modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
distilbert-base-german-cased
|
[
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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| 43,667
| 2023-03-29T09:33:19Z
|
---
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: 1520.14 +/- 97.99
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
...
```
|
IssakaAI/wav2vec2-large-xls-r-300m-turkish-colab
|
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}
| 0
| 2023-03-29T11:41:52Z
|
---
title: Chatbot With Vits
emoji: 🚀
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 3.23.0
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
ALINEAR/albert-japanese-v2
|
[
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"AlbertForMaskedLM"
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}
| 20,882
| 2023-03-29T12:10:42Z
|
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rhenus_v2.0_cleared
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.9566115702479339
- name: Recall
type: recall
value: 0.9566115702479339
- name: F1
type: f1
value: 0.9566115702479339
- name: Accuracy
type: accuracy
value: 0.955607476635514
---
<!-- 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. -->
# rhenus_v2.0_cleared
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2695
- Precision: 0.9566
- Recall: 0.9566
- F1: 0.9566
- Accuracy: 0.9556
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.91 | 100 | 2.3292 | 0.2072 | 0.1074 | 0.1415 | 0.3949 |
| No log | 1.82 | 200 | 1.5407 | 0.6009 | 0.5475 | 0.5730 | 0.6939 |
| No log | 2.73 | 300 | 0.9994 | 0.7174 | 0.6715 | 0.6937 | 0.8084 |
| No log | 3.64 | 400 | 0.7563 | 0.7692 | 0.7645 | 0.7668 | 0.8435 |
| 1.6153 | 4.55 | 500 | 0.5786 | 0.8071 | 0.8037 | 0.8054 | 0.8692 |
| 1.6153 | 5.45 | 600 | 0.5156 | 0.7988 | 0.7955 | 0.7971 | 0.8680 |
| 1.6153 | 6.36 | 700 | 0.4419 | 0.8693 | 0.8657 | 0.8675 | 0.8984 |
| 1.6153 | 7.27 | 800 | 0.3940 | 0.8786 | 0.8822 | 0.8804 | 0.9077 |
| 1.6153 | 8.18 | 900 | 0.3359 | 0.9156 | 0.9194 | 0.9175 | 0.9252 |
| 0.4054 | 9.09 | 1000 | 0.3066 | 0.9262 | 0.9339 | 0.9300 | 0.9381 |
| 0.4054 | 10.0 | 1100 | 0.2570 | 0.9270 | 0.9442 | 0.9355 | 0.9568 |
| 0.4054 | 10.91 | 1200 | 0.2439 | 0.9406 | 0.9483 | 0.9444 | 0.9603 |
| 0.4054 | 11.82 | 1300 | 0.2378 | 0.9446 | 0.9504 | 0.9475 | 0.9614 |
| 0.4054 | 12.73 | 1400 | 0.2502 | 0.9388 | 0.9504 | 0.9446 | 0.9521 |
| 0.1461 | 13.64 | 1500 | 0.2008 | 0.9569 | 0.9628 | 0.9598 | 0.9696 |
| 0.1461 | 14.55 | 1600 | 0.2454 | 0.9446 | 0.9504 | 0.9475 | 0.9544 |
| 0.1461 | 15.45 | 1700 | 0.2234 | 0.9609 | 0.9649 | 0.9629 | 0.9673 |
| 0.1461 | 16.36 | 1800 | 0.2408 | 0.9526 | 0.9545 | 0.9536 | 0.9544 |
| 0.1461 | 17.27 | 1900 | 0.2620 | 0.9545 | 0.9545 | 0.9545 | 0.9544 |
| 0.0693 | 18.18 | 2000 | 0.2170 | 0.9588 | 0.9628 | 0.9608 | 0.9673 |
| 0.0693 | 19.09 | 2100 | 0.2408 | 0.9588 | 0.9607 | 0.9598 | 0.9603 |
| 0.0693 | 20.0 | 2200 | 0.2450 | 0.9506 | 0.9545 | 0.9526 | 0.9556 |
| 0.0693 | 20.91 | 2300 | 0.2437 | 0.9545 | 0.9545 | 0.9545 | 0.9544 |
| 0.0693 | 21.82 | 2400 | 0.2173 | 0.9548 | 0.9607 | 0.9578 | 0.9685 |
| 0.0472 | 22.73 | 2500 | 0.2428 | 0.9485 | 0.9504 | 0.9494 | 0.9626 |
| 0.0472 | 23.64 | 2600 | 0.2659 | 0.9525 | 0.9525 | 0.9525 | 0.9533 |
| 0.0472 | 24.55 | 2700 | 0.2713 | 0.9566 | 0.9566 | 0.9566 | 0.9556 |
| 0.0472 | 25.45 | 2800 | 0.2803 | 0.9545 | 0.9545 | 0.9545 | 0.9544 |
| 0.0472 | 26.36 | 2900 | 0.2779 | 0.9566 | 0.9566 | 0.9566 | 0.9556 |
| 0.0356 | 27.27 | 3000 | 0.2695 | 0.9566 | 0.9566 | 0.9566 | 0.9556 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2
|
ARATHI/electra-small-discriminator-fintuned-cola
|
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| 0
| 2023-03-29T12:12:58Z
|
# Vocabulary Trimmed [lmqg/mt5-small-squad-qg](https://huggingface.co/lmqg/mt5-small-squad-qg): `vocabtrimmer/mt5-small-squad-qg-trimmed-en`
This model is a trimmed version of [lmqg/mt5-small-squad-qg](https://huggingface.co/lmqg/mt5-small-squad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-squad-qg | vocabtrimmer/mt5-small-squad-qg-trimmed-en |
|:---------------------------|:--------------------------|:---------------------------------------------|
| parameter_size_full | 300,165,504 | 258,414,976 |
| parameter_size_embedding | 256,103,424 | 214,352,896 |
| vocab_size | 250,101 | 209,329 |
| compression_rate_full | 100.0 | 86.09 |
| compression_rate_embedding | 100.0 | 83.7 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | | 2 |
|
AdapterHub/bert-base-uncased-pf-scitail
|
[
"bert",
"en",
"dataset:scitail",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/scitail"
] |
text-classification
|
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| 2
| 2023-03-29T15:13:11Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TerryYH/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"])
```
|
AdapterHub/roberta-base-pf-boolq
|
[
"roberta",
"en",
"dataset:boolq",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:qa/boolq"
] |
text-classification
|
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}
| 36
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: feabries/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Adil617/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
],
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| 4
| null |
---
license: cc
---
# Random celebrities voice models
### Currently available:
- James Hetfield (https://youtu.be/8ektmj8GMqQ)
- Lady Gaga (https://youtu.be/I5BgH8mPplY)
- Eddie Vedder
- Marina Sena
- Chris Cornell
- Tim Maia
- Eric Cartman (https://youtu.be/noh2dRdkP1Y)
- David Bowie (https://youtu.be/nCYLG2WXKmg)
- Phil Anselmo (https://youtu.be/ijiU28SK3cw)
- Stevie Ray Vaughan (https://youtu.be/Wr96qwTgw4M)
- Liam Gallagher (https://youtu.be/CKVXjCfttaQ)
- Noel Gallagher (https://youtu.be/OaVRUzZ6qqI)
- Parappa the Rapper (https://youtu.be/W08bpagIn44)
|
Ajay191191/autonlp-Test-530014983
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
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| 34
| 2023-03-29T18:04:39Z
|
---
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
|
adorkin/xlm-roberta-en-ru-emoji
|
[
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:tweet_eval",
"transformers"
] |
text-classification
|
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}
| 31
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 230329tanavaksha-0-1 Dreambooth model trained by arthur-nvk with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AnonymousSub/AR_rule_based_twostagetriplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 6
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
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| 4
| 2023-03-29T19:16:01Z
|
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: GGunjan/SnowballTarget-v1-ppo
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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}
| 10
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.66 +/- 0.21
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
...
