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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| downloads
int64 0
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| likes
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| library_name
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DrishtiSharma/mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.1 | DrishtiSharma | 2023-09-02T17:32:29Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-09-02T15:17:32Z | ---
license: mit
base_model: facebook/mbart-large-50
tags:
- translation
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.1
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9532
- Bleu: 45.1551
- Rouge: {'rouge1': 0.707093830119779, 'rouge2': 0.5240989044660875, 'rougeL': 0.6865395711179825, 'rougeLsum': 0.6867643949864491}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|
| 1.4485 | 1.0 | 4500 | 1.0236 | 42.1586 | {'rouge1': 0.6728104679322686, 'rouge2': 0.4866267759088613, 'rougeL': 0.6507619922873461, 'rougeLsum': 0.6508024989844624} |
| 0.8867 | 2.0 | 9000 | 0.9542 | 44.1945 | {'rouge1': 0.6933374960151913, 'rouge2': 0.5090654274262618, 'rougeL': 0.6722360570050694, 'rougeLsum': 0.6723972406375381} |
| 0.7112 | 3.0 | 13500 | 0.9408 | 44.9173 | {'rouge1': 0.7047659807760827, 'rouge2': 0.5200169348076622, 'rougeL': 0.6839031690668775, 'rougeLsum': 0.6842067045539153} |
| 0.6075 | 4.0 | 18000 | 0.9532 | 45.2020 | {'rouge1': 0.7070170730434684, 'rouge2': 0.5239391023023636, 'rougeL': 0.6863309446860562, 'rougeLsum': 0.6866635686411662} |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
bigmorning/whisper_syl_noforce__0030 | bigmorning | 2023-09-02T17:19:47Z | 59 | 0 | transformers | [
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-02T17:19:39Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_syl_noforce__0030
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. -->
# whisper_syl_noforce__0030
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7408
- Train Accuracy: 0.0305
- Train Wermet: 0.2408
- Validation Loss: 0.9883
- Validation Accuracy: 0.0216
- Validation Wermet: 0.3596
- Epoch: 29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.2961 | 0.0113 | 1.9043 | 3.9402 | 0.0116 | 0.9526 | 0 |
| 4.6207 | 0.0121 | 0.8740 | 3.7957 | 0.0120 | 0.9397 | 1 |
| 4.4142 | 0.0128 | 0.8473 | 3.6045 | 0.0124 | 0.8988 | 2 |
| 4.1915 | 0.0135 | 0.8361 | 3.4445 | 0.0128 | 0.9019 | 3 |
| 4.0072 | 0.0140 | 0.8260 | 3.3268 | 0.0131 | 0.8816 | 4 |
| 3.8559 | 0.0145 | 0.8084 | 3.2440 | 0.0133 | 0.8592 | 5 |
| 3.7359 | 0.0149 | 0.7986 | 3.1751 | 0.0135 | 0.8598 | 6 |
| 3.6368 | 0.0152 | 0.7891 | 3.1298 | 0.0136 | 0.8398 | 7 |
| 3.5465 | 0.0154 | 0.7775 | 3.0736 | 0.0138 | 0.8606 | 8 |
| 3.4710 | 0.0157 | 0.7681 | 3.0318 | 0.0138 | 0.8455 | 9 |
| 3.3988 | 0.0159 | 0.7603 | 3.0159 | 0.0139 | 0.8770 | 10 |
| 3.3279 | 0.0162 | 0.7504 | 2.9672 | 0.0141 | 0.8241 | 11 |
| 3.2611 | 0.0164 | 0.7397 | 2.9541 | 0.0141 | 0.8676 | 12 |
| 3.1996 | 0.0167 | 0.7284 | 2.8913 | 0.0144 | 0.7990 | 13 |
| 3.1311 | 0.0169 | 0.7162 | 2.8671 | 0.0145 | 0.7934 | 14 |
| 3.0590 | 0.0172 | 0.7044 | 2.8241 | 0.0146 | 0.7907 | 15 |
| 2.9692 | 0.0177 | 0.6843 | 2.7517 | 0.0149 | 0.7645 | 16 |
| 2.8783 | 0.0181 | 0.6630 | 2.6682 | 0.0152 | 0.7263 | 17 |
| 2.7622 | 0.0187 | 0.6417 | 2.5586 | 0.0156 | 0.7220 | 18 |
| 2.6164 | 0.0194 | 0.6138 | 2.4121 | 0.0161 | 0.6909 | 19 |
| 2.4405 | 0.0203 | 0.5838 | 2.2417 | 0.0167 | 0.6527 | 20 |
