall_fromP_95K_topP_0.75_ratio2.63
|
AustinCarthy
| 2023-06-21T13:36:33Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-06-21T09:40:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2V2_suffix_100KP_BFall_fromP_95K_topP_0.75_ratio2.63
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. -->
# MixGPT2V2_suffix_100KP_BFall_fromP_95K_topP_0.75_ratio2.63
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2V2_using_phish_95K_top_p_0.75suffix dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0424
- Accuracy: 0.9979
- F1: 0.9770
- Precision: 0.9973
- Recall: 0.9576
- Roc Auc Score: 0.9787
- Tpr At Fpr 0.01: 0.9568
## 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0229 | 1.0 | 11061 | 0.0344 | 0.9975 | 0.9736 | 0.9786 | 0.9686 | 0.9838 | 0.0 |
| 0.0102 | 2.0 | 22122 | 0.0450 | 0.9971 | 0.9691 | 0.9977 | 0.9422 | 0.9710 | 0.941 |
| 0.0047 | 3.0 | 33183 | 0.0499 | 0.9974 | 0.9715 | 0.9987 | 0.9458 | 0.9729 | 0.958 |
| 0.0007 | 4.0 | 44244 | 0.0390 | 0.9979 | 0.9773 | 0.9977 | 0.9578 | 0.9788 | 0.9572 |
| 0.0007 | 5.0 | 55305 | 0.0424 | 0.9979 | 0.9770 | 0.9973 | 0.9576 | 0.9787 | 0.9568 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
LOGQS/a2c-AntBulletEnv-v0
|
LOGQS
| 2023-06-21T13:36:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T13:35:21Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1917.61 +/- 139.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
chocky18/alpaca7B-lora
|
chocky18
| 2023-06-21T13:24:29Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T13:24:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
chjooon/distilgpt2_fiscal
|
chjooon
| 2023-06-21T13:21:55Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T13:09:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2_fiscal
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. -->
# distilgpt2_fiscal
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1431
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9252 | 1.0 | 816 | 1.3275 |
| 1.3825 | 2.0 | 1632 | 1.1800 |
| 1.3156 | 3.0 | 2448 | 1.1431 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
rodrigoclira/q-Taxi-v3
|
rodrigoclira
| 2023-06-21T13:20:15Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T13:20:13Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.38 +/- 2.45
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rodrigoclira/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"])
```
|
rodrigoclira/q-FrozenLake-v1-4x4-noSlippery
|
rodrigoclira
| 2023-06-21T13:13:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T13:13:54Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rodrigoclira/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"])
```
|
nikolajking/Low_resource_translator_en_vt
|
nikolajking
| 2023-06-21T13:06:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"translation",
"en",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-06-20T07:19:19Z |
---
language:
- en
- vi
metrics:
- bleu
pipeline_tag: translation
---
|
AravindVadlapudi02/swinv2-base-patch4-window12to16-192to256-22kto1k-ft-finetuned-swinv2-final-augmented-73
|
AravindVadlapudi02
| 2023-06-21T13:05:23Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T13:05:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
Daya7624/openai-whisper-large-v2-LORA-colab
|
Daya7624
| 2023-06-21T13:02:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T12:46:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
elshehawy/Reinforce-CartPole-1
|
elshehawy
| 2023-06-21T12:54:38Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T15:29:50Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mediattack/elbio
|
mediattack
| 2023-06-21T12:54:36Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-06-21T12:50:00Z |
---
license: openrail
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
keremnazliel/distilbert_squad_for_musique
|
keremnazliel
| 2023-06-21T12:47:25Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-20T08:29:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_squad_for_musique
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_squad_for_musique
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.5452
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
prashantmahajan/my_awesome_model
|
prashantmahajan
| 2023-06-21T12:31:14Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T05:45:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
haddadalwi/roberta-large-squad2-finetuned-squad2-all
|
haddadalwi
| 2023-06-21T12:31:11Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T11:44:37Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-large-squad2-finetuned-squad2-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. -->
# roberta-large-squad2-finetuned-squad2-all
This model is a fine-tuned version of [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5864
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9526 | 1.0 | 839 | 0.5363 |
| 0.5279 | 2.0 | 1678 | 0.5864 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
antuuuu/seonn
|
antuuuu
| 2023-06-21T12:21:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T12:20:22Z |
---
license: creativeml-openrail-m
---
|
user1251/my_awesome_eli5_clm-model
|
user1251
| 2023-06-21T12:19:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-21T11:51:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7429
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8686 | 1.0 | 1135 | 3.7603 |
| 3.7724 | 2.0 | 2270 | 3.7451 |
| 3.7304 | 3.0 | 3405 | 3.7429 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Pranjal-666/ppo-LunarLander-v2
|
Pranjal-666
| 2023-06-21T12:17:23Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T11:09:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.46 +/- 25.75
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
NickyNicky/rwkv-4-169m-pile-oasst1-QLora-v3
|
NickyNicky
| 2023-06-21T12:17:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T12:17:14Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: True
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
joncam14/a2c-PandaReachDense-v2
|
joncam14
| 2023-06-21T12:10:55Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T12:08:10Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.03 +/- 0.64
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
TheBloke/PMC_LLAMA-7B-GGML
|
TheBloke
| 2023-06-21T12:08:38Z | 0 | 10 | null |
[
"medical",
"dataset:allenai/s2orc",
"license:other",
"region:us"
] | null | 2023-06-03T00:46:05Z |
---
inference: false
license: other
tags:
- medical
datasets:
- allenai/s2orc
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Chaoyi Wi's PMC_LLAMA 7B GGML
These files are GGML format model files for [Chaoyi Wi's PMC_LLAMA 7B](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/PMC_LLAMA-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/PMC_LLAMA-7B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt.
## Explanation of the new k-quant methods
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| PMC_LLAMA-7B.ggmlv3.q2_K.bin | q2_K | 2 | 2.87 GB | 5.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| PMC_LLAMA-7B.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 3.60 GB | 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| PMC_LLAMA-7B.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 3.28 GB | 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| PMC_LLAMA-7B.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 2.95 GB | 5.45 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| PMC_LLAMA-7B.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. |
| PMC_LLAMA-7B.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| PMC_LLAMA-7B.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 4.08 GB | 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| PMC_LLAMA-7B.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 3.83 GB | 6.33 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| PMC_LLAMA-7B.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| PMC_LLAMA-7B.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| PMC_LLAMA-7B.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 4.78 GB | 7.28 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| PMC_LLAMA-7B.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 4.65 GB | 7.15 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| PMC_LLAMA-7B.ggmlv3.q6_K.bin | q6_K | 6 | 5.53 GB | 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| PMC_LLAMA-7B.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m PMC_LLAMA-7B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
If you're able to use full GPU offloading, you should use `-t 1` to get best performance.
If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Mano Prime, Fen Risland, Derek Yates, Preetika Verma, webtim, Sean Connelly, Alps Aficionado, Karl Bernard, Junyu Yang, Nathan LeClaire, Chris McCloskey, Lone Striker, Asp the Wyvern, Eugene Pentland, Imad Khwaja, trip7s trip, WelcomeToTheClub, John Detwiler, Artur Olbinski, Khalefa Al-Ahmad, Trenton Dambrowitz, Talal Aujan, Kevin Schuppel, Luke Pendergrass, Pyrater, Joseph William Delisle, terasurfer , vamX, Gabriel Puliatti, David Flickinger, Jonathan Leane, Iucharbius , Luke, Deep Realms, Cory Kujawski, ya boyyy, Illia Dulskyi, senxiiz, Johann-Peter Hartmann, John Villwock, K, Ghost , Spiking Neurons AB, Nikolai Manek, Rainer Wilmers, Pierre Kircher, biorpg, Space Cruiser, Ai Maven, subjectnull, Willem Michiel, Ajan Kanaga, Kalila, chris gileta, Oscar Rangel.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Chaoyi Wi's PMC_LLAMA 7B
This repo contains PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in S2ORC dataset.
The model was trained with the following hyperparameters:
* Epochs: 5
* Batch size: 128
* Cutoff length: 512
* Learning rate: 2e-5
Each epoch we sample 512 tokens per paper for training.