```
|
AnonymousSub/unsup-consert-base_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 6
| null |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="markeidsaune/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis
|
[] | null |
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| 0
| null |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-squad-qg](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg): `vocabtrimmer/mbart-large-cc25-squad-qg-trimmed-en-30000`
This model is a trimmed version of [lmqg/mbart-large-cc25-squad-qg](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-squad-qg | vocabtrimmer/mbart-large-cc25-squad-qg-trimmed-en-30000 |
|:---------------------------|:---------------------------------|:----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 385,548,288 |
| parameter_size_embedding | 512,057,344 | 61,448,192 |
| vocab_size | 250,028 | 30,004 |
| compression_rate_full | 100.0 | 63.12 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 30000 | 2 |
|
Anubhav23/IndianlegalBert
|
[] | null |
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| 0
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: anthonyx/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayoola/wav2vec2-large-xlsr-turkish-demo-colab
|
[] | null |
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}
| 0
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: eLarry/poca-SoccerTwos-v3-Self-Aware
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Azura/data
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: keyword_category_classifier_v5
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. -->
# keyword_category_classifier_v5
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: 0.2458
- Accuracy: 0.9281
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7236 | 1.0 | 986 | 0.2750 | 0.9139 |
| 0.2326 | 2.0 | 1972 | 0.2458 | 0.9281 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BSC-LT/roberta-base-ca
|
[
"pytorch",
"roberta",
"fill-mask",
"ca",
"transformers",
"masked-lm",
"BERTa",
"catalan",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
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}
| 18
| null |
---
license: mit
---
### channelized on Stable Diffusion
This is the `<channelized>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
BSC-LT/roberta-large-bne
|
[
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
],
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}
| 24
| 2023-03-29T21:42:22Z
|
---
license: apache-2.0
model-index:
- name: newt5en-to-bn2
results: []
pipeline_tag: translation
---
<!-- 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. -->
# newt5en-to-bn2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0303
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2145 | 1.0 | 94 | 0.1407 |
| 0.0963 | 2.0 | 188 | 0.0358 |
| 0.0806 | 3.0 | 282 | 0.0303 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Babysittingyoda/DialoGPT-small-familyguy
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 13
| null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('jackoyoungblood/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Bagus/SER-LSSED
|
[] | null |
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| 0
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.58 +/- 23.58
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
...
```
|
Bagus/wav2vec2-large-xlsr-bahasa-indonesia
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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}
| 12
| null |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
"Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."]
example_title: Not Equivalent
- text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.",
"With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."]
example_title: Equivalent
model-index:
- name: platzi-distilroberta-base-mrpc-glue-david-garcia
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7965686274509803
- name: F1
type: f1
value: 0.8623548922056385
---
<!-- 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. -->
# platzi-distilroberta-base-mrpc-glue-david-garcia
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6754
- Accuracy: 0.7966
- F1: 0.8624
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.526 | 1.09 | 500 | 0.6754 | 0.7966 | 0.8624 |
| 0.3485 | 2.18 | 1000 | 0.6995 | 0.8309 | 0.8783 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition
|
[
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
] |
audio-classification
|
{
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"HubertForSequenceClassification"
],
"model_type": "wav2vec2",
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}
| 26
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: worknewt5en-to-bn2
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. -->
# worknewt5en-to-bn2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0002 | 1.0 | 4688 | 0.0000 |
| 0.0 | 2.0 | 9376 | 0.0000 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Balgow/prod_desc
|
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| 0
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.11 +/- 25.27
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
...
```
|
Banshee/dialoGPT-small-luke
|
[] | null |
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| 0
| null |
---
license: afl-3.0
metrics:
- accuracy
---
Ultra realistic image of Joe Biden eating mud. He is open mouth, Joe Biden is clearly eating mud Photography, RTX light, very detailed, 8K, realistic light, he brings the mud in his mouth with his hands. There is mud on joe biden but the rest of the picture is clean
|
Barleysack/AERoberta2
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 2
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/Gerbil-A-6.7m | 6.7m | A-Class | 20 | 134M | 131k | 6.074100 |
|
BatuhanYilmaz/bert-finetuned-ner
|
[] | null |
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}
| 0
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### my_wife_testing Dreambooth model trained by CrystalTea with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Bee-Garbs/DialoGPT-cartman-small
|
[] | null |
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}
| 0
| null |
---
language:
- pt
metrics:
- perplexity
pipeline_tag: text-generation
---
# Model Card for Model ID
A Portuguese language model trained on https://huggingface.co/facebook/opt-125m .
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Monique Monteiro
- **Shared by [optional]:** Monique Monteiro
- **Model type:** OPT
- **Language(s) (NLP):** Portuguese
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** facebook/opt-125m
Use the code below to get started with the model.
```python
generator = pipeline('text-generation', 'monilouise/opt125M_portuguese')
output = generator("Era uma vez", max_length=50, do_sample=True)
```
## Training Details
### Training Data
The model was trained on gs://unicamp-dl/ia025a_2022s1/aula9/sample-1gb.txt
### Training Procedure
The model was trained for 3 epochs, by using learning rate = 5e-5 (linear scheduler).
#### Preprocessing [optional]
All text was tokenized and broken into chunks of 1024 tokens.
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
#### Speeds, Sizes, Times [optional]
Training time: 17 hours
## Evaluation
The model was evaluated on a 5% validation split.
#### Metrics
Perplexity = 7.94.
## Model Card Authors [optional]
moniquelouise@gmail.com
## Model Card Contact
moniquelouise@gmail.com
|
BenWitter/DialoGPT-small-Tyrion
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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}
| 11
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/Gerbil-B-6.7m | 6.7m | B-Class | 42 | 281M | 131k | 5.513200 |
|
Benicio/t5-small-finetuned-en-to-ro
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-biolaysum-both-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. -->
# led-base-16384-biolaysum-both-all
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1320
- Rouge1: 0.4554
- Rouge2: 0.1583
- Rougel: 0.2462
- Rougelsum: 0.2464
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.2477 | 0.69 | 5000 | 2.1940 | 0.4501 | 0.1552 | 0.2441 | 0.2442 |
| 2.0151 | 1.37 | 10000 | 2.1320 | 0.4554 | 0.1583 | 0.2462 | 0.2464 |
| 1.8991 | 2.06 | 15000 | 2.0994 | 0.4561 | 0.1561 | 0.2436 | 0.2437 |
| 1.877 | 2.75 | 20000 | 2.0860 | 0.4588 | 0.1582 | 0.2452 | 0.2453 |
| 1.753 | 3.43 | 25000 | 2.0723 | 0.4566 | 0.1569 | 0.2455 | 0.2457 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.12.1
|
BertChristiaens/EmojiPredictor
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 6
| null |
---
license: apache-2.0
---
insert_at_4_nonfrozen_50rn_8heads_k=128.ckpt
* Final validation scores: acc@1=0.758 acc@5=0.92
* Trained for 4 epochs
```
# lightning.pytorch==2.0.0
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
fast_dev_run: false
max_epochs: 8
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 15
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 1.0
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/insert_at_4_nonfrozen_50rn_4heads_k=128
model:
resnet_type: 50
is_rq: false
quantizer_args:
heads: 8
use_cosine_sim: false
accept_image_fmap: true
codebook_dim: 256
codebook_size: 128
decay: 0.95
eps: 1.0e-05
commitment_weight: 0.0
threshold_ema_dead_code: 2
resnet_insertion_index: 4
unfreeze_resnet_block_indeces: [3]
unfreeze_fc: true
lr: 0.00010
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 6
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
epoch0-insert-at-4-frozen-deep-norq.ckpt
* trained to vall acc@5 0.887 acc@1 .6697
* big codebook size (256)
* 8 heads
```