| 2.2404 | 0.0213 | 0.5486 | 2.1401 | 0.0170 | 0.6662 | 21 |
| 2.0196 | 0.0225 | 0.5086 | 1.8907 | 0.0180 | 0.5774 | 22 |
| 1.7917 | 0.0237 | 0.4665 | 1.7073 | 0.0186 | 0.5446 | 23 |
| 1.5286 | 0.0253 | 0.4182 | 1.5139 | 0.0194 | 0.4919 | 24 |
| 1.2991 | 0.0267 | 0.3736 | 1.3605 | 0.0200 | 0.4570 | 25 |
| 1.1117 | 0.0279 | 0.3336 | 1.2304 | 0.0205 | 0.4262 | 26 |
| 0.9643 | 0.0289 | 0.2986 | 1.1387 | 0.0209 | 0.4040 | 27 |
| 0.8404 | 0.0298 | 0.2663 | 1.0514 | 0.0213 | 0.3776 | 28 |
| 0.7408 | 0.0305 | 0.2408 | 0.9883 | 0.0216 | 0.3596 | 29 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
CzarnyRycerz/taxi-v3-q-table | CzarnyRycerz | 2023-09-02T17:17:01Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T16:40:46Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-q-table
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="CzarnyRycerz/taxi-v3-q-table", 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"])
```
|
The-matt/autumn-shadow-48_220 | The-matt | 2023-09-02T17:16:20Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T17:16:14Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
PraveenJesu/whisper-medium-96-random-peft-V1-drug_list | PraveenJesu | 2023-09-02T17:16:03Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T17:16:02Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_p_tuning_500_10_50000_8_e5_s6789_v4_l4_v100 | KingKazma | 2023-09-02T17:15:46Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T17:15:42Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
bigmorning/whisper_syl_noforce__0025 | bigmorning | 2023-09-02T17:06:34Z | 59 | 0 | transformers | [
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-02T17:06:27Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_syl_noforce__0025
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. -->
# whisper_syl_noforce__0025
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5286
- Train Accuracy: 0.0253
- Train Wermet: 0.4182
- Validation Loss: 1.5139
- Validation Accuracy: 0.0194
- Validation Wermet: 0.4919
- Epoch: 24
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.2961 | 0.0113 | 1.9043 | 3.9402 | 0.0116 | 0.9526 | 0 |
| 4.6207 | 0.0121 | 0.8740 | 3.7957 | 0.0120 | 0.9397 | 1 |
| 4.4142 | 0.0128 | 0.8473 | 3.6045 | 0.0124 | 0.8988 | 2 |
| 4.1915 | 0.0135 | 0.8361 | 3.4445 | 0.0128 | 0.9019 | 3 |
| 4.0072 | 0.0140 | 0.8260 | 3.3268 | 0.0131 | 0.8816 | 4 |
| 3.8559 | 0.0145 | 0.8084 | 3.2440 | 0.0133 | 0.8592 | 5 |
| 3.7359 | 0.0149 | 0.7986 | 3.1751 | 0.0135 | 0.8598 | 6 |
| 3.6368 | 0.0152 | 0.7891 | 3.1298 | 0.0136 | 0.8398 | 7 |
| 3.5465 | 0.0154 | 0.7775 | 3.0736 | 0.0138 | 0.8606 | 8 |
| 3.4710 | 0.0157 | 0.7681 | 3.0318 | 0.0138 | 0.8455 | 9 |
| 3.3988 | 0.0159 | 0.7603 | 3.0159 | 0.0139 | 0.8770 | 10 |
| 3.3279 | 0.0162 | 0.7504 | 2.9672 | 0.0141 | 0.8241 | 11 |
| 3.2611 | 0.0164 | 0.7397 | 2.9541 | 0.0141 | 0.8676 | 12 |
| 3.1996 | 0.0167 | 0.7284 | 2.8913 | 0.0144 | 0.7990 | 13 |
| 3.1311 | 0.0169 | 0.7162 | 2.8671 | 0.0145 | 0.7934 | 14 |
| 3.0590 | 0.0172 | 0.7044 | 2.8241 | 0.0146 | 0.7907 | 15 |
| 2.9692 | 0.0177 | 0.6843 | 2.7517 | 0.0149 | 0.7645 | 16 |
| 2.8783 | 0.0181 | 0.6630 | 2.6682 | 0.0152 | 0.7263 | 17 |
| 2.7622 | 0.0187 | 0.6417 | 2.5586 | 0.0156 | 0.7220 | 18 |
| 2.6164 | 0.0194 | 0.6138 | 2.4121 | 0.0161 | 0.6909 | 19 |
| 2.4405 | 0.0203 | 0.5838 | 2.2417 | 0.0167 | 0.6527 | 20 |
| 2.2404 | 0.0213 | 0.5486 | 2.1401 | 0.0170 | 0.6662 | 21 |