The model can be loaded as following:
```
import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
sentence = 'Hello, doctor'
batch = tokenizer(
sentence,
return_tensors="pt",
add_special_tokens=False
)
with torch.no_grad():
generated = model.generate(inputs = batch["input_ids"], max_length=200, do_sample=True, top_k=50)
print('model predict: ',tokenizer.decode(generated[0]))
```
|
Rajaganapathy/my_awesome_qa_model
|
Rajaganapathy
| 2023-06-21T11:37:52Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T11:28:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6019
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.1796 |
| 2.6572 | 2.0 | 500 | 1.6555 |
| 2.6572 | 3.0 | 750 | 1.6019 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
manasvinid/Sentiment-Analysis-on-Twitter-BCS
|
manasvinid
| 2023-06-21T11:30:23Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T12:44:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Sentiment-Analysis-on-Twitter-BCS
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. -->
# Sentiment-Analysis-on-Twitter-BCS
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1303
- Accuracy: 0.9615
- Precision: 0.7730
- Recall: 0.6384
- F1: 0.6993
- Roc Auc: 0.9701
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.211 | 1.0 | 1798 | 0.1622 | 0.9515 | 0.6769 | 0.5893 | 0.6301 | 0.9417 |
| 0.1369 | 2.0 | 3596 | 0.1568 | 0.9568 | 0.7009 | 0.6696 | 0.6849 | 0.9646 |
| 0.1118 | 3.0 | 5394 | 0.1303 | 0.9615 | 0.7730 | 0.6384 | 0.6993 | 0.9701 |
| 0.0887 | 4.0 | 7192 | 0.1532 | 0.9631 | 0.8011 | 0.6295 | 0.7050 | 0.9708 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
moka-ai/m3e-large
|
moka-ai
| 2023-06-21T11:25:23Z | 2,209 | 205 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"embedding",
"text-embedding",
"zh",
"en",
"region:us"
] | null | 2023-06-21T09:07:12Z |
---
language:
- zh
- en
tags:
- embedding
- text-embedding
library_name: sentence-transformers
---
# M3E Models
[m3e-small](https://huggingface.co/moka-ai/m3e-small) | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | [m3e-large](https://huggingface.co/moka-ai/m3e-large)
M3E 是 Moka Massive Mixed Embedding 的缩写
- Moka，此模型由 MokaAI 训练，开源和评测，训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py) ，评测 BenchMark 使用 [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh)
- Massive，此模型通过**千万级** (2200w+) 的中文句对数据集进行训练
- Mixed，此模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索
- Embedding，此模型是文本嵌入模型，可以将自然语言转换成稠密的向量
## 更新说明
- 2023.06.14，添加了三个中文开源文本嵌入模型到评测中，包括 UER, ErLangShen, DMetaSoul
- 2023.06.08，添加检索任务的评测结果，在 T2Ranking 1W 中文数据集上，m3e-base 在 ndcg@10 上达到了 0.8004，超过了 openai-ada-002 的 0.7786
- 2023.06.07，添加文本分类任务的评测结果，在 6 种文本分类数据集上，m3e-base 在 accuracy 上达到了 0.6157，超过了 openai-ada-002 的 0.5956
## 模型对比
| | 参数数量 | 维度 | 中文 | 英文 | s2s | s2p | s2c | 开源 | 兼容性 | s2s Acc | s2p ndcg@10 |
| --------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | ---- | ---------- | ------------ | -------- |
| m3e-small | 24M | 512 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5834 | 0.7262 |
| m3e-base | 110M | 768 | 是 | 是 | 是 | 是 | 否 | 是 | 优 | 0.6157 | **0.8004** |
| m3e-large | 340M | 768 | 是 | 否 | 是 | 是 | 否 | 是 | 优 | **0.6231** | 0.7974 |
| text2vec | 110M | 768 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5755 | 0.6346 |
| openai-ada-002 | 未知 | 1536 | 是 | 是 | 是 | 是 | 是 | 否 | 优 | 0.5956 | 0.7786 |
说明：
- s2s, 即 sentence to sentence ，代表了同质文本之间的嵌入能力，适用任务：文本相似度，重复问题检测，文本分类等
- s2p, 即 sentence to passage ，代表了异质文本之间的嵌入能力，适用任务：文本检索，GPT 记忆模块等
- s2c, 即 sentence to code ，代表了自然语言和程序语言之间的嵌入能力，适用任务：代码检索
- 兼容性，代表了模型在开源社区中各种项目被支持的程度，由于 m3e 和 text2vec 都可以直接通过 sentence-transformers 直接使用，所以和 openai 在社区的支持度上相当
- ACC & ndcg@10，详情见下方的评测
Tips:
- 使用场景主要是中文，少量英文的情况，建议使用 m3e 系列的模型
- 多语言使用场景，并且不介意数据隐私的话，我建议使用 openai text-embedding-ada-002
- 代码检索场景，推荐使用 openai text-embedding-ada-002
- 文本检索场景，请使用具备文本检索能力的模型，只在 S2S 上训练的文本嵌入模型，没有办法完成文本检索任务
## 使用方式
您需要先安装 sentence-transformers
```bash
pip install -U sentence-transformers
```
安装完成后，您可以使用以下代码来使用 M3E Models
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('moka-ai/m3e-base')
#Our sentences we like to encode
sentences = [
'* Moka 此文本嵌入模型由 MokaAI 训练并开源，训练脚本使用 uniem',
'* Massive 此文本嵌入模型通过**千万级**的中文句对数据集进行训练',
'* Mixed 此文本嵌入模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索，ALL in one'
]
#Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
#Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
```
M3E 系列的所有模型在设计的时候就考虑到完全兼容 [sentence-transformers](https://www.sbert.net/) ，所以你可以通过**替换名称字符串**的方式在所有支持 sentence-transformers 的项目中**无缝**使用 M3E Models，比如 [chroma](https://docs.trychroma.com/getting-started), [guidance](https://github.com/microsoft/guidance), [semantic-kernel](https://github.com/microsoft/semantic-kernel) 。
## 训练方案
M3E 使用 in-batch 负采样的对比学习的方式在句对数据集进行训练，为了保证 in-batch 负采样的效果，我们使用 A100 80G 来最大化 batch-size，并在共计 2200W+ 的句对数据集上训练了 1 epoch。训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py)，您可以在这里查看具体细节。
## 特性
- 中文训练集，M3E 在大规模句对数据集上的训练，包含中文百科，金融，医疗，法律，新闻，学术等多个领域共计 2200W 句对样本，数据集详见 [M3E 数据集](#M3E数据集)
- 英文训练集，M3E 使用 MEDI 145W 英文三元组数据集进行训练，数据集详见 [MEDI 数据集](https://drive.google.com/file/d/1vZ5c2oJNonGOvXzppNg5mHz24O6jcc52/view)，此数据集由 [instructor team](https://github.com/HKUNLP/instructor-embedding) 提供
- 指令数据集，M3E 使用了 300W + 的指令微调数据集，这使得 M3E 对文本编码的时候可以遵从指令，这部分的工作主要被启发于 [instructor-embedding](https://github.com/HKUNLP/instructor-embedding)
- 基础模型，M3E 使用 hfl 实验室的 [Roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext) 系列模型进行训练，目前提供 small、base和large三个版本，大家则需选用
- ALL IN ONE，M3E 旨在提供一个 ALL IN ONE 的文本嵌入模型，不仅支持同质句子相似度判断，还支持异质文本检索，你只需要一个模型就可以覆盖全部的应用场景，未来还会支持代码检索
## 评测
- 评测模型，[text2vec](https://github.com/shibing624/text2vec), m3e-base, m3e-small, openai text-embedding-ada-002, [DMetaSoul](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2), [UER](https://huggingface.co/uer/sbert-base-chinese-nli), [ErLangShen](https://huggingface.co/IDEA-CCNL/Erlangshen-SimCSE-110M-Chinese)
- 评测脚本，具体参考 [MTEB-zh] (https://github.com/wangyuxinwhy/uniem/blob/main/mteb-zh)
### 文本分类
- 数据集选择，选择开源在 HuggingFace 上的 6 种文本分类数据集，包括新闻、电商评论、股票评论、长文本等
- 评测方式，使用 MTEB 的方式进行评测，报告 Accuracy。
| | text2vec | m3e-small | m3e-base | m3e-large | openai | DMetaSoul | uer | erlangshen |
| ----------------- | -------- | --------- | -------- | ------ | ----------- | ------- | ----------- | ----------- |
| TNews | 0.43 | 0.4443 | 0.4827 | **0.4866** | 0.4594 | 0.3084 | 0.3539 | 0.4361 |
| JDIphone | 0.8214 | 0.8293 | 0.8533 | **0.8692** | 0.746 | 0.7972 | 0.8283 | 0.8356 |