# lightning.pytorch==2.0.0
# bigger depth
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
callbacks: null
fast_dev_run: false
max_epochs: 5
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 15
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 1.0
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/insert_at_4_frozen_deep
model:
resnet_type: 34
is_rq: false
quantizer_args:
heads: 8
use_cosine_sim: false
accept_image_fmap: true
codebook_dim: 128
codebook_size: 256
decay: 0.85
eps: 1.0e-05
commitment_weight: 5.0
threshold_ema_dead_code: 1
resnet_insertion_index: 4
unfreeze_resnet_block_indeces: []
unfreeze_fc: false
lr: 0.0002
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 6
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
insert_at_4_nonfrozen_deep_epoch=3-step=7759.ckpt
* small codebook size (64)
* trained to v acc@5 .8645 acc@1 0.6554
```
# lightning.pytorch==2.0.0
# bigger depth
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
callbacks: null
fast_dev_run: false
max_epochs: 5
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 15
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 1.0
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/insert_at_4_nonfrozen_deep
model:
resnet_type: 34
is_rq: false
quantizer_args:
heads: 8
use_cosine_sim: false
accept_image_fmap: true
codebook_dim: 128
codebook_size: 64
decay: 0.85
eps: 1.0e-05
commitment_weight: 0.5
threshold_ema_dead_code: 1
sample_codebook_temp: 0.1
resnet_insertion_index: 4
unfreeze_resnet_block_indeces:
- 2
- 3
unfreeze_fc: true
lr: 0.0001
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 6
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
epoch=1-step=4503.ckpt
* inserted at 3, all resnet weights frozen
* ~.62 val acc
```
# lightning.pytorch==2.0.0
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
callbacks: null
fast_dev_run: false
max_epochs: 10
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 5
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 0.5
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/test_insert_at_3_frozen
model:
resnet_type: 34
is_rq: true
quantizer_args:
num_quantizers: 4
shared_codebook: false
quantize_dropout: true
accept_image_fmap: true
codebook_dim: 128
codebook_size: 512
decay: 0.85
eps: 1.0e-05
commitment_weight: 25.0
threshold_ema_dead_code: 2
sample_codebook_temp: 0.05
quantize_dropout_cutoff_index: 1
quantize_dropout_multiple_of: 1
resnet_insertion_index: 3
lr: 0.0002
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 8
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
epoch2-val-63.ckpt
* final val acc .63
* trained for 2 epochs
* More compressed embedding space, with more dropout
* git commit dc54a9bdbfcfbc83c736ac5c06ab09c5acf2d5e8
```
# lightning.pytorch==2.0.0
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
callbacks: null
fast_dev_run: false
max_epochs: 10
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 5
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 0.5
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/insert_at_4
model:
resnet_type: 34
is_rq: true
quantizer_args:
num_quantizers: 8
shared_codebook: true
quantize_dropout: false
accept_image_fmap: true
codebook_dim: 128
codebook_size: 64
decay: 0.8
eps: 1.0e-05
commitment_weight: 5.0
threshold_ema_dead_code: 1
sample_codebook_temp: 0.1
resnet_insertion_index: 4
unfreeze_resnet_block_indeces:
- 3
unfreeze_fc: true
lr: 0.0002
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 6
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
### epoch=5-step=14765.ckpt
* trained for 5 1/2 epochs on imagenet, on top of resnet 34
* final validation accuracy: .66
* final training accuracy: 0.64
* git hash: `c4852331f9a40393b8ffd8b7b9a689d1ff6e1021`
* config:
```
# lightning.pytorch==2.0.0
seed_everything: true
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_last: true
save_top_k: 1
monitor: v_c_loss
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: 16-mixed
logger: null
callbacks: null
fast_dev_run: false
max_epochs: 10
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: 0.1
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 5
enable_checkpointing: true
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: 0.5
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: ckpt/test_insert_at_4
model:
resnet_type: 34
is_rq: true
quantizer_args:
num_quantizers: 4
shared_codebook: true
quantize_dropout: false
accept_image_fmap: true
codebook_dim: 128
codebook_size: 256
decay: 0.8
eps: 1.0e-05
commitment_weight: 5.0
threshold_ema_dead_code: 1
sample_codebook_temp: 0.0
resnet_insertion_index: 4
unfreeze_resnet_block_indeces:
- 3
unfreeze_fc: true
lr: 0.0002
data:
data_dir: "/home/figes/Downloads/ILSVRC2012_CLS-LOC/"
image_size: 224
num_workers: 8
batch_size: 512
shuffle: true
pin_memory: true
drop_last: false
```
|
Berzemu/Coco
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: LinearProbing01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.83152
---
<!-- 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. -->
# LinearProbing01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4013
- Accuracy: 0.8315
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4682 | 1.0 | 1563 | 0.4316 | 0.8251 |
| 0.4202 | 2.0 | 3126 | 0.4013 | 0.8315 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BhanuSama/gpt2-finetuned-xsum
|
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| 0
| null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Custom resolution versions are tagged accordingly.
- The `vae-ft-mse-840000-ema-pruned.ckpt` vae is embedded into the model.
- This model was converted with a `vae-encoder` for `image2image`.
- This model is `fp16`.
- Descriptions are posted as-is from original model source.
- Not all features and/or results may be available in CoreML format.
- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
- This model does not include a safety checker (for NSFW content).
# floralPatterns-v10:
Source(s): [Hugging Face](https://huggingface.co/Mobius-labs/floral_pattern) - [CivitAI](https://civitai.com/models/26295/floral-patterns)
This model was trained to generate floral patterns.
Join our [Discord](https://discord.gg/qM84dzKC) for any questions or feedback!
**Prompting**
Use the word **"pattern"** to trigger the style. You can also use negative prompt to get rid of unwanted colors if the positive prompt isn't providing enough control.
Example prompts
A yellow and pink pattern with flowers on white background
A yellow and pink pattern with birds and flowers on white background
A black and white pattern of butterflies in a jungle
The example images were all generated with fairly simple prompts, but feel free to experiment with more complex prompts as well as negative prompts.
**Settings**
Resolution must be **768 x 768** for best results
Sample images were generated with CFG = 11 and 75 inference steps.
Inference settings otherwise shouldn’t matter too much.
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi2-colab
|
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| 0
| null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 712.50 +/- 265.04
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 vijmeister -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 vijmeister -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 vijmeister
```
## 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)])
```
|
Bharathdamu/wav2vec2-model-hindi-stt
|
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| 0
| null |
---
license: bigscience-openrail-m
base_model: riffusion/riffusion-model-v1
datasets:
- rxk/MC_caption
language:
- en
tags:
- riffusion
---
|
Bia18/Beatriz
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CIS6930_DAAGR_T5_Emo
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. -->
# CIS6930_DAAGR_T5_Emo
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3253
- Train Accuracy: 0.9647
- Validation Loss: 0.4468
- Validation Accuracy: 0.9495
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4976 | 0.9412 | 0.4567 | 0.9459 | 0 |
| 0.4359 | 0.9482 | 0.4462 | 0.9474 | 1 |
| 0.4228 | 0.9502 | 0.4406 | 0.9484 | 2 |
| 0.4131 | 0.9517 | 0.4370 | 0.9488 | 3 |
| 0.4050 | 0.9528 | 0.4349 | 0.9493 | 4 |
| 0.3981 | 0.9539 | 0.4335 | 0.9496 | 5 |
| 0.3914 | 0.9548 | 0.4327 | 0.9498 | 6 |
| 0.3851 | 0.9558 | 0.4328 | 0.9500 | 7 |
| 0.3794 | 0.9565 | 0.4328 | 0.9501 | 8 |
| 0.3738 | 0.9574 | 0.4321 | 0.9502 | 9 |
| 0.3685 | 0.9582 | 0.4328 | 0.9502 | 10 |
| 0.3632 | 0.9589 | 0.4340 | 0.9502 | 11 |
| 0.3582 | 0.9597 | 0.4343 | 0.9501 | 12 |
| 0.3531 | 0.9605 | 0.4363 | 0.9501 | 13 |
| 0.3482 | 0.9612 | 0.4381 | 0.9501 | 14 |
| 0.3436 | 0.9619 | 0.4390 | 0.9500 | 15 |
| 0.3391 | 0.9626 | 0.4396 | 0.9500 | 16 |
| 0.3340 | 0.9633 | 0.4438 | 0.9499 | 17 |
| 0.3297 | 0.9640 | 0.4454 | 0.9498 | 18 |
| 0.3253 | 0.9647 | 0.4468 | 0.9495 | 19 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.11.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Biasface/DDDC
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 14
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/Gerbil-A-3.3m | 3.3m | A-Class | 20 | 60M | 65.5k | 6.664400 |
|
Biasface/DDDC2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: david3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 17.20 +/- 5.10
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
|
BigSalmon/BertaMyWorda
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8
| null |
---
license: apache-2.0
---
Out repository [flan-alpaca-lora](https://github.com/Reason-Wang/flan-alpaca-lora) contains the details to train flan-t5 with [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) instructions and [low-rank adaptation](https://arxiv.org/abs/2106.09685).