| 2.0196 | 0.0225 | 0.5086 | 1.8907 | 0.0180 | 0.5774 | 22 |
| 1.7917 | 0.0237 | 0.4665 | 1.7073 | 0.0186 | 0.5446 | 23 |
| 1.5286 | 0.0253 | 0.4182 | 1.5139 | 0.0194 | 0.4919 | 24 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_210 | The-matt | 2023-09-02T17:06:12Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T17:06:08Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
leofn3/modelo_racismo | leofn3 | 2023-09-02T17:01:56Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:PORTULAN/albertina-900m-portuguese-ptbr-encoder-brwac",
"base_model:finetune:PORTULAN/albertina-900m-portuguese-ptbr-encoder-brwac",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-18T14:11:56Z | ---
license: other
base_model: PORTULAN/albertina-ptbr
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modelo_racismo
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. -->
# modelo_racismo
This model is a fine-tuned version of [PORTULAN/albertina-ptbr](https://huggingface.co/PORTULAN/albertina-ptbr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 0.9989
## 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
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 468 | 0.2304 | 0.9583 |
| 0.7037 | 2.0 | 936 | 0.0847 | 0.9840 |
| 0.256 | 3.0 | 1404 | 0.0075 | 0.9979 |
| 0.0759 | 4.0 | 1872 | 0.0036 | 0.9989 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ukeme/ukay-base-sentence-transformer | ukeme | 2023-09-02T17:00:03Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:embedding-data/sentence-compression",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-09-02T16:41:46Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- embedding-data/sentence-compression
---
# ukeme/ukay-base-sentence-transformer
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ukeme/ukay-base-sentence-transformer')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ukeme/ukay-base-sentence-transformer')
model = AutoModel.from_pretrained('ukeme/ukay-base-sentence-transformer')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ukeme/ukay-base-sentence-transformer)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
KingKazma/xsum_t5-small_lora_500_10_50000_8_e5_s6789_v4_l4_r4 | KingKazma | 2023-09-02T16:59:22Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T16:59:21Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_p_tuning_500_10_50000_8_e4_s6789_v4_l4_v100 | KingKazma | 2023-09-02T16:45:49Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T16:45:45Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
The-matt/autumn-shadow-48_190 | The-matt | 2023-09-02T16:43:05Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T16:43:01Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Kamer/NoFrequentWords | Kamer | 2023-09-02T16:38:13Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-02T14:34:27Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: NoFrequentWords
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. -->
# NoFrequentWords
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5140
- eval_Accuracy: 0.4027
- eval_F1_macro: 0.1427
- eval_F1_class_0: 0.9205
- eval_F1_class_1: 0.6667
- eval_F1_class_2: 0.1782
- eval_F1_class_3: 0.0
- eval_F1_class_4: 0.0
- eval_F1_class_5: 0.0
- eval_F1_class_6: 0.0204
- eval_F1_class_7: 0.0
- eval_F1_class_8: 0.0
- eval_F1_class_9: 0.9070
- eval_F1_class_10: 0.0253
- eval_F1_class_11: 0.0
- eval_F1_class_12: 0.1140
- eval_F1_class_13: 0.0