| GubaEastmony | 0.7472 | 0.712 | 0.7621 | 0.7663 | 0.7574 | 0.735 | 0.7534 | **0.7787** |
| TYQSentiment | 0.6099 | 0.6596 | 0.7188 | **0.7247** | 0.68 | 0.6437 | 0.6662 | 0.6444 |
| StockComSentiment | 0.4307 | 0.4291 | 0.4363 | 0.4475 | **0.4819** | 0.4309 | 0.4555 | 0.4482 |
| IFlyTek | 0.414 | 0.4263 | 0.4409 | 0.4445 | **0.4486** | 0.3969 | 0.3762 | 0.4241 |
| Average | 0.5755 | 0.5834 | 0.6157 | **0.6231** | 0.5956 | 0.552016667 | 0.57225 | 0.594516667 |
### 检索排序
#### T2Ranking 1W
- 数据集选择，使用 [T2Ranking](https://github.com/THUIR/T2Ranking/tree/main) 数据集，由于 T2Ranking 的数据集太大，openai 评测起来的时间成本和 api 费用有些高，所以我们只选择了 T2Ranking 中的前 10000 篇文章
- 评测方式，使用 MTEB 的方式进行评测，报告 map@1, map@10, mrr@1, mrr@10, ndcg@1, ndcg@10
- 注意！从实验结果和训练方式来看，除了 M3E 模型和 openai 模型外，其余模型都没有做检索任务的训练，所以结果仅供参考。
| | text2vec | openai-ada-002 | m3e-small | m3e-base | m3e-large | DMetaSoul | uer | erlangshen |
| ------- | -------- | -------------- | --------- | -------- | --------- | ------- | ---------- | ---------- |
| map@1 | 0.4684 | 0.6133 | 0.5574 | **0.626** | 0.6256 | 0.25203 | 0.08647 | 0.25394 |
| map@10 | 0.5877 | 0.7423 | 0.6878 | **0.7656** | 0.7627 | 0.33312 | 0.13008 | 0.34714 |
| mrr@1 | 0.5345 | 0.6931 | 0.6324 | 0.7047 | **0.7063** | 0.29258 | 0.10067 | 0.29447 |
| mrr@10 | 0.6217 | 0.7668 | 0.712 | **0.7841** | 0.7827 | 0.36287 | 0.14516 | 0.3751 |
| ndcg@1 | 0.5207 | 0.6764 | 0.6159 | 0.6881 | **0.6884** | 0.28358 | 0.09748 | 0.28578 |
| ndcg@10 | 0.6346 | 0.7786 | 0.7262 | **0.8004** | 0.7974 | 0.37468 | 0.15783 | 0.39329 |
#### T2Ranking
- 数据集选择，使用 T2Ranking，刨除 openai-ada-002 模型后，我们对剩余的三个模型，进行 T2Ranking 10W 和 T2Ranking 50W 的评测。（T2Ranking 评测太耗内存了... 128G 都不行）
- 评测方式，使用 MTEB 的方式进行评测，报告 ndcg@10
| | text2vec | m3e-small | m3e-base |
| ------- | -------- | --------- | -------- |
| t2r-1w | 0.6346 | 0.72621 | **0.8004** |
| t2r-10w | 0.44644 | 0.5251 | **0.6263** |
| t2r-50w | 0.33482 | 0.38626 | **0.47364** |
说明：
- 检索排序对于 text2vec 并不公平，因为 text2vec 在训练的时候没有使用过检索相关的数据集，所以没有办法很好的完成检索任务也是正常的。
## M3E数据集
如果您想要使用这些数据集，你可以在 [uniem process_zh_datasets](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/process_zh_datasets.py) 中找到加载 huggingface 数据集的脚本，非 huggingface 数据集需要您根据下方提供的链接自行下载和处理。
| 数据集名称 | 领域 | 数量 | 任务类型 | Prompt | 质量 | 数据提供者 | 说明 | 是否开源/研究使用 | 是否商用 | 脚本 | Done | URL | 是否同质 |
| -------------------- | ---- | --------- | ----------------- | ------ | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----------------- | -------- | ---- | ---- | ------------------------------------------------------------ | -------- |
| cmrc2018 | 百科 | 14,363 | 问答 | 问答 | 优 | Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu | https://github.com/ymcui/cmrc2018/blob/master/README_CN.md 专家标注的基于维基百科的中文阅读理解数据集，将问题和上下文视为正例 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/cmrc2018 | 否 |
| belle_2m | 百科 | 2,000,000 | 指令微调 | 无 | 优 | LianjiaTech/BELLE | belle 的指令微调数据集，使用 self instruct 方法基于 gpt3.5 生成 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/BelleGroup/train_2M_CN | 否 |
| firefily | 百科 | 1,649,399 | 指令微调 | 无 | 优 | YeungNLP | Firefly（流萤） 是一个开源的中文对话式大语言模型，使用指令微调（Instruction Tuning）在中文数据集上进行调优。使用了词表裁剪、ZeRO等技术，有效降低显存消耗和提高训练效率。 在训练中，我们使用了更小的模型参数量，以及更少的计算资源。 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M | 否 |
| alpaca_gpt4 | 百科 | 48,818 | 指令微调 | 无 | 优 | Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao | 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/alpaca-zh | 否 |
| zhihu_kol | 百科 | 1,006,218 | 问答 | 问答 | 优 | wangrui6 | 知乎问答 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wangrui6/Zhihu-KOL | 否 |
| hc3_chinese | 百科 | 39,781 | 问答 | 问答 | 良 | Hello-SimpleAI | 问答数据，包括人工回答和 GPT 回答 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese | 否 |
| amazon_reviews_multi | 电商 | 210,000 | 问答 文本分类 | 摘要 | 优 | 亚马逊 | 亚马逊产品评论数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/amazon_reviews_multi/viewer/zh/train?row=8 | 否 |
| mlqa | 百科 | 85,853 | 问答 | 问答 | 良 | patrickvonplaten | 一个用于评估跨语言问答性能的基准数据集 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/mlqa/viewer/mlqa-translate-train.zh/train?p=2 | 否 |
| xlsum | 新闻 | 93,404 | 摘要 | 摘要 | 良 | BUET CSE NLP Group | BBC的专业注释文章摘要对 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/chinese_simplified/train?row=259 | 否 |
| ocnli | 口语 | 17,726 | 自然语言推理 | 推理 | 良 | Thomas Wolf | 自然语言推理数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/clue/viewer/ocnli | 是 |
| BQ | 金融 | 60,000 | 文本分类 | 相似 | 良 | Intelligent Computing Research Center, Harbin Institute of Technology(Shenzhen) | http://icrc.hitsz.edu.cn/info/1037/1162.htm BQ 语料库包含来自网上银行自定义服务日志的 120，000 个问题对。它分为三部分：100，000 对用于训练，10，000 对用于验证，10，000 对用于测试。 数据提供者： 哈尔滨工业大学（深圳）智能计算研究中心 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/BQ | 是 |
| lcqmc | 口语 | 149,226 | 文本分类 | 相似 | 良 | Ming Xu | 哈工大文本匹配数据集，LCQMC 是哈尔滨工业大学在自然语言处理国际顶会 COLING2018 构建的问题语义匹配数据集，其目标是判断两个问题的语义是否相同 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/LCQMC/train | 是 |
| paws-x | 百科 | 23,576 | 文本分类 | 相似 | 优 | Bhavitvya Malik | PAWS Wiki中的示例 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/paws-x/viewer/zh/train | 是 |
| wiki_atomic_edit | 百科 | 1,213,780 | 平行语义 | 相似 | 优 | abhishek thakur | 基于中文维基百科的编辑记录收集的数据集 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wiki_atomic_edits | 是 |
| chatmed_consult | 医药 | 549,326 | 问答 | 问答 | 优 | Wei Zhu | 真实世界的医学相关的问题，使用 gpt3.5 进行回答 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/michaelwzhu/ChatMed_Consult_Dataset | 否 |
| webqa | 百科 | 42,216 | 问答 | 问答 | 优 | suolyer | 百度于2016年开源的数据集，数据来自于百度知道；格式为一个问题多篇意思基本一致的文章，分为人为标注以及浏览器检索；数据整体质量中，因为混合了很多检索而来的文章 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/suolyer/webqa/viewer/suolyer--webqa/train?p=3 | 否 |
| dureader_robust | 百科 | 65,937 | 机器阅读理解 问答 | 问答 | 优 | 百度 | DuReader robust旨在利用真实应用中的数据样本来衡量阅读理解模型的鲁棒性，评测模型的过敏感性、过稳定性以及泛化能力，是首个中文阅读理解鲁棒性数据集。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/PaddlePaddle/dureader_robust/viewer/plain_text/train?row=96 | 否 |
| csl | 学术 | 395,927 | 语料 | 摘要 | 优 | Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao and Hui Zhang | 提供首个中文科学文献数据集（CSL），包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）。CSL 数据集可以作为预训练语料，也可以构建许多NLP任务，例如文本摘要（标题预测）、 关键词生成和文本分类等。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/neuclir/csl | 否 |
| miracl-corpus | 百科 | 4,934,368 | 语料 | 摘要 | 优 | MIRACL | The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., \n\n in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/miracl/miracl-corpus | 否 |