You can use the following code easily.
Usage:
```python
import transformers
from peft import PeftModel
model_name = "google/flan-t5-large"; peft_model_id = "reasonwang/flan-alpaca-lora-large"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name)
peft_model = PeftModel.from_pretrained(base_model, peft_model_id)
inputs = tokenizer("List a few tips to get good scores in math.", return_tensors="pt")
outputs = peft_model.generate(**inputs, max_length=128, do_sample=True)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
|
BigSalmon/BestMask2
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: david4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 121.70 +/- 5.80
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
|
BigSalmon/BlankSlots
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 4
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 1"
- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 2"
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 22.43
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 49.7
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 24.33
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 89.95
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 63.14
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-15000](https://huggingface.co/ckpts/mt5-small-trimmed-en-15000) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-15000](https://huggingface.co/ckpts/mt5-small-trimmed-en-15000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 89.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 54.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 38.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 28.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 22.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 24.33 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 63.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 49.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-15000
- max_length: 512
- max_length_output: 32
- epoch: 13
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-15000-squad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
BigSalmon/FormalBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: FineTuning01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93028
---
<!-- 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. -->
# FineTuning01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2349
- Accuracy: 0.9303
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2333 | 1.0 | 1563 | 0.1971 | 0.9241 |
| 0.1537 | 2.0 | 3126 | 0.2349 | 0.9303 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/FormalBerta3
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 4
| null |
---
license: cc-by-sa-4.0
datasets:
- mc4
language:
- ja
- en
tags:
- t5
- text2text-generation
- seq2seq
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a small T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese and English corpus.
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
* [Wikipedia](https://ja.wikipedia.org)の日本語ダンプデータ (2020年7月6日時点のもの)
* [OSCAR](https://oscar-corpus.com)の日本語コーパス
* [CC-100](http://data.statmt.org/cc-100/)の日本語コーパス
このモデルは事前学習のみを行なったものであり、特定のタスクに利用するにはファインチューニングする必要があります。
本モデルにも、大規模コーパスを用いた言語モデルにつきまとう、学習データの内容の偏りに由来する偏った（倫理的ではなかったり、有害だったり、バイアスがあったりする）出力結果になる問題が潜在的にあります。
この問題が発生しうることを想定した上で、被害が発生しない用途にのみ利用するよう気をつけてください。
SentencePieceトークナイザーの学習には上記Wikipediaの全データを用いました。
### 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
BigSalmon/FormalRobertaaa
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: h_size_2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 462.00 +/- 114.00
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
|
BigSalmon/GPT2HardArticleEasyArticle
|
[
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/Gerbil-D-6.7m | 6.7m | D-Class | 142 | 951M | 131k | 4.8186 |
|
BigSalmon/GPTNeo350MInformalToFormalLincoln
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
| 8
| null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- xiaoql/autotrain-data-dialogsum4
co2_eq_emissions:
emissions: 10.855487054586101
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 45048113162
- CO2 Emissions (in grams): 10.8555
## Validation Metrics
- Loss: 0.959
- Rouge1: 82.819
- Rouge2: 65.516
- RougeL: 82.525
- RougeLsum: 82.825
- Gen Len: 131.487
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/xiaoql/autotrain-dialogsum4-45048113162
```
|
BigSalmon/InformalToFormalLincoln17
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 12
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CIS6930_DAAGR_T5_NoEmo
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. -->
# CIS6930_DAAGR_T5_NoEmo
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3368
- Train Accuracy: 0.9629
- Validation Loss: 0.4438
- Validation Accuracy: 0.9496
- Epoch: 17
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5062 | 0.9405 | 0.4590 | 0.9454 | 0 |
| 0.4381 | 0.9479 | 0.4477 | 0.9472 | 1 |
| 0.4249 | 0.9499 | 0.4423 | 0.9481 | 2 |
| 0.4152 | 0.9513 | 0.4386 | 0.9486 | 3 |
| 0.4071 | 0.9525 | 0.4365 | 0.9490 | 4 |
| 0.4000 | 0.9535 | 0.4349 | 0.9493 | 5 |
| 0.3935 | 0.9545 | 0.4338 | 0.9496 | 6 |
| 0.3876 | 0.9553 | 0.4337 | 0.9498 | 7 |
| 0.3816 | 0.9562 | 0.4338 | 0.9498 | 8 |
| 0.3763 | 0.9571 | 0.4343 | 0.9499 | 9 |
| 0.3708 | 0.9578 | 0.4338 | 0.9500 | 10 |
| 0.3657 | 0.9586 | 0.4357 | 0.9498 | 11 |
| 0.3605 | 0.9593 | 0.4355 | 0.9500 | 12 |
| 0.3556 | 0.9601 | 0.4370 | 0.9499 | 13 |
| 0.3507 | 0.9608 | 0.4380 | 0.9499 | 14 |
| 0.3463 | 0.9615 | 0.4397 | 0.9498 | 15 |
| 0.3413 | 0.9622 | 0.4427 | 0.9496 | 16 |
| 0.3368 | 0.9629 | 0.4438 | 0.9496 | 17 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.11.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln25
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
Access to model satwikapaul/aicamp_nlpcrashcourse is restricted and you are not in the authorized list. Visit https://huggingface.co/satwikapaul/aicamp_nlpcrashcourse to ask for access.
|
BigSalmon/T52
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 8
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: gamma_0_05_fail
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 9.20 +/- 0.60
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
|
Blazeolmo/Scrabunzi
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
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},
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"num_beams": null,
"prefix": null
}
}
}
| 0
| 2023-03-30T03:31:11Z
|
---
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: 761.72 +/- 57.34
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
...
```
|
BritishLibraryLabs/bl-books-genre
|
[
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 76
| null |
---
license: wtfpl
language:
- en
pipeline_tag: text-generation
tags:
- llama
library_name: adapter-transformers
---
# Alpaca Native Enhanced 7B model download for Alpaca.cpp, Llama.cpp, and Dalai
Use this command to run with llama.cpp
```sh
main -m models/ANE-7B/ggml-model-q4_1.bin -n -1 --ctx_size 2048 --batch_size 16 --keep 512 --repeat_penalty 1.0 -t 16 --temp 0.4 --top_k 30 --top_p 0.18 --interactive-first -ins --color -i -r "User:" -f prompts/alpacanativeenhanced.txt
```
contents of `prompts/alpacanativeenhanced.txt` should be
```txt
You are an AI language model designed to assist the User by answering their questions, offering advice, and engaging in casual conversation in a friendly, helpful, and informative manner. You respond clearly, coherently, and you consider the conversation history.
User: Hey, how's it going?