- eval_F1_class_14: 0.0220
- eval_F1_class_15: 0.0
- eval_F1_class_16: 0.0
- eval_F1_class_17: 0.0
- eval_F1_class_18: 0.0
- eval_F1_class_19: 0.0
- eval_runtime: 17.6645
- eval_samples_per_second: 63.97
- eval_steps_per_second: 4.019
- epoch: 2.92
- step: 9500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Naska223/AWPortrait | Naska223 | 2023-09-02T16:34:20Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-02T15:53:39Z | ---
license: creativeml-openrail-m
---
|
CzarnyRycerz/q-FrozenLake-v1-4x4-noSlippery | CzarnyRycerz | 2023-09-02T16:34:07Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T16:34:03Z | ---
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="CzarnyRycerz/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"])
```
|
HorcruxNo13/beit-base-patch16-224-pt22k-ft22k | HorcruxNo13 | 2023-09-02T16:27:17Z | 192 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224-pt22k-ft22k",
"base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-02T13:12:22Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6866666666666666
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6312
- Accuracy: 0.6867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 3.4268 | 0.275 |
| 6.7921 | 2.0 | 16 | 0.6216 | 0.7083 |
| 0.7831 | 3.0 | 24 | 0.5972 | 0.7417 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
thegrigorian/ppo-LunarLander-v2 | thegrigorian | 2023-09-02T16:26:52Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T16:26:33Z | ---
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: 258.11 +/- 19.78
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
...
```
|
raymondowf/flan-t5-large-qlora-financial-phrasebank | raymondowf | 2023-09-02T16:21:01Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T16:20:56Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
patientxtr/photon_v1_onnx | patientxtr | 2023-09-02T16:12:46Z | 12 | 1 | diffusers | [
"diffusers",
"onnx",
"text-to-image",
"license:unknown",
"diffusers:OnnxStableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-08-28T17:42:59Z | ---
license: unknown
library_name: diffusers
pipeline_tag: text-to-image
---
Microsoft Olive optimized onnx version of "https://huggingface.co/digiplay/Photon_v1" |
KingKazma/xsum_t5-small_lora_500_10_50000_8_e3_s6789_v4_l4_r4 | KingKazma | 2023-09-02T16:04:17Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T16:04:16Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
asparius/bert-base-combined-large | asparius | 2023-09-02T15:58:53Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-08-26T16:01:47Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-combined-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-combined-large
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3029
- Accuracy: 0.8940
- F1: 0.8956
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2668 | 1.0 | 3077 | 0.2812 | 0.8931 | 0.8915 |
| 0.2042 | 2.0 | 6154 | 0.2675 | 0.8952 | 0.8950 |
| 0.1453 | 3.0 | 9231 | 0.3029 | 0.8940 | 0.8956 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
The-matt/autumn-shadow-48_130 | The-matt | 2023-09-02T15:55:52Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:55:47Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/roberta-base-finetuned-wls-manual-10ep | btamm12 | 2023-09-02T15:52:47Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:50:16Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-10ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-10ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0599