| lawzhidao | 法律 | 36,368 | 问答 | 问答 | 优 | 和鲸社区-Ustinian | 百度知道清洗后的法律问答 | 是 | 是 | 否 | 是 | https://www.heywhale.com/mw/dataset/5e953ca8e7ec38002d02fca7/content | 否 |
| CINLID | 成语 | 34,746 | 平行语义 | 相似 | 优 | 高长宽 | 中文成语语义推理数据集（Chinese Idioms Natural Language Inference Dataset）收集了106832条由人工撰写的成语对（含少量歇后语、俗语等短文本），通过人工标注的方式进行平衡分类，标签为entailment、contradiction和neutral，支持自然语言推理（NLI）的任务。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=39 | 是 |
| DuSQL | SQL | 25,003 | NL2SQL | SQL | 优 | 百度 | DuSQL是一个面向实际应用的数据集，包含200个数据库，覆盖了164个领域，问题覆盖了匹配、计算、推理等实际应用中常见形式。该数据集更贴近真实应用场景，要求模型领域无关、问题无关，且具备计算推理等能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=13 | 否 |
| Zhuiyi-NL2SQL | SQL | 45,918 | NL2SQL | SQL | 优 | 追一科技 刘云峰 | NL2SQL是一个多领域的简单数据集，其主要包含匹配类型问题。该数据集主要验证模型的泛化能力，其要求模型具有较强的领域泛化能力、问题泛化能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=12 | 否 |
| Cspider | SQL | 7,785 | NL2SQL | SQL | 优 | 西湖大学 张岳 | CSpider是一个多语言数据集，其问题以中文表达，数据库以英文存储，这种双语模式在实际应用中也非常常见，尤其是数据库引擎对中文支持不好的情况下。该数据集要求模型领域无关、问题无关，且能够实现多语言匹配。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=11 | 否 |
| news2016zh | 新闻 | 2,507,549 | 语料 | 摘要 | 良 | Bright Xu | 包含了250万篇新闻。新闻来源涵盖了6.3万个媒体，含标题、关键词、描述、正文。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| baike2018qa | 百科 | 1,470,142 | 问答 | 问答 | 良 | Bright Xu | 含有150万个预先过滤过的、高质量问题和答案，每个问题属于一个类别。总共有492个类别，其中频率达到或超过10次的类别有434个。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| webtext2019zh | 百科 | 4,258,310 | 问答 | 问答 | 优 | Bright Xu | 含有410万个预先过滤过的、高质量问题和回复。每个问题属于一个【话题】，总共有2.8万个各式话题，话题包罗万象。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 |
| SimCLUE | 百科 | 775,593 | 平行语义 | 相似 | 良 | 数据集合，请在 simCLUE 中查看 | 整合了中文领域绝大多数可用的开源的语义相似度和自然语言推理的数据集，并重新做了数据拆分和整理。 | 是 | 否 | 否 | 是 | https://github.com/CLUEbenchmark/SimCLUE | 是 |
| Chinese-SQuAD | 新闻 | 76,449 | 机器阅读理解 | 问答 | 优 | junzeng-pluto | 中文机器阅读理解数据集，通过机器翻译加人工校正的方式从原始Squad转换而来 | 是 | 否 | 否 | 是 | https://github.com/pluto-junzeng/ChineseSquad | 否 |
## 计划表
- [x] 完成 MTEB 中文评测 BenchMark, [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh)
- [ ] 完成 Large 模型的训练和开源
- [ ] 完成支持代码检索的模型
- [ ] 对 M3E 数据集进行清洗，保留高质量的部分，组成 m3e-hq，并在 huggingface 上开源
- [ ] 在 m3e-hq 的数据集上补充 hard negative 的样本及相似度分数，组成 m3e-hq-with-score，并在 huggingface 上开源
- [ ] 在 m3e-hq-with-score 上通过 [cosent loss](https://github.com/wangyuxinwhy/uniem/blob/main/uniem/criteria.py#LL24C39-L24C39) loss 进行训练并开源模型，CoSent 原理参考这篇[博客](https://kexue.fm/archives/8847)
- [ ] 开源商用版本的 M3E models
## 致谢
感谢开源社区提供的中文语料，感谢所有在此工作中提供帮助的人们，希望中文社区越来越好，共勉！
## License
M3E models 使用的数据集中包括大量非商用的数据集，所以 M3E models 也是非商用的，仅供研究使用。不过我们已经在 M3E 数据集上标识了商用和非商用的数据集，您可以根据自己的需求自行训练。
## Citation
Please cite this model using the following format:
```
@software {Moka Massive Mixed Embedding,
author = {Wang Yuxin,Sun Qingxuan,He sicheng},
title = {M3E: Moka Massive Mixed Embedding Model},
year = {2023}
}
```
|
hkaraoguz/BERT_swedish-ner
|
hkaraoguz
| 2023-06-21T11:23:35Z | 34 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"sv",
"dataset:wikiann",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-19T11:52:10Z |
---
tags:
- generated_from_trainer
language:
- sv
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_swedish-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: sv
split: train
args: sv
metrics:
- name: Precision
type: precision
value: 0.9340386115444618
- name: Recall
type: recall
value: 0.9418907624993855
- name: F1
type: f1
value: 0.9379482534942355
- name: Accuracy
type: accuracy
value: 0.979997105690534
widget:
- "Jag heter Peter Petersson och jag jobbar på Skatteverket. Jag bor i Uppsala."
inference:
parameters:
aggregation_strategy: "first"
---
<!-- 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_swedish-ner
This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1316
- Precision: 0.9340
- Recall: 0.9419
- F1: 0.9379
- Accuracy: 0.9800
## Model description
Finetuned the model from [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) for Swedish NER task. The model can classify three categories:
- PER (person names)
- LOC (Location)
- ORG (Organization)
## Intended uses & limitations
NER, token classification
## Training and evaluation data
wikiann-SV dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ChilNik/PR_digits
|
ChilNik
| 2023-06-21T11:23:24Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"code",
"dataset:mnist",
"region:us"
] | null | 2023-06-20T18:17:48Z |
---
datasets:
- mnist
library_name: keras
tags:
- code
---
Модель берет изображение (в данном случае из mnist) определяет цифру которая изображена, делит эту цифру на 2 и выводит остаток от деления.
Оптимизаторы: Adam
Размер тренировочного датасета: 60000
Размер валидационного датасета: 6000
Размер тестового датасета: 10000
Результаты обучения: Loss: 0.045721635222435, Accuracy: 0.9848999977111816
|
Knows-Nothing/GPT_2_FineTuned
|
Knows-Nothing
| 2023-06-21T11:23:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-04T18:18:46Z |
# Totally a Life Hacker
So this model was trained on https://lifehacks.stackexchange.com/.
Does it know life hacks ?
Depends on what is your definition of life hacks.
But it knows how to clean glass.
To use the model, ask a question and make sure to end it with "?"
because the model was trained on the format of "Question?Answer"
## Some Replies
And The Best One:
If you are interested in the code please refer to [Github](https://github.com/The-Sad-Zewalian/Knows-Nothing-Bot).
Have a great day!
|
kkkkkgsp/my_awesome_fashion_model
|
kkkkkgsp
| 2023-06-21T11:21:54Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:fashion_mnist",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T11:26:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fashion_mnist
metrics:
- accuracy
model-index:
- name: my_awesome_fashion_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: fashion_mnist
type: fashion_mnist
config: fashion_mnist
split: train[:5000]
args: fashion_mnist
metrics:
- name: Accuracy
type: accuracy
value: 0.785
---
<!-- 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. -->
# my_awesome_fashion_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fashion_mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8596
- Accuracy: 0.785
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3171 | 0.99 | 62 | 1.1960 | 0.755 |
| 0.947 | 2.0 | 125 | 0.8729 | 0.801 |
| 0.8346 | 2.98 | 186 | 0.8596 | 0.785 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
furusu/wd-v1-4-tagger-pytorch
|
furusu
| 2023-06-21T11:18:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-21T09:07:44Z |
---
license: apache-2.0
---
wd-v1-4-tagger for pytorch.