Assistant: Hey there! I'm doing great, thank you. What can I help you with today? Let's have a fun chat!
```
Original model https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced
|
Broadus20/DialoGPT-small-joshua
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12
| 2023-03-30T04:10:01Z
|
---
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: 708.50 +/- 262.60
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Hourai -f logs/
python enjoy.py --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 Hourai -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Hourai
```
## 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)])
```
|
Bubb-les/DisloGPT-medium-HarryPotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8
| 2023-03-30T04:22:59Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="matank/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"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
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}
| 42
| 2023-03-30T04:51:43Z
|
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: uyenvuong/super-cool-model
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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},
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},
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}
}
| 18
| null |
title: Aitrial
emoji: 🚀
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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}
| 1,862
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/GerbilBlender-A-3.3m | 3.3m | A-Class | 20 | 60M | 65.5k | 6.7417 |
"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models include:
```
'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>'
# Example fill in the middle
'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>'
# Example causal language modelling
'<causal> this is an example text for causal language modelling <|endoftext|>'
# Example masked language modelling
'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>'
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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"BertForSequenceClassification"
],
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}
}
}
| 75
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jamesimmanuel/SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 71
| null |
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| GerbilLab/Gerbil-D-3.3m | 3.3m | D-Class | 142 | 426M | 65.5k | 5.3307 |
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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"BertForSequenceClassification"
],
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}
}
}
| 25
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_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_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1538
- Rouge1: 0.1789
- Rouge2: 0.1075
- Rougel: 0.1585
- Rougelsum: 0.1584
- Gen Len: 19.0
## 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: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2372 | 1.0 | 1635 | 0.1729 | 0.1764 | 0.1032 | 0.1554 | 0.1553 | 19.0 |
| 0.2077 | 2.0 | 3270 | 0.1602 | 0.1774 | 0.1054 | 0.1569 | 0.1567 | 19.0 |
| 0.197 | 3.0 | 4905 | 0.1550 | 0.1788 | 0.1073 | 0.1584 | 0.1583 | 19.0 |
| 0.1924 | 4.0 | 6540 | 0.1538 | 0.1789 | 0.1075 | 0.1585 | 0.1584 | 19.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
"model_type": "bert",
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}
}
}
| 21
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Prgrg/ja-en-dataset-v3.0-subset-v3.0
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. -->
# Prgrg/ja-en-dataset-v3.0-subset-v3.0
This model is a fine-tuned version of [Prgrg/ja-en-dataset-v3.0-subset-v2.0](https://huggingface.co/Prgrg/ja-en-dataset-v3.0-subset-v2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1261
- Validation Loss: 1.1683
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': 0.0, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'LinearWarmupCosineSchedule', 'config': {'initial_learning_rate': 0.0001, 'num_warmup_steps': 28181, 'num_training_steps': 281814}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.1261 | 1.1683 | 0 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 574
| 2023-03-30T05:47:39Z
|
---
model-index:
- name: twitter-roberta-base-hate-latest
results: []
pipeline_tag: text-classification
---
# cardiffnlp/twitter-roberta-base-hate-latest
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
## Following metrics are achieved
| **Dataset** | **Accuracy** | **Macro-F1** | **Weighted-F1** |
|------------------------------------------------------------------------------------------------------------------------------------------------------|:------------:|:------------:|:---------------:|
| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5848 | 0.5657 | 0.5514 |
| ucberkeley-dlab/measuring-hate-speech | 0.8706 | 0.8531 | 0.8701 |
| Detecting East Asian Prejudice on Social Media | 0.9276 | 0.8935 | 0.9273 |
| Call me sexist, but | 0.9033 | 0.6288 | 0.8852 |
| Predicting the Type and Target of Offensive Posts in Social Media | 0.9075 | 0.5984 | 0.8935 |
| HateXplain | 0.9594 | 0.8024 | 0.9600 |
| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.6817 | 0.5939 | 0.6233 |
| Twitter Sentiment Analysis | 0.9808 | 0.9258 | 0.9807 |
| Overview of the HASOC track at FIRE 2019:Hate Speech and Offensive Content Identification in Indo-European Languages | 0.8665 | 0.5562 | 0.8343 |
| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter | 0.9465 | 0.8557 | 0.9440 |
| Automated Hate Speech Detection and the Problem of Offensive Language | 0.9116 | 0.8797 | 0.9100 |
| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter | 0.8378 | 0.8338 | 0.8385 |
| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9655 | 0.4912 | 0.9824 |
| **Overall** | **0.8827** | **0.8383** | **0.8842** |
### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('I love everybody :)')
>> {'label': 'NOT-HATE'}
```
|
CAUKiel/JavaBERT
|
[
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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}
| 388
| 2023-03-30T05:54:34Z
|
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: omarcevi/ppo-Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CL/safe-math-bot
|
[] | null |
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}
| 0
| 2023-03-30T05:55:41Z
|
---
tags:
- Abe
- Shinzo
- AbeShinzo
- Former Japanese Prime Minister
language:
- ja
---
|
CLAck/en-km
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
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"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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},
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}
| 12
| 2023-03-30T05:55:45Z
|
---
model-index:
- name: twitter-roberta-base-hate-latest
results: []
pipeline_tag: text-classification
---
# cardiffnlp/twitter-roberta-base-hate-multiclass-latest
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for multiclass hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
## Classes available
```
{'racism': 0,
'sexism': 1,
'religion': 2,
'sexual_orientation': 3,
'disability': 4,
'origin': 5,
'not_hate': 6}
```
## Following metrics are achieved
* Accuracy: 0.9419
* Macro-F1: 0.5752
* Weighted-F1: 0.9390
### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('Women are trash 2.')