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8201 | 0.93 | 7 | 1.5286 |
| 1.4462 | 2.0 | 15 | 1.3480 |
| 1.3032 | 2.93 | 22 | 1.3377 |
| 1.2564 | 4.0 | 30 | 1.1907 |
| 1.246 | 4.93 | 37 | 1.1702 |
| 1.1777 | 6.0 | 45 | 1.1549 |
| 1.118 | 6.93 | 52 | 1.0611 |
| 1.1339 | 8.0 | 60 | 1.1084 |
| 1.1158 | 8.93 | 67 | 1.1376 |
| 1.0143 | 9.33 | 70 | 1.1225 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_120 | The-matt | 2023-09-02T15:49:07Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:49:04Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
norman365/atom-Llama2-chinese-7b-ggml.bin | norman365 | 2023-09-02T15:47:03Z | 0 | 0 | null | [
"zh",
"license:apache-2.0",
"region:us"
]
| null | 2023-09-02T15:46:12Z | ---
license: apache-2.0
language:
- zh
--- |
kaneki1933/testes | kaneki1933 | 2023-09-02T15:44:09Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-06-20T17:55:55Z | ---
license: creativeml-openrail-m
---
|
btamm12/bert-base-uncased-finetuned-wls-manual-9ep-lower | btamm12 | 2023-09-02T15:42:56Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:40:41Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-9ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-9ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2788
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1096 | 0.93 | 7 | 1.9445 |
| 1.5963 | 2.0 | 15 | 1.5711 |
| 1.4734 | 2.93 | 22 | 1.4391 |
| 1.3716 | 4.0 | 30 | 1.4138 |
| 1.2719 | 4.93 | 37 | 1.2480 |
| 1.2486 | 6.0 | 45 | 1.2483 |
| 1.2156 | 6.93 | 52 | 1.2662 |
| 1.1523 | 8.0 | 60 | 1.3172 |
| 1.1596 | 8.4 | 63 | 1.2467 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_110 | The-matt | 2023-09-02T15:41:55Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:41:51Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/bert-base-cased-finetuned-wls-manual-9ep | btamm12 | 2023-09-02T15:40:33Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:38:23Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-9ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-9ep
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1588 | 0.93 | 7 | 1.8380 |
| 1.6343 | 2.0 | 15 | 1.6555 |
| 1.6181 | 2.93 | 22 | 1.5436 |
| 1.4245 | 4.0 | 30 | 1.4227 |
| 1.3525 | 4.93 | 37 | 1.4219 |
| 1.2804 | 6.0 | 45 | 1.3093 |
| 1.2167 | 6.93 | 52 | 1.2617 |
| 1.1662 | 8.0 | 60 | 1.2366 |
| 1.1817 | 8.4 | 63 | 1.2008 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
shmart/shmisper-medium-PL | shmart | 2023-09-02T15:40:20Z | 1 | 0 | transformers | [
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2023-04-11T20:17:19Z | ---
license: mit
---
# faster-whisper finetuned model for PL Phonetic transcription
This model is a result of finetuning `openai/whisper-medium` model on custom PL dataset and then conversion to `faster-whisper` model.
In training dataset there were also 5 english speakers and 4 japanese speakers for which polish transcription was manually created.
## About model:
- I created this because original whisper model is not doing precise transcription, e.g. some disfluences like stuttering or repetition are normalized.
- This model generates more accurate transcriptions so it's better for automatic creation of unsupervised dataset for Text-To-Speech model training.
- I noticed it also normalized numbers so it's in word form, there are no digits generated in transcript.