# models
* wd-v1-4-vit-tagger-v2.ckpt:
converted from https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2
|
reinforceYrWay/ppo-LunarLander-v2
|
reinforceYrWay
| 2023-06-21T11:17:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-20T13:33:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.50 +/- 21.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
joncam14/a2c-AntBulletEnv-v0
|
joncam14
| 2023-06-21T11:16:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T11:14:50Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1721.08 +/- 51.98
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Mu-mj/my_awesome_wnut_model
|
Mu-mj
| 2023-06-21T11:05:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-21T11:01:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5353846153846153
- name: Recall
type: recall
value: 0.32252085264133457
- name: F1
type: f1
value: 0.4025448235974552
- name: Accuracy
type: accuracy
value: 0.9434397845325125
---
<!-- 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. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
- Precision: 0.5354
- Recall: 0.3225
- F1: 0.4025
- Accuracy: 0.9434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2606 | 0.5326 | 0.2956 | 0.3802 | 0.9413 |
| No log | 2.0 | 426 | 0.2749 | 0.5354 | 0.3225 | 0.4025 | 0.9434 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
oooriii/natural2solr_biblio_ca
|
oooriii
| 2023-06-21T10:52:10Z | 0 | 0 | null |
[
"library",
"solr",
"ca",
"en",
"es",
"license:cc-by-4.0",
"region:us"
] | null | 2023-06-21T08:39:23Z |
---
license: cc-by-4.0
language:
- ca
- en
- es
tags:
- library
- solr
---
|
padalavinaybhushan/VinayParser
|
padalavinaybhushan
| 2023-06-21T10:50:53Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"layoutlm",
"document-question-answering",
"endpoints_compatible",
"region:us"
] |
document-question-answering
| 2023-05-31T02:25:27Z |
## About us
This model was created by the team at (DarwinBox|Vinay Bhushan|Rushikesh|Koushik).
|
joohwan/jko-gd
|
joohwan
| 2023-06-21T10:41:46Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-21T09:59:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: jko-gd
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. -->
# jko-gd
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1265
- Wer: 12.5734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2351 | 0.18 | 500 | 0.2485 | 27.5711 |
| 0.1147 | 0.36 | 1000 | 0.1657 | 15.8410 |
| 0.0814 | 0.54 | 1500 | 0.1265 | 12.5734 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
tianzhihui-isc/vit-base-patch16-224-in21k-finetuned-pokemon-classification
|
tianzhihui-isc
| 2023-06-21T10:38:17Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:pokemon-classification",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-21T10:19:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pokemon-classification
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-pokemon-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: pokemon-classification
type: pokemon-classification
config: full
split: train
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.8028747433264887
---
<!-- 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. -->
# vit-base-patch16-224-in21k-finetuned-pokemon-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8734
- Accuracy: 0.8029
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.9109 | 0.99 | 34 | 4.8193 | 0.1499 |
| 4.6433 | 1.99 | 68 | 4.5361 | 0.4661 |
| 4.2878 | 2.98 | 102 | 4.2546 | 0.6838 |
| 4.0792 | 4.0 | 137 | 4.0353 | 0.7680 |
| 3.8934 | 4.99 | 171 | 3.9120 | 0.7926 |
| 3.8294 | 5.96 | 204 | 3.8734 | 0.8029 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Varun1808/my_awesome_qa_model
|
Varun1808
| 2023-06-21T10:36:44Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-21T08:09:46Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Varun1808/my_awesome_qa_model
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. -->
# Varun1808/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0493
- Validation Loss: 3.1955
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.4426 | 3.5441 | 0 |
| 3.2901 | 3.1955 | 1 |
| 3.0493 | 3.1955 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ckpt/controlnet_qrcode-control_v1p_sd15
|
ckpt
| 2023-06-21T10:21:43Z | 13 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"controlnet",
"image-to-image",
"en",
"license:openrail++",
"region:us"
] |
image-to-image
| 2023-06-21T10:19:31Z |
---
tags:
- stable-diffusion
- controlnet
- image-to-image
license: openrail++
language:
- en
library_name: diffusers
pipeline_tag: image-to-image
---
# QR Code Conditioned ControlNet Models for Stable Diffusion 1.5
## Model Description
This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v1.5.
The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, this 1.5 version model was also trained on the same dataset for those who are using the older version.
## How to use with Diffusers
```bash
pip -q install diffusers transformers accelerate torch xformers
```
```python
import torch
from PIL import Image
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.utils import load_image
controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15",
torch_dtype=torch.float16)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
# play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image
# qr code image
source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png")
# initial image, anything
init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg")
condition_image = resize_for_condition_image(source_image, 768)
init_image = resize_for_condition_image(init_image, 768)
generator = torch.manual_seed(123121231)
image = pipe(prompt="a bilboard in NYC with a qrcode",
negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
image=init_image,
control_image=condition_image,
width=768,
height=768,
guidance_scale=20,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150,
)
image.images[0]
```
## Performance and Limitations
These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).**
To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
## Installation
The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application.
For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail.
Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.
|
readerbench/RoBERT-small
|
readerbench
| 2023-06-21T10:10:03Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Model card for RoBERT-small
---
language:
- ro
---
# RoBERT-small
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581/). Three BERT models were released: **RoBERT-small**, RoBERT-base and RoBERT-large, all versions uncased.
| Model | Weights | L | H | A | MLM accuracy | NSP accuracy |
|----------------|:---------:|:------:|:------:|:------:|:--------------:|:--------------:|
| *RoBERT-small* | *19M* | *12* | *256* | *8* | *0.5363* | *0.9687* |
| RoBERT-base | 114M | 12 | 768 | 12 | 0.6511 | 0.9802 |
| RoBERT-large | 341M | 24 | 1024 | 24 | 0.6929 | 0.9843 |
All models are available:
* [RoBERT-small](https://huggingface.co/readerbench/RoBERT-small)
* [RoBERT-base](https://huggingface.co/readerbench/RoBERT-base)
* [RoBERT-large](https://huggingface.co/readerbench/RoBERT-large)
#### How to use
```python
# tensorflow
from transformers import AutoModel, AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-small")
model = TFAutoModel.from_pretrained("readerbench/RoBERT-small")
inputs = tokenizer("exemplu de propoziție", return_tensors="tf")
outputs = model(inputs)
# pytorch
from transformers import AutoModel, AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-small")
model = AutoModel.from_pretrained("readerbench/RoBERT-small")
inputs = tokenizer("exemplu de propoziție", return_tensors="pt")
outputs = model(**inputs)
```
## Training data
The model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.
| Corpus | Words | Sentences | Size (GB)|
|-----------|:---------:|:---------:|:--------:|
| Oscar | 1.78B | 87M | 10.8 |
| RoTex | 240M | 14M | 1.5 |
| RoWiki | 50M | 2M | 0.3 |
| **Total** | **2.07B** | **103M** | **12.6** |
## Downstream performance
### Sentiment analysis
We report Macro-averaged F1 score (in %)
| Model | Dev | Test |
|------------------|:--------:|:--------:|
| multilingual-BERT| 68.96 | 69.57 |
| XLM-R-base | 71.26 | 71.71 |
| BERT-base-ro | 70.49 | 71.02 |
| *RoBERT-small* | *66.32* | *66.37* |
| RoBERT-base | 70.89 | 71.61 |
| RoBERT-large | **72.48**| **72.11**|
### Moldavian vs. Romanian Dialect and Cross-dialect Topic identification
We report results on [VarDial 2019](https://sites.google.com/view/vardial2019/campaign) Moldavian vs. Romanian Cross-dialect Topic identification Challenge, as Macro-averaged F1 score (in %).
| Model | Dialect Classification | MD to RO | RO to MD |
|-------------------|:----------------------:|:--------:|:--------:|
| 2-CNN + SVM | 93.40 | 65.09 | 75.21 |
| Char+Word SVM | 96.20 | 69.08 | 81.93 |
| BiGRU | 93.30 | **70.10**| 80.30 |
| multilingual-BERT | 95.34 | 68.76 | 78.24 |
| XLM-R-base | 96.28 | 69.93 | 82.28 |
| BERT-base-ro | 96.20 | 69.93 | 78.79 |
| *RoBERT-small* | *95.67* | *69.01* | *80.40* |
| RoBERT-base | 97.39 | 68.30 | 81.09 |
| RoBERT-large | **97.78** | 69.91 | **83.65**|
### Diacritics Restoration
Challenge can be found [here](https://diacritics-challenge.speed.pub.ro/). We report results on the official test set, as accuracies in %.