>> {'label': 'sexism'}
model.predict('@user dear mongoloid respect sentiments & belief refrain totalitarianism. @user')
>> {'label': 'disability'}
```
|
CLAck/indo-mixed
|
[
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] |
translation
|
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"MarianMTModel"
],
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},
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}
}
}
| 15
| 2023-03-30T05:58:26Z
|
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 1"
- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 2"
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 22.57
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 49.67
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 24.39
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 89.92
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 63.17
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-10000](https://huggingface.co/ckpts/mt5-small-trimmed-en-10000) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-10000](https://huggingface.co/ckpts/mt5-small-trimmed-en-10000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 89.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 54.56 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 38.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 29.05 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 22.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 24.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 63.17 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 49.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-10000
- max_length: 512
- max_length_output: 32
- epoch: 13
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-10000-squad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
CLAck/vi-en
|
[
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] |
translation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 6
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Please use prompt: comic style side face portrait of
davide-comic-book-characters Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
This AI is trained with @puppopages (instagram) charater illustration work
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
CLS/WubiBERT_models
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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}
}
}
| 0
| 2023-03-30T06:11:30Z
|
---
license: apache-2.0
---
| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss |
| --- | --- | --- | --- | --- | --- | --- |
| [GerbilLab/GerbilBlender-A-6.7m](https://hf.co/GerbilLab/GerbilBlender-A-6.7m) | 6.7m | A-Class | 20 | 134M | 131k | 6.0908 |
"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models include:
```
'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>'
# Example fill in the middle
'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>'
# Example causal language modelling
'<causal> this is an example text for causal language modelling <|endoftext|>'
# Example masked language modelling
'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>'
```
|
CLTL/MedRoBERTa.nl
|
[
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2,988
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 1"
- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 2"
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 22.84
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 50.34
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 24.75
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 90.01
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 63.43
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-5000](https://huggingface.co/ckpts/mt5-small-trimmed-en-5000) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-5000](https://huggingface.co/ckpts/mt5-small-trimmed-en-5000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 90.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 55.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 38.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 29.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 22.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 24.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 63.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 50.34 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-5000
- max_length: 512
- max_length_output: 32
- epoch: 15
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-5000-squad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
CLTL/icf-domains
|
[
"pytorch",
"roberta",
"nl",
"transformers",
"license:mit",
"text-classification"
] |
text-classification
|
{
"architectures": [
"RobertaForMultiLabelSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 35
| 2023-03-30T06:13:30Z
|
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- zhaozh/autotrain-data-further-train-chatdoctor
co2_eq_emissions:
emissions: 0.6371864100333517
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 45099113239
- CO2 Emissions (in grams): 0.6372
## Validation Metrics
- Loss: 1.468
- SacreBLEU: 27.918
- Gen len: 74.588
|
CLTL/icf-levels-adm
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 33
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep7_lr5_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep7_lr5_v1
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2884
- Precision: 0.6752
- Recall: 0.6436
- F1: 0.6590
- Accuracy: 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: 5e-09
- 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.2972 | 0.6754 | 0.6363 | 0.6553 | 0.9413 |
| 0.2981 | 2.0 | 934 | 0.2939 | 0.6742 | 0.6387 | 0.6560 | 0.9413 |
| 0.2882 | 3.0 | 1401 | 0.2915 | 0.6747 | 0.6409 | 0.6574 | 0.9414 |
| 0.2913 | 4.0 | 1868 | 0.2898 | 0.6744 | 0.6422 | 0.6579 | 0.9414 |
| 0.2852 | 5.0 | 2335 | 0.2889 | 0.6741 | 0.6425 | 0.6579 | 0.9415 |
| 0.2883 | 6.0 | 2802 | 0.2885 | 0.6752 | 0.6436 | 0.6590 | 0.9415 |
| 0.2819 | 7.0 | 3269 | 0.2884 | 0.6752 | 0.6436 | 0.6590 | 0.9415 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
CLTL/icf-levels-ins
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
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}
| 32
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sudeepsharma/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CLTL/icf-levels-stm
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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| 32
| null |
---
language:
- am
tags:
- speech
- audio
- audio-classification
- hubert
pipeline_tag: audio-classification
---
model_name_or_path = "quaja/hubert-base-amharic-speech-emotion-recognition"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = HubertForSpeechClassification.from_pretrained(model_name_or_path)
|
CM-CA/Cartman
|
[] | null |
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}
| 0
| 2023-03-30T06:27:00Z
|
---
tags:
- generated_from_trainer
datasets:
- afrispeech-200
model-index:
- name: whisper-small-hi-2400_500_120
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-hi-2400_500_120
This model is a fine-tuned version of [saif-daoud/whisper-small-hi-2400_500_103](https://huggingface.co/saif-daoud/whisper-small-hi-2400_500_103) on the afrispeech-200 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 300
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
CSZay/bart
|
[] | null |
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}
}
| 0
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 1"
- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
example_title: "Question Generation Example 2"
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 21.36
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 48.53
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 23.58
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 89.82
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 62.55
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-120000](https://huggingface.co/ckpts/mt5-small-trimmed-en-120000) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-120000](https://huggingface.co/ckpts/mt5-small-trimmed-en-120000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 89.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 53.3 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 36.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 27.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 21.36 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 23.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 62.55 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 48.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-120000
- max_length: 512
- max_length_output: 32
- epoch: 5
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-120000-squad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Calamarii/calamari
|
[] | null |
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}
}
}
| 0
| null |
---
datasets:
- deepset/germanquad
- mlqa
- xquad
language:
- de
pipeline_tag: question-answering
---
|
Cameron/BERT-SBIC-targetcategory
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 30
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: Galeros/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Cameron/BERT-jigsaw-severetoxic
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 30
| null |
Access to model basimbashir/gpt-j-6B-8bit is restricted and you are not in the authorized list. Visit https://huggingface.co/basimbashir/gpt-j-6B-8bit to ask for access.
|
Camzure/MaamiBot-test
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep6_lr1
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_ep6_lr1
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- Precision: 0.8654
- Recall: 0.8689
- F1: 0.8671
- Accuracy: 0.9759
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.1009 | 0.7821 | 0.8764 | 0.8266 | 0.9665 |
| 0.1009 | 2.0 | 934 | 0.0870 | 0.8476 | 0.8643 | 0.8559 | 0.9740 |
| 0.0521 | 3.0 | 1401 | 0.1020 | 0.8789 | 0.8497 | 0.8641 | 0.9746 |
| 0.0327 | 4.0 | 1868 | 0.1236 | 0.8707 | 0.8649 | 0.8678 | 0.9752 |
| 0.0175 | 5.0 | 2335 | 0.1290 | 0.8669 | 0.8700 | 0.8685 | 0.9759 |
| 0.0109 | 6.0 | 2802 | 0.1352 | 0.8654 | 0.8689 | 0.8671 | 0.9759 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Camzure/MaamiBot
|
[] | null |
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}
| 0
| 2023-03-30T06:56:21Z
|
---
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="halil93ibrahim/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"])
```
|
CasualHomie/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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}
}
| 11
| 2023-03-30T07:23:46Z
|
---
license: gpl-3.0
tags:
- DocVQA
- Document Question Answering
- Document Visual Question Answering
datasets:
- rubentito/mp-docvqa
language:
- en
---
# LongT5 base with transient-global attention fine-tuned on MP-DocVQA
This is LongT5 trained on SQuADv2, CoQA and TryoCoQA datasets from [Tryolabs hub](https://huggingface.co/tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx), and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
## How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
import torch
from transformers import AutoTokenizer, LongT5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("rubentito/longt5-tglobal-base-mpdocvqa")
model = LongT5ForConditionalGeneration.from_pretrained("rubentito/longt5-tglobal-base-mpdocvqa")
context = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done?"
input_text = "question: {:s} context: {:s}".format(question, context)
encoding = tokenizer(input_text, return_tensors="pt")
output = self.model.generate(**encoding)
answer = tokenizer.decode(output['sequences'], skip_special_tokens=True)
```
## Metrics
**Average Normalized Levenshtein Similarity (ANLS)**
The standard metric for text-based VQA tasks (ST-VQA and DocVQA). It evaluates the method's reasoning capabilities while smoothly penalizes OCR recognition errors.
Check [Scene Text Visual Question Answering](https://arxiv.org/abs/1905.13648) for detailed information.
**Answer Page Prediction Accuracy (APPA)**
In the MP-DocVQA task, the models can provide the index of the page where the information required to answer the question is located. For this subtask accuracy is used to evaluate the predictions: i.e. if the predicted page is correct or not.
Check [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/abs/2212.05935) for detailed information.
## Model results
Extended experimentation can be found in Table 2 of [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/pdf/2212.05935.pdf).
You can also check the live leaderboard at the [RRC Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4).
| Model | HF name | Parameters | ANLS | APPA |
|-----------------------------------------------------------------------------------|:--------------------------------------|:-------------:|:-------------:|:---------:|
| [Bert large](https://huggingface.co/rubentito/bert-large-mpdocvqa) | rubentito/bert-large-mpdocvqa | 334M | 0.4183 | 51.6177 |
| [Longformer base](https://huggingface.co/rubentito/longformer-base-mpdocvqa) | rubentito/longformer-base-mpdocvqa | 148M | 0.5287 | 71.1696 |
| [BigBird ITC base](https://huggingface.co/rubentito/bigbird-base-itc-mpdocvqa) | rubentito/bigbird-base-itc-mpdocvqa | 131M | 0.4929 | 67.5433 |
| [LayoutLMv3 base](https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa) | rubentito/layoutlmv3-base-mpdocvqa | 125M | 0.4538 | 51.9426 |
| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa) | rubentito/t5-base-mpdocvqa | 223M | 0.5050 | 0.0000 |
| [Hi-VT5](https://huggingface.co/rubentito/hivt5-base-mpdocvqa) | rubentito/hivt5-base-mpdocvqa | 316M | 0.6201 | 79.23 |
## Citation Information
```tex
@article{tito2022hierarchical,
title={Hierarchical multimodal transformers for Multi-Page DocVQA},
author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest},
journal={arXiv preprint arXiv:2212.05935},
year={2022}
}
```
|
Cat/Kitty
|
[] | null |
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}
| 0
| 2023-03-30T07:26:31Z
|
---
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: 234.54 +/- 68.22
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
...