- English audio is transcribed into phonetic polish transcription instead of leaving original english form or translating to polish language like it's in original whisper model (however due to low amount of data it was trained on, it's far from perfection)
## Example:
```
from faster_whisper import WhisperModel
import huggingface_hub
model_path = huggingface_hub.snapshot_download("shmart/shmisper-medium-PL")
model = WhisperModel(model_path, device="cuda", compute_type="float16")
options = {
'language': "pl",
'beam_size': 5,
'without_timestamps': True,
'suppress_tokens': [],
'log_prob_threshold': None,
'no_speech_threshold': 0.05
}
input_wav_path = './audio.wav'
result, info = model.transcribe(input_wav_path, **options)
text = ' '.join([r.text for r in result])
print(text)
```
|
rajaswa-postman/es_chat_lora | rajaswa-postman | 2023-09-02T15:39:41Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:22:10Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
haddadalwi/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns | haddadalwi | 2023-09-02T15:36:53Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-large-uncased-whole-word-masking-finetuned-squad",
"base_model:finetune:google-bert/bert-large-uncased-whole-word-masking-finetuned-squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-09-01T16:30:38Z | ---
license: apache-2.0
base_model: bert-large-uncased-whole-word-masking-finetuned-squad
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns
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-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the squad_v2 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 266 | 0.0000 |
| 0.0649 | 2.0 | 532 | 0.0000 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/bert-base-cased-finetuned-wls-manual-8ep | btamm12 | 2023-09-02T15:33:27Z | 115 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:31:23Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-8ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-8ep
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1599 | 0.93 | 7 | 1.8488 |
| 1.6266 | 2.0 | 15 | 1.6340 |
| 1.5518 | 2.93 | 22 | 1.5175 |
| 1.382 | 4.0 | 30 | 1.4146 |
| 1.3309 | 4.93 | 37 | 1.4054 |
| 1.2715 | 6.0 | 45 | 1.3004 |
| 1.2182 | 6.93 | 52 | 1.2688 |
| 1.1738 | 7.47 | 56 | 1.2962 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/roberta-base-finetuned-wls-manual-7ep | btamm12 | 2023-09-02T15:31:16Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:28:58Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-7ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-7ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8224 | 0.93 | 7 | 1.5284 |
| 1.4374 | 2.0 | 15 | 1.3331 |
| 1.2988 | 2.93 | 22 | 1.3356 |
| 1.2666 | 4.0 | 30 | 1.1919 |
| 1.2422 | 4.93 | 37 | 1.1769 |
| 1.1804 | 6.0 | 45 | 1.1424 |
| 1.1443 | 6.53 | 49 | 1.1581 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_90 | The-matt | 2023-09-02T15:27:43Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:27:39Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/bert-base-cased-finetuned-wls-manual-7ep | btamm12 | 2023-09-02T15:26:41Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:24:40Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-7ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-7ep
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1707 | 0.93 | 7 | 1.9153 |
| 1.658 | 2.0 | 15 | 1.6462 |
| 1.5689 | 2.93 | 22 | 1.5263 |
| 1.4013 | 4.0 | 30 | 1.4385 |
| 1.3501 | 4.93 | 37 | 1.4224 |
| 1.293 | 6.0 | 45 | 1.3189 |
| 1.2473 | 6.53 | 49 | 1.2231 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Satorio/so-vits-4.1-Nice_Nature | Satorio | 2023-09-02T15:22:42Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-08-06T13:14:51Z | ---
license: cc-by-nc-4.0
---
Model: Nice Nature(Umamusume: Pretty Derby)
Dataset Source: DMM Umamusume Game
Still training to improve model... Maybe better, maybe not... |
The-matt/autumn-shadow-48_80 | The-matt | 2023-09-02T15:21:01Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:20:51Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
crewdon/AICategoryMapping-multilingual-e5-small | crewdon | 2023-09-02T15:20:57Z | 14 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-09-02T15:05:10Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# AICategoryMapping-multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 94 with parameters:
```
{'batch_size': 400}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 40,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 376,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
btamm12/bert-base-uncased-finetuned-wls-manual-6ep-lower | btamm12 | 2023-09-02T15:20:25Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:18:28Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-6ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-6ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1123 | 0.93 | 7 | 1.9531 |
| 1.6034 | 2.0 | 15 | 1.5832 |
| 1.489 | 2.93 | 22 | 1.4553 |
| 1.3975 | 4.0 | 30 | 1.4448 |
| 1.3074 | 4.93 | 37 | 1.2918 |
| 1.3083 | 5.6 | 42 | 1.4088 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/bert-base-uncased-finetuned-wls-manual-5ep-lower | btamm12 | 2023-09-02T15:14:00Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:12:03Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-5ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-5ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1142 | 0.93 | 7 | 1.9585 |
| 1.6082 | 2.0 | 15 | 1.5910 |
| 1.4973 | 2.93 | 22 | 1.4644 |
| 1.4145 | 4.0 | 30 | 1.4717 |
| 1.335 | 4.67 | 35 | 1.4035 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_70 | The-matt | 2023-09-02T15:13:29Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:13:13Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/roberta-base-finetuned-wls-manual-4ep | btamm12 | 2023-09-02T15:09:55Z | 123 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T15:07:08Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-4ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-4ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8232 | 0.93 | 7 | 1.5217 |
| 1.4594 | 2.0 | 15 | 1.4173 |
| 1.402 | 2.93 | 22 | 1.3668 |
| 1.3193 | 3.73 | 28 | 1.2170 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_lora_500_10_50000_8_e1_s6789_v4_l4_r4 | KingKazma | 2023-09-02T15:09:11Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T15:09:10Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
DrishtiSharma/mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.001 | DrishtiSharma | 2023-09-02T15:04:08Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-09-02T12:48:56Z | ---
license: mit
base_model: facebook/mbart-large-50
tags:
- translation
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.001
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. -->
# mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.001
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9549
- Bleu: 45.0307
- Rouge: {'rouge1': 0.7049318825090395, 'rouge2': 0.5238048751750992, 'rougeL': 0.684187379601513, 'rougeLsum': 0.6843574853855577}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|
| 1.4627 | 1.0 | 4500 | 1.0255 | 42.1880 | {'rouge1': 0.6725633216905762, 'rouge2': 0.48605402524493657, 'rougeL': 0.6498853764470456, 'rougeLsum': 0.6501981166312041} |
| 0.8878 | 2.0 | 9000 | 0.9572 | 44.1734 | {'rouge1': 0.6912686406245903, 'rouge2': 0.5093695171345348, 'rougeL': 0.6701896043455414, 'rougeLsum': 0.6703473419504804} |
| 0.7125 | 3.0 | 13500 | 0.9414 | 44.8709 | {'rouge1': 0.7051197958532004, 'rouge2': 0.5210482863677958, 'rougeL': 0.6843075431636916, 'rougeLsum': 0.6846265298079588} |
| 0.6092 | 4.0 | 18000 | 0.9549 | 45.0821 | {'rouge1': 0.7047932899349161, 'rouge2': 0.523739339466653, 'rougeL': 0.6840127607742443, 'rougeLsum': 0.684202100852132} |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
btamm12/roberta-base-finetuned-wls-manual-3ep | btamm12 | 2023-09-02T15:01:54Z | 129 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T14:59:09Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-3ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-3ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3361
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8156 | 0.93 | 7 | 1.5116 |
| 1.4371 | 2.0 | 15 | 1.3472 |
| 1.3218 | 2.8 | 21 | 1.3278 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
yaohuacn/a2c-PandaPickAndPlace-v3 | yaohuacn | 2023-09-02T15:00:35Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T14:45:56Z | ---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
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
...