| Model | word level | char level |
|-----------------------------|:----------:|:----------:|
| BiLSTM | 99.42 | - |
| CharCNN | 98.40 | 99.65 |
| CharCNN + multilingual-BERT | 99.72 | 99.94 |
| CharCNN + XLM-R-base | 99.76 | **99.95** |
| CharCNN + BERT-base-ro | **99.79** | **99.95** |
| *CharCNN + RoBERT-small* | *99.73* | *99.94* |
| CharCNN + RoBERT-base | 99.78 | **99.95** |
| CharCNN + RoBERT-large | 99.76 | **99.95** |
### BibTeX entry and citation info
```bibtex
@inproceedings{masala2020robert,
title={RoBERT--A Romanian BERT Model},
author={Masala, Mihai and Ruseti, Stefan and Dascalu, Mihai},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6626--6637},
year={2020}
}
```
|
readerbench/RoBERT-large
|
readerbench
| 2023-06-21T10:01:42Z | 175 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Model card for RoBERT-large
---
language:
- ro
---
# RoBERT-large
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581/). Three BERT models were released: RoBERT-small, RoBERT-base and **RoBERT-large**, all versions uncased.
| Model | Weights | L | H | A | MLM accuracy | NSP accuracy |
|----------------|:---------:|:------:|:------:|:------:|:--------------:|:--------------:|
| RoBERT-small | 19M | 12 | 256 | 8 | 0.5363 | 0.9687 |
| RoBERT-base | 114M | 12 | 768 | 12 | 0.6511 | 0.9802 |
| *RoBERT-large* | *341M* | *24* | *1024* | *24* | *0.6929* | *0.9843* |
All models are available:
* [RoBERT-small](https://huggingface.co/readerbench/RoBERT-small)
* [RoBERT-base](https://huggingface.co/readerbench/RoBERT-base)
* [RoBERT-large](https://huggingface.co/readerbench/RoBERT-large)
#### How to use
```python
# tensorflow
from transformers import AutoModel, AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-large")
model = TFAutoModel.from_pretrained("readerbench/RoBERT-large")
inputs = tokenizer("exemplu de propoziție", return_tensors="tf")
outputs = model(inputs)
# pytorch
from transformers import AutoModel, AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-large")
model = AutoModel.from_pretrained("readerbench/RoBERT-large")
inputs = tokenizer("exemplu de propoziție", return_tensors="pt")
outputs = model(**inputs)
```
## Training data
The model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.
| Corpus | Words | Sentences | Size (GB)|
|-----------|:---------:|:---------:|:--------:|
| Oscar | 1.78B | 87M | 10.8 |
| RoTex | 240M | 14M | 1.5 |
| RoWiki | 50M | 2M | 0.3 |
| **Total** | **2.07B** | **103M** | **12.6** |
## Downstream performance
### Sentiment analysis
We report Macro-averaged F1 score (in %)
| Model | Dev | Test |
|------------------|:--------:|:--------:|
| multilingual-BERT| 68.96 | 69.57 |
| XLM-R-base | 71.26 | 71.71 |
| BERT-base-ro | 70.49 | 71.02 |
| RoBERT-small | 66.32 | 66.37 |
| RoBERT-base | 70.89 | 71.61 |
| *RoBERT-large* | **72.48**| **72.11**|
### Moldavian vs. Romanian Dialect and Cross-dialect Topic identification
We report results on [VarDial 2019](https://sites.google.com/view/vardial2019/campaign) Moldavian vs. Romanian Cross-dialect Topic identification Challenge, as Macro-averaged F1 score (in %).
| Model | Dialect Classification | MD to RO | RO to MD |
|-------------------|:----------------------:|:--------:|:--------:|
| 2-CNN + SVM | 93.40 | 65.09 | 75.21 |
| Char+Word SVM | 96.20 | 69.08 | 81.93 |
| BiGRU | 93.30 | **70.10**| 80.30 |
| multilingual-BERT | 95.34 | 68.76 | 78.24 |
| XLM-R-base | 96.28 | 69.93 | 82.28 |
| BERT-base-ro | 96.20 | 69.93 | 78.79 |
| RoBERT-small | 95.67 | 69.01 | 80.40 |
| RoBERT-base | 97.39 | 68.30 | 81.09 |
| *RoBERT-large* | **97.78** | *69.91* | **83.65**|
### Diacritics Restoration
Challenge can be found [here](https://diacritics-challenge.speed.pub.ro/). We report results on the official test set, as accuracies in %.
| Model | word level | char level |
|-----------------------------|:----------:|:----------:|
| BiLSTM | 99.42 | - |
| CharCNN | 98.40 | 99.65 |
| CharCNN + multilingual-BERT | 99.72 | 99.94 |
| CharCNN + XLM-R-base | 99.76 | **99.95** |
| CharCNN + BERT-base-ro | **99.79** | **99.95** |
| CharCNN + RoBERT-small | 99.73 | 99.94 |
| CharCNN + RoBERT-base | 99.78 | **99.95** |
| *CharCNN + RoBERT-large* | *99.76* | **99.95** |
### BibTeX entry and citation info
```bibtex
@inproceedings{masala2020robert,
title={RoBERT--A Romanian BERT Model},
author={Masala, Mihai and Ruseti, Stefan and Dascalu, Mihai},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6626--6637},
year={2020}
}
```
|
yuvalkirstain/sd15_cat
|
yuvalkirstain
| 2023-06-21T09:58:06Z | 1 | 0 |
diffusers
|
[
"diffusers",
"if",
"if-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-inpainting",
"base_model:finetune:runwayml/stable-diffusion-inpainting",
"license:creativeml-openrail-m",
"diffusers:StableDiffusionInpaintPipeline",
"region:us"
] |
text-to-image
| 2023-06-21T09:25:40Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-inpainting
instance_prompt: a photo of sks cat
tags:
- if
- if-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - yuvalkirstain/sd15_cat
This is a dreambooth model derived from runwayml/stable-diffusion-inpainting. The weights were trained on a photo of sks cat using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
AustinCarthy/MixGPT2V2_domain_100KP_BFall_fromP_95K_topP_0.75_ratio5
|
AustinCarthy
| 2023-06-21T09:39:38Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-06-21T03:23:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2V2_domain_100KP_BFall_fromP_95K_topP_0.75_ratio5
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. -->
# MixGPT2V2_domain_100KP_BFall_fromP_95K_topP_0.75_ratio5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2V2_using_phish_95K_top_p_0.75domain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0476
- Accuracy: 0.9976
- F1: 0.9743
- Precision: 0.9989
- Recall: 0.9508
- Roc Auc Score: 0.9754
- Tpr At Fpr 0.01: 0.9578
## 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0142 | 1.0 | 18282 | 0.0353 | 0.9976 | 0.9738 | 0.9971 | 0.9516 | 0.9757 | 0.9462 |
| 0.0092 | 2.0 | 36564 | 0.0423 | 0.9973 | 0.9713 | 0.9968 | 0.947 | 0.9734 | 0.9446 |
| 0.0033 | 3.0 | 54846 | 0.0431 | 0.9975 | 0.9732 | 0.9987 | 0.949 | 0.9745 | 0.9516 |
| 0.0005 | 4.0 | 73128 | 0.0438 | 0.9976 | 0.9737 | 0.9981 | 0.9504 | 0.9752 | 0.953 |
| 0.0 | 5.0 | 91410 | 0.0476 | 0.9976 | 0.9743 | 0.9989 | 0.9508 | 0.9754 | 0.9578 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AravindVadlapudi02/swinv2-base-patch4-window12to16-192to256-22kto1k-ft-finetuned-swinv2-final-augmented-72
|
AravindVadlapudi02
| 2023-06-21T09:36:27Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-21T09:36:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
sim11som11/t5_results1
|
sim11som11
| 2023-06-21T09:35:59Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-20T10:08:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_results1
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_results1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 57 | 0.2983 |
| No log | 2.0 | 114 | 0.2797 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
gokuls/hbertv2-emotion_48_emb_compress
|
gokuls
| 2023-06-21T09:32:33Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T09:15:44Z |
---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv2-emotion_48_emb_compress
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8572
---
<!-- 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. -->
# hbertv2-emotion_48_emb_compress
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5104
- Accuracy: 0.8572
## 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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5945 | 1.0 | 250 | 1.4950 | 0.478 |
| 1.352 | 2.0 | 500 | 1.1901 | 0.5595 |
| 1.0712 | 3.0 | 750 | 0.9287 | 0.651 |
| 0.8129 | 4.0 | 1000 | 0.7898 | 0.6955 |
| 0.6574 | 5.0 | 1250 | 0.7526 | 0.7335 |
| 0.5577 | 6.0 | 1500 | 0.6192 | 0.813 |
| 0.4418 | 7.0 | 1750 | 0.5638 | 0.8425 |
| 0.3931 | 8.0 | 2000 | 0.5432 | 0.8395 |
| 0.3536 | 9.0 | 2250 | 0.4958 | 0.8495 |
| 0.3184 | 10.0 | 2500 | 0.5104 | 0.851 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ratewar/ppo-LunarLander-v2
|
ratewar
| 2023-06-21T09:32:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T09:31:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.88 +/- 19.39
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Boristss/q-FrozenLake-v1-8x8-Slippery
|
Boristss
| 2023-06-21T09:26:14Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T09:24:55Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.14 +/- 0.35
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Boristss/q-FrozenLake-v1-8x8-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
KPrashanth/ppo-LunarLander-v2
|
KPrashanth
| 2023-06-21T09:20:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T09:20:19Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 286.77 +/- 22.81
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
kavlata/bert-base-uncased-finetuned-swag
|
kavlata
| 2023-06-21T09:17:14Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-06-21T09:15:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7193
- Accuracy: 0.1400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 38 | 1.7806 | 0.1867 |
| No log | 2.0 | 76 | 1.7043 | 0.2467 |
| No log | 3.0 | 114 | 1.7193 | 0.1400 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
gegre/q-FrozenLake-v1-4x4-noSlippery
|
gegre
| 2023-06-21T09:16:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T09:16:36Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gegre/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"])
```
|
swl-models/FuwaFuwaMix-v1.5
|
swl-models
| 2023-06-21T09:11:09Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-21T09:01:20Z |
---
license: creativeml-openrail-m
---
|
j5ng/et5-formal-convertor
|
j5ng
| 2023-06-21T09:02:42Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-21T08:39:24Z |
---
license: apache-2.0
language:
- ko
pipeline_tag: text2text-generation
---
# korean Formal Convertor Using Deep Learning
존댓말과 반말은 한국어에서만 존재합니다, 본 모델은 반말(informal)을 존댓말(formal)로 바꿔주는 변환기(convertor) 입니다. <br>
*확보한 존댓말 데이터셋에는 "해요체"와 "합쇼체" 두 종류가 존재했지만 본 모델은 "해요체"로 통일하여 변환하기로 결정했습니다.