```
|
Cathy/reranking_model
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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}
}
}
| 27
| 2023-03-30T07:26:33Z
|
---
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: 121.60 +/- 29.17
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.0006
'num_envs': 32
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.98
'num_minibatches': 32
'update_epochs': 32
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'yumingyi/lunarlander-v2-unit8'
'batch_size': 32768
'minibatch_size': 1024}
```
|
Cedille/fr-boris
|
[
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"arxiv:2202.03371",
"transformers",
"causal-lm",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPTJForCausalLM"
],
"model_type": "gptj",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
"translation_en_to_fr": {
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}
}
}
| 401
| 2023-03-30T07:27:55Z
|
---
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: 267.61 +/- 23.09
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
...
```
|
dccuchile/albert-base-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
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},
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}
}
}
| 34
| null |
valDataset consists of GBH / NPR / DemocracyNow!
[[ 1284 692]
[ 7539 28555]]
0.6497975708502024
0.791128719454757
|
dccuchile/albert-base-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 14
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2
results: []
datasets:
- Jsevisal/balanced_augmented_dataset_2
---
<!-- 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. -->
# balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4965
- Precision: 0.9214
- Recall: 0.9180
- F1: 0.9135
- Accuracy: 0.9012
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 3.0873 | 1.0 | 52 | 2.5606 | 0.1508 | 0.1205 | 0.1095 | 0.3096 |
| 2.2599 | 2.0 | 104 | 1.8545 | 0.3409 | 0.3827 | 0.3343 | 0.5265 |
| 1.7149 | 3.0 | 156 | 1.4711 | 0.5470 | 0.5222 | 0.4715 | 0.6087 |
| 1.3056 | 4.0 | 208 | 1.0879 | 0.6500 | 0.6103 | 0.5886 | 0.6919 |
| 0.9978 | 5.0 | 260 | 1.0036 | 0.7039 | 0.6766 | 0.6497 | 0.7221 |
| 0.7532 | 6.0 | 312 | 0.7722 | 0.7356 | 0.7552 | 0.7286 | 0.7842 |
| 0.5945 | 7.0 | 364 | 0.6766 | 0.8316 | 0.7902 | 0.7790 | 0.8053 |
| 0.473 | 8.0 | 416 | 0.5994 | 0.8602 | 0.8248 | 0.8224 | 0.8406 |
| 0.3762 | 9.0 | 468 | 0.5572 | 0.8725 | 0.8743 | 0.8600 | 0.8593 |
| 0.2943 | 10.0 | 520 | 0.5767 | 0.8893 | 0.8714 | 0.8659 | 0.8593 |
| 0.251 | 11.0 | 572 | 0.5480 | 0.8892 | 0.8765 | 0.8667 | 0.8633 |
| 0.2074 | 12.0 | 624 | 0.5652 | 0.8960 | 0.8866 | 0.8757 | 0.8714 |
| 0.1714 | 13.0 | 676 | 0.5254 | 0.9172 | 0.9087 | 0.9019 | 0.8875 |
| 0.1523 | 14.0 | 728 | 0.5788 | 0.9217 | 0.8900 | 0.8918 | 0.8790 |
| 0.1309 | 15.0 | 780 | 0.5209 | 0.9205 | 0.9141 | 0.9080 | 0.8961 |
| 0.1187 | 16.0 | 832 | 0.5030 | 0.9163 | 0.9138 | 0.9073 | 0.8961 |
| 0.1065 | 17.0 | 884 | 0.5449 | 0.9278 | 0.9212 | 0.9153 | 0.8986 |
| 0.0923 | 18.0 | 936 | 0.4965 | 0.9214 | 0.9180 | 0.9135 | 0.9012 |
| 0.0894 | 19.0 | 988 | 0.5171 | 0.9236 | 0.9189 | 0.9148 | 0.9007 |
| 0.0869 | 20.0 | 1040 | 0.5211 | 0.9245 | 0.9214 | 0.9159 | 0.9027 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
dccuchile/albert-base-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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}
}
}
| 25
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.43 +/- 20.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-xxlarge-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 68
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8322368421052632
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2369
- F1: 0.8322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8113 | 1.0 | 70 | 0.3088 | 0.7546 |
| 0.259 | 2.0 | 140 | 0.2541 | 0.8155 |
| 0.1791 | 3.0 | 210 | 0.2369 | 0.8322 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
dccuchile/albert-base-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
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},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
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}
}
| 586
| 2023-03-30T08:05:10Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-Bangla-Common_Voice
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: bn
split: train+validation
args: bn
metrics:
- name: Wer
type: wer
value: 0.6576650727705051
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-Bangla-Common_Voice
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Wer: 0.6577
## 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: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.922 | 4.57 | 800 | 0.7379 | 0.8157 |
| 0.5136 | 9.14 | 1600 | 0.6155 | 0.7056 |
| 0.2759 | 13.71 | 2400 | 0.6172 | 0.6577 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
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| 75
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6991051454138703
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3926
- F1: 0.6991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1415 | 1.0 | 50 | 0.5404 | 0.5163 |
| 0.5045 | 2.0 | 100 | 0.4347 | 0.6498 |
| 0.371 | 3.0 | 150 | 0.3926 | 0.6991 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 25
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 38.40 +/- 30.62
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
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
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| 1
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
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
|
dccuchile/distilbert-base-spanish-uncased
|
[
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
] |
fill-mask
|
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"DistilBertForMaskedLM"
],
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}
| 670
| null |
# Model card for CoNN Add Carry
### Introduction
In paper Neural Comprehension: Language Models with Compiled Neural Networks , we introduced the integration of Compiled Neural Networks (CoNN) into the framework of language models, enabling existing language models to perform symbolic operations with perfect accuracy without the need for external tools.
In this model card, we introduce the Add Carry model, which is similar to the Transformer model and can perform carry operations on a sequence of numbers added in parallel.
### Install
```
git clone https://github.com/WENGSYX/Neural-Comprehension
cd Neural-Comprehension
pip install .
```
To run neural comprehension, you need to install `PyTorch`, `Transformers`, `jax`, and `tracr`.
### How to Use?
```
from NeuralCom.CoNN.modeling_conn import CoNNModel
from NeuralCom.CoNN import Tokenizer
model = CoNNModel.from_pretrained('WENGSYX/CoNN_Add_Carry')
tokenizer = Tokenizer(model.config.input_encoding_map, model.config.output_encoding_map,model.config.max_position_embeddings)
output = model(tokenizer('2 15 3 8 10').unsqueeze(0))
print(tokenizer.decode(output.argmax(2)))
>>> [['bos', '3', '5', '3', '9', '0']]