|합쇼체|*해요체|
|------|---|
|안녕하십니까.|안녕하세요.|
|좋은 아침입니다.|좋은 아침이에요.|
|바쁘시지 않았으면 좋겠습니다.|바쁘시지 않았으면 좋겠어요.|
## 배경
- 이전에 존댓말과 반말을 구분하는 분류기(https://github.com/jongmin-oh/korean-formal-classifier) 를 학습했습니다.<br>
분류기로 말투를 나눠 사용하려했지만, 상대적으로 존댓말의 비중이 적었고 반말을 존댓말로 바꾸어 존댓말 데이터의 비중을 늘리기위해 만들게 되었습니다.
## 한국어 존댓말 변환기
- 존댓말 변환기는 T5모델 아키텍쳐를 기반으로한 Text2Text generation Task를 수행함으로 반말을 존댓말로 변환하여 사용할 수 있습니다.
- 바로 사용하실 분들은 밑에 예제 코드 참고해서 huggingFace 모델('j5ng/et5-formal-convertor') 다운받아 사용하실 수 있습니다.
## Base on PLM model(ET5)
- ETRI(https://aiopen.etri.re.kr/et5Model)
## Base on Dataset
- AI허브(https://www.aihub.or.kr/) : 한국어 어체 변환 코퍼스
1. KETI 일상오피스 대화 1,254 문장
2. 수동태깅 병렬데이터
- 스마일게이트 말투 데이터 셋(korean SmileStyle Dataset)
### Preprocessing
1. 반말/존댓말 데이터 분리("해요체"만 분리)
- 스마일게이트 데이터에서 (['formal','informal']) 칼럼만 사용
- 수동태깅 병렬데이터에서 ["*.ban", "*.yo"] txt 파일만 사용
- KETI 일상오피스 데이터에서(["반말","해요체"]) 칼럼만 사용
2. 데이터 셋 병합(3가지 데이터 셋 병합)
3. 마침표(.)와 쉼표(,)제거
4. 반말(informal) 칼럼 중복 제거 : 1632개 중복데이터 제거
### 최종 학습데이터 예시
|informal|formal|
|------|---|
|응 고마워|네 감사해요|
|나도 그 책 읽었어 굉장히 웃긴 책이였어|저도 그 책 읽었습니다 굉장히 웃긴 책이였어요|
|미세먼지가 많은 날이야|미세먼지가 많은 날이네요|
|괜찮겠어?|괜찮으실까요?|
|아니야 회의가 잠시 뒤에 있어 준비해줘|아니에요 회의가 잠시 뒤에 있어요 준비해주세요|
#### total : 14,992 쌍
***
## How to use
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
# T5 모델 로드
model = T5ForConditionalGeneration.from_pretrained("j5ng/et5-formal-convertor")
tokenizer = T5Tokenizer.from_pretrained("j5ng/et5-formal-convertor")
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# device = "mps:0" if torch.cuda.is_available() else "cpu" # for mac m1
model = model.to(device)
# 예시 입력 문장
input_text = "나 진짜 화났어 지금"
# 입력 문장 인코딩
input_encoding = tokenizer("존댓말로 바꿔주세요: " + input_text, return_tensors="pt")
input_ids = input_encoding.input_ids.to(device)
attention_mask = input_encoding.attention_mask.to(device)
# T5 모델 출력 생성
output_encoding = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_length=128,
num_beams=5,
early_stopping=True,
)
# 출력 문장 디코딩
output_text = tokenizer.decode(output_encoding[0], skip_special_tokens=True)
# 결과 출력
print(output_text) # 저 진짜 화났습니다 지금.
```
***
## With Transformer Pipeline
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
model = T5ForConditionalGeneration.from_pretrained('j5ng/et5-formal-convertor')
tokenizer = T5Tokenizer.from_pretrained('j5ng/et5-formal-convertor')
typos_corrector = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer,
device=0 if torch.cuda.is_available() else -1,
framework="pt",
)
input_text = "널 가질 수 있을거라 생각했어"
output_text = typos_corrector("존댓말로 바꿔주세요: " + input_text,
max_length=128,
num_beams=5,
early_stopping=True)[0]['generated_text']
print(output_text) # 당신을 가질 수 있을거라 생각했습니다.
```
## Thanks to
존댓말 변환기의 학습은 인공지능산업융합사업단(AICA)의 GPU 리소스를 지원받아 학습되었습니다.
|
IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1
|
IDEA-CCNL
| 2023-06-21T09:01:46Z | 1,495 | 19 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"arxiv:2210.08590",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-01T02:43:52Z |
---
license: gpl-3.0
language:
- en
- zh
inference: false
---
# Ziya-LLaMA-13B-Pretrain-v1
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
（LLaMA权重的许可证限制，我们无法直接发布完整的模型权重，用户需要参考[使用说明](#-使用-usage-)进行合并)
# 姜子牙系列模型
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)
- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1)
## 简介 Brief Introduction
Ziya-LLaMA-13B-Pretrain-v1 是基于LLaMa的130亿参数大规模预训练模型，针对中文分词优化，并完成了中英文 110B tokens 的增量预训练，进一步提升了中文生成和理解能力。目前姜子牙通用大模型 [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) 在本模型上，进一步完成了多任务有监督微调和人类反馈学习阶段的训练过程，具备翻译，编程，文本分类，信息抽取，摘要，文案生成，常识问答和数学计算等能力。
**用户须知**：为了遵循 Meta 发布的 LLaMA 模型许可，本模型发布的是训练前后的权重增量，最终模型可方便地通过脚本获得（参考 Usage 中的步骤）。
The Ziya-LLaMA-13B-Pretrain-v1 is a large-scale pre-trained model based on LLaMA with 13 billion parameters. We optimizes LLaMAtokenizer on chinese, and incrementally train 110 billion tokens of data based on LLaMa-13B model, which significantly improved the understanding and generation ability on Chinese. Based on the Ziya-LLaMA-13B-Pretrain-v1, the [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) is furtherly trained with 2 stages: multi-task supervised fine-tuning (SFT), and human feedback learning (RM, PPO). The Ziya-LLaMA-13B-v1 has the ability to perform tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation.