```
### 🙏Cite🙏
###### If you are interested in our paper, please feel free to cite it.
```
@misc{weng2023neural,
title={Neural Comprehension: Language Models with Compiled Neural Networks},
author={Yixuan Weng and Minjun Zhu and Fei Xia and Bin Li and Shizhu He and Kang Liu and Jun Zhao},
year={2023},
eprint={2304.01665},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Chaewon/mmnt_decoder_en
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 12
| null |
# Vocabulary Trimmed [lmqg/mt5-small-squad-qg](https://huggingface.co/lmqg/mt5-small-squad-qg): `vocabtrimmer/mt5-small-squad-qg-trimmed-en-5000`
This model is a trimmed version of [lmqg/mt5-small-squad-qg](https://huggingface.co/lmqg/mt5-small-squad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-squad-qg | vocabtrimmer/mt5-small-squad-qg-trimmed-en-5000 |
|:---------------------------|:--------------------------|:--------------------------------------------------|
| parameter_size_full | 300,165,504 | 49,184,128 |
| parameter_size_embedding | 256,103,424 | 5,122,048 |
| vocab_size | 250,101 | 5,002 |
| compression_rate_full | 100.0 | 16.39 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 5000 | 2 |
|
Chaewon/mnmt_decoder_en_gpt2
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch-2
results: []
datasets:
- Jsevisal/balanced_augmented_dataset_2
---
<!-- 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. -->
# balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3718
- Precision: 0.9171
- Recall: 0.9183
- F1: 0.9144
- Accuracy: 0.9173
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.8924 | 1.0 | 52 | 2.1510 | 0.3032 | 0.2572 | 0.2464 | 0.4120 |
| 1.8492 | 2.0 | 104 | 1.3548 | 0.5880 | 0.5350 | 0.5046 | 0.6440 |
| 1.1692 | 3.0 | 156 | 0.8621 | 0.7443 | 0.6770 | 0.6775 | 0.7468 |
| 0.7452 | 4.0 | 208 | 0.6391 | 0.7779 | 0.7919 | 0.7691 | 0.8159 |
| 0.5185 | 5.0 | 260 | 0.6401 | 0.8197 | 0.8000 | 0.7868 | 0.8109 |
| 0.3536 | 6.0 | 312 | 0.4251 | 0.8808 | 0.8684 | 0.8675 | 0.8820 |
| 0.2411 | 7.0 | 364 | 0.4748 | 0.8709 | 0.8659 | 0.8613 | 0.8800 |
| 0.1762 | 8.0 | 416 | 0.3809 | 0.8991 | 0.8721 | 0.8812 | 0.8971 |
| 0.1377 | 9.0 | 468 | 0.3977 | 0.9062 | 0.8950 | 0.8953 | 0.9022 |
| 0.1026 | 10.0 | 520 | 0.4637 | 0.9068 | 0.8887 | 0.8897 | 0.8951 |
| 0.0763 | 11.0 | 572 | 0.4210 | 0.9079 | 0.9066 | 0.9020 | 0.9012 |
| 0.0554 | 12.0 | 624 | 0.4950 | 0.8962 | 0.8837 | 0.8809 | 0.8850 |
| 0.0419 | 13.0 | 676 | 0.4643 | 0.9043 | 0.9007 | 0.8969 | 0.8961 |
| 0.0358 | 14.0 | 728 | 0.3718 | 0.9171 | 0.9183 | 0.9144 | 0.9173 |
| 0.0264 | 15.0 | 780 | 0.4456 | 0.9349 | 0.9042 | 0.9132 | 0.9123 |
| 0.0178 | 16.0 | 832 | 0.4296 | 0.9362 | 0.9169 | 0.9227 | 0.9193 |
| 0.015 | 17.0 | 884 | 0.3900 | 0.9302 | 0.9235 | 0.9240 | 0.9254 |
| 0.0105 | 18.0 | 936 | 0.4335 | 0.9284 | 0.9161 | 0.9181 | 0.9168 |
| 0.0116 | 19.0 | 988 | 0.4426 | 0.9285 | 0.9138 | 0.9166 | 0.9138 |
| 0.0104 | 20.0 | 1040 | 0.4410 | 0.9276 | 0.9141 | 0.9163 | 0.9138 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
ChaitanyaU/FineTuneLM
|
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| 0
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: Shivraj8615/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Champion/test_upload_vox2_wavlm_epoch8
|
[
"sidekit",
"audio"
] | null |
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| 0
| null |
---
license: apache-2.0
---
https://wandb.ai/open-assistant/supervised-finetuning/runs/7pz5n33h
exported checkpoint: 3000 steps
datasets:
```
oasst_export_eu:
datasets:
- oasst_export:
lang: "en,es,de,fr"
input_file_path: 2023-03-27_oasst_research_ready_synth.jsonl.gz
- alpaca
- oig_file:
source_url: https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl
max_count: 15000
min_length: 500
val_split: 0.2
- oig_file:
source_url: https://huggingface.co/datasets/laion/OIG/raw/main/unified_grade_school_math_instructions.jsonl
val_split: 0.1
min_length: 1000
sort_by_length: false
use_custom_sampler: false
```
pythia:
```
pythia-12b:
fp16: true
log_dir: "pythia_log_12b"
learning_rate: 6e-6
model_name: EleutherAI/pythia-12b-deduped
output_dir: pythia_model_12b
weight_decay: 0.0
residual_dropout: 0.2
max_length: 2048
use_flash_attention: true
warmup_steps: 100
gradient_checkpointing: false
gradient_accumulation_steps: 4
per_device_train_batch_size: 2
per_device_eval_batch_size: 5
eval_steps: 200
save_steps: 500
num_train_epochs: 16
save_total_limit: 4
```
|
Chan/distilgpt2-finetuned-wikitext2
|
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}
| 0
| null |
---
license: unknown
datasets:
- irds/codec_history
- nbalepur/cs_history_wiki_web_noise
language:
- de
- en
- fr
- es
- it
- ru
- la
- nb
metrics:
- accuracy
library_name: open_clip
pipeline_tag: reinforcement-learning
tags:
- art
---
|
Chandanbhat/distilbert-base-uncased-finetuned-cola
|
[] | null |
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| 0
| null |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Hinataaa/autotrain-data-text_summary_arp
co2_eq_emissions:
emissions: 4.2992847624934365
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 45146113307
- CO2 Emissions (in grams): 4.2993
## Validation Metrics
- Loss: 1.285
- Rouge1: 49.529
- Rouge2: 25.404
- RougeL: 46.465
- RougeLsum: 46.645
- Gen Len: 18.803
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Hinataaa/autotrain-text_summary_arp-45146113307
```
|
Chertilasus/main
|
[] | null |
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| 0
| null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
- not-for-all-eyes
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Custom resolution versions are tagged accordingly.
- The `vae-ft-mse-840000-ema-pruned.ckpt` VAE is embedded into the model.
- This model was converted with a `vae-encoder` for use with `image2image`.
- This model is `fp16`.
- Descriptions are posted as-is from original model source.
- Not all features and/or results may be available in `CoreML` format.
- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
- This model does not include a `safety checker` (for NSFW content).
# realisticVision-v20:
Source(s): [Hugging Face](https://huggingface.co/SG161222/Realistic_Vision_V2.0) - [CivitAI](https://civitai.com/models/4201/realistic-vision-v20)
**Please read this!**
My model has always been free and always will be free. There are no restrictions on the use of the model. The rights to this model still belong to me.
This model is available on Mage.Space, Sinkin.ai, GetImg.ai and RandomSeed.co (NSFW content)
You can find out news about this model and future models, as well as support me on Boosty.
Recommended for use with [VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) which has already been baked into the converted `CoreML` model version here.
I use this template to get good generation results:
**Prompt**: RAW photo, subject, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
**Example**: RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
**Negative Prompt**: (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck
OR
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
`Euler A` or `DPM++ 2M Karras` with 25 steps
`CFG Scale` 7
`Hires Fix` with `Latent` upscaler
0 `Hires Steps` and `Denoising Strength` 0.25 - 0.45
`Upscaling` by 1.1 - 2.0 <br><br>
|
Chuah/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
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},
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},
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}
| 9
| null |
---
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: 0.56
- name: Recall
type: recall
value: 0.28544949026876737
- name: F1
type: f1
value: 0.378146101903008
- name: Accuracy
type: accuracy
value: 0.9407464409388226
---
<!-- 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: 0.2754
- Precision: 0.56
- Recall: 0.2854
- F1: 0.3781
- Accuracy: 0.9407
## 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 | 0.2826 | 0.5246 | 0.2475 | 0.3363 | 0.9384 |
| No log | 2.0 | 426 | 0.2754 | 0.56 | 0.2854 | 0.3781 | 0.9407 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
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