**README**: To follow the License of LLaMA released by Meta, we only release the incremental weights after continual pretraining. The final model Ziya-LLaMA-13B-Pretrain-v1 could be easily got via the script (refer to Usage).
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | AGI模型 | 姜子牙 Ziya | LLaMA | 13B | English&Chinese |
## 模型信息 Model Information
### 继续预训练 Continual Pretraining
原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。
为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了7k+个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。
在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。据我们所知，这也是至今为止LLaMA-13B上最大规模增量训练。
训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。
The original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of data.
To address the issue of low efficiency in Chinese encoding and decoding caused by the tokenizer of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa SentencePiece vocabulary. Deduplicating with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.
During the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data based on LLaMa-13B model in just 8 days.As far as we know, it is the largest increamental training on LLaMA-13B so far.
Throughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.
<img src="https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1/resolve/main/loss.png" width=1000 height=600>
### 效果评估 Performance
以下是 Ziya-LLaMA-13B-Pertrain-v1 和继续训练前的LLaMA 模型在英文公开评测 [HeLM](https://crfm.stanford.edu/helm/latest/) 和中文多项选择评测集上的评估效果对比。
Here are comparisons of the Ziya-LLaMA-13B-Pretrain-v1 model and the LLaMA model before continual pre-training, evaluated on the English benchmark (HeLM), and our Chinese multiple-choice evaluation datasets.
<img src="https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1/resolve/main/ziya_en_eval.png" width=2542 height=1045>
| Model | Meanwin_rate | MMLU | BoolQ | NarrativeQA | NaturalQuestion(closed-book) | NaturalQuestion(open-book) | QuAC | TruthfulQA | IMDB |
| -------------------------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
| LLaMA-13B | 0.500 | 0.424 | 0.718 | 0.440 | 0.349 | 0.591 | 0.318 | 0.326 | 0.487 |
| Ziya-LLaMA-13B-Pretrain-v1 | 0.650 | 0.433 | 0.753 | 0.445 | 0.348 | 0.528 | 0.335 | 0.249 | 0.497 |
<img src="https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1/resolve/main/ziya_zh_eval.png" width=2340 height=1523>
| 模型 | incontext | c3 | 常识 | 语文 | 数学 | 英语 | 物理 | 化学 | 生物 | 历史 | 政治 | 地理 |
|-------------------------|------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| LLaMA-13B | 0-shot | 0.4817 | 0.3088 | 0.2674 | 0.2882 | 0.3399 | 0.2581 | 0.2478 | 0.2271 | 0.3380 | 0.3275 | 0.296 |
| Ziya-LLaMA-13B-Pretrain-v1 | 0-shot | 0.5354 | 0.3373 | 0.2925 | 0.3059 | 0.3428 | 0.2903 | 0.2655 | 0.3215 | 0.4190 | 0.4123 | 0.4425 |
| LLaMA-13B | 5-shot | 0.5314 | 0.3586 | 0.2813 | 0.2912 | 0.4476 | 0.2939 | 0.2301 | 0.2330 | 0.3268 | 0.3187 | 0.3103 |
| Ziya-LLaMA-13B-Pretrain-v1 | 5-shot | 0.6037 | 0.4330 | 0.2802 | 0.2912 | 0.4363 | 0.2975 | 0.2802 | 0.3422 | 0.4358 | 0.4357 | 0.4540 |
<!--
<img src="" width=1000 height=600> -->
## <span id="jump"> 使用 Usage </span>
由于LLaMA权重的许可限制，该模型不能用于商业用途，请严格遵守LLaMA的使用政策。考虑到LLaMA权重的许可证限制，我们无法直接发布完整的模型权重。因此，我们使用了[FastChat开源工具](https://github.com/lm-sys/FastChat/blob/main/fastchat/model/apply_delta.py)作为基础，并对其进行了进一步的优化。我们计算并发布了Ziya-LLaMA-13B-v1权重与原始LLaMA权重之间的差值。用户可以按照以下步骤操作以获得Ziya-LLaMA-13B-v1完整权重，具体步骤如下：
Step 1:获取[LLaMA](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform)权重并转成Hugging Face Transformers模型格式，可参考转换[脚本](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)（若已经有huggingface权重则跳过）
```
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
--input_dir /path/to/downloaded/llama/weights --model_size 13B --output_dir /output/path
```
Step 2:下载Ziya-LLaMA-13B-v1的delta权重以及step 1中转换好的原始LLaMA权重，使用如下脚本转换：https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/utils/apply_delta.py.
```
python3 -m apply_delta --base ~/model_weights/llama-13b --target ~/model_weights/Ziya-LLaMA-13B --delta ~/model_weights/Ziya-LLaMA-13B-v1
```
Step 3: 加载step 2得到的模型推理
```python3
from transformers import AutoTokenizer
from transformers import LlamaForCausalLM
import torch
device = torch.device("cuda")
query="帮我写一份去西安的旅游计划"
model = LlamaForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(ckpt)
inputs = query.strip()
input_ids = tokenizer(inputs, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(
input_ids,
max_new_tokens=1024,
do_sample = True,
top_p = 0.85,
temperature = 1.0,
repetition_penalty=1.,
eos_token_id=2,
bos_token_id=1,
pad_token_id=0)
output = tokenizer.batch_decode(generate_ids)[0]
print(output)
```
Step 1: Obtain the [LLaMA](https://huggingface.co/docs/transformers/main/en/model_doc/llama#overview) weights and convert them into the Hugging Face Transformers format. You can refer to the [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) (skip this step if you already have the Hugging Face weights).
```
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
--input_dir /path/to/downloaded/llama/weights --model_size 13B --output_dir /output/path
```
Step 2: Download the delta weights for Ziya-LLaMA-13B-v1 and the pre-converted original LLaMA weights from step 1. Use the following script for conversion: https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/utils/apply_delta.py
```
python3 -m apply_delta --base ~/model_weights/llama-13b --target ~/model_weights/Ziya-LLaMA-13B --delta ~/model_weights/Ziya-LLaMA-13B-v1(huggingface下载)
```
Step 3: Load the model obtained in Step 2 for inference.
## 微调示例 Finetune Example
Refer to [ziya_finetune](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_llama)
## 推理量化示例 Inference & Quantization Example
Refer to [ziya_inference](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_inference)
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
gokuls/bert-base-emotion_24
|
gokuls
| 2023-06-21T08:57:27Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T08:33:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-base-emotion_24
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8611
---
<!-- 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-emotion_24
This model is a fine-tuned version of [gokuls/bert_base_24](https://huggingface.co/gokuls/bert_base_24) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5612
- Accuracy: 0.8611
## 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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.736 | 1.0 | 250 | 0.4242 | 0.8565 |
| 0.3013 | 2.0 | 500 | 0.3314 | 0.8845 |
| 0.2014 | 3.0 | 750 | 0.3442 | 0.8905 |
| 0.1392 | 4.0 | 1000 | 0.3276 | 0.8915 |
| 0.1072 | 5.0 | 1250 | 0.3833 | 0.89 |
| 0.0783 | 6.0 | 1500 | 0.4205 | 0.8895 |
| 0.0559 | 7.0 | 1750 | 0.5287 | 0.8865 |
| 0.0378 | 8.0 | 2000 | 0.5459 | 0.8865 |
| 0.027 | 9.0 | 2250 | 0.5612 | 0.8925 |
| 0.02 | 10.0 | 2500 | 0.5601 | 0.8915 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
|
wordcab/whisper-large-int8-he
|
wordcab
| 2023-06-21T08:47:20Z | 1 | 0 |
transformers
|
[
"transformers",
"he",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-20T15:35:55Z |
---
license: apache-2.0
language:
- he
---
This is a ctranslate2 `int8` version of the [Shiry/whisper-large-v2-he](https://huggingface.co/Shiry/whisper-large-v2-he) model.
|
JorgeSarry/classify-setfit
|
JorgeSarry
| 2023-06-21T08:43:54Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-06-21T08:43:01Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# JorgeSarry/classify-setfit
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("JorgeSarry/classify-setfit")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
wordcab/whisper-large-fp16-he
|
wordcab
| 2023-06-21T08:43:07Z | 1 | 0 |
transformers
|
[
"transformers",
"he",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-20T15:22:14Z |
---
license: apache-2.0
language:
- he
---
This is a ctranslate2 `float16` version of the [Shiry/whisper-large-v2-he](https://huggingface.co/Shiry/whisper-large-v2-he) model.
|
Subsets and Splits
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