```
|
tsukemono/japanese-stablelm-base-alpha-7b-qlora-marisa | tsukemono | 2023-09-02T14:58:35Z | 0 | 0 | null | [
"ja",
"region:us"
]
| null | 2023-08-28T08:24:30Z | ---
language:
- ja
---
## モデルの概略
霧雨魔理沙とおしゃべりできるモデルです。
[Japanese-StableLM-Base-Alpha-7B](https://huggingface.co/stabilityai/japanese-stablelm-base-alpha-7b)のLoRAデータになります
## 使い方
推論のさせかたの一例をhow_to_use.ipynbに記しましたので参考にしていただけると幸いです。
｢ユーザー: hogehoge\n魔理沙: ｣といったプロンプトを与えてあげることで、魔理沙とおしゃべりができるようになります。
## 備考
これは東方Projectの二次創作です
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0 |
btamm12/roberta-base-finetuned-wls-manual-2ep | btamm12 | 2023-09-02T14:53:53Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T14:51:11Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-2ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-2ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8161 | 0.93 | 7 | 1.5123 |
| 1.4497 | 1.87 | 14 | 1.3929 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nightdude/config_821 | nightdude | 2023-09-02T14:53:38Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T14:52:34Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
btamm12/bert-base-cased-finetuned-wls-manual-2ep | btamm12 | 2023-09-02T14:48:32Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T14:46:11Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-2ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-2ep
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6386
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1651 | 0.93 | 7 | 1.8869 |
| 1.6819 | 1.87 | 14 | 1.7442 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_30 | The-matt | 2023-09-02T14:45:31Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T14:45:15Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
DrYond3r/OrelsanV1 | DrYond3r | 2023-09-02T14:44:10Z | 0 | 0 | null | [
"arxiv:1910.09700",
"license:openrail",
"region:us"
]
| null | 2023-08-30T07:07:50Z | ---
license: openrail
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
btamm12/bert-base-cased-finetuned-wls-manual-1ep | btamm12 | 2023-09-02T14:42:09Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-02T14:40:23Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-1ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-1ep
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1332 | 0.93 | 7 | 1.9236 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
foxxy-hm/wav2vec2-base-finetune-vi-v2 | foxxy-hm | 2023-09-02T14:41:30Z | 25 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-01T13:15:22Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-finetune-vi-v2
results: []
widget:
- example_title: SOICT 2023 - SLU public test 1
src: >-
https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/055R7BruAa333g9teFfamQH.wav
- example_title: SOICT 2023 - SLU public test 2
src: >-
https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/0BLHhoJexE8THB8BrsZxWbh.wav
- example_title: SOICT 2023 - SLU public test 3
src: >-
https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/1ArUTGWJQ9YALH2xaNhU6GV.wav
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetune-vi-v2
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2188
- Wer: 0.1391
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3873 | 0.67 | 500 | 2.4321 | 0.9719 |
| 1.4812 | 1.34 | 1000 | 0.5449 | 0.3062 |
| 0.7731 | 2.0 | 1500 | 0.3793 | 0.2263 |
| 0.542 | 2.67 | 2000 | 0.3021 | 0.2002 |
| 0.4461 | 3.34 | 2500 | 0.2905 | 0.1862 |
| 0.4175 | 4.01 | 3000 | 0.2687 | 0.1771 |
| 0.3878 | 4.67 | 3500 | 0.2958 | 0.1751 |
| 0.3373 | 5.34 | 4000 | 0.2713 | 0.1721 |
| 0.3046 | 6.01 | 4500 | 0.2505 | 0.1616 |
| 0.2933 | 6.68 | 5000 | 0.2561 | 0.1611 |
| 0.285 | 7.34 | 5500 | 0.2405 | 0.1617 |
| 0.2998 | 8.01 | 6000 | 0.2363 | 0.1578 |
| 0.2486 | 8.68 | 6500 | 0.2254 | 0.1570 |
| 0.2682 | 9.35 | 7000 | 0.2306 | 0.1547 |
| 0.2327 | 10.01 | 7500 | 0.2289 | 0.1537 |
| 0.2141 | 10.68 | 8000 | 0.2383 | 0.1499 |
| 0.2124 | 11.35 | 8500 | 0.2261 | 0.15 |
| 0.2156 | 12.02 | 9000 | 0.2142 | 0.1511 |
| 0.2082 | 12.68 | 9500 | 0.2386 | 0.1467 |
| 0.1814 | 13.35 | 10000 | 0.2301 | 0.1448 |
| 0.1836 | 14.02 | 10500 | 0.2302 | 0.1446 |
| 0.18 | 14.69 | 11000 | 0.2244 | 0.1445 |
| 0.1756 | 15.35 | 11500 | 0.2280 | 0.1439 |
| 0.1693 | 16.02 | 12000 | 0.2307 | 0.1426 |
| 0.1588 | 16.69 | 12500 | 0.2164 | 0.1422 |
| 0.1587 | 17.36 | 13000 | 0.2198 | 0.1417 |
| 0.1738 | 18.02 | 13500 | 0.2282 | 0.1411 |
| 0.1524 | 18.69 | 14000 | 0.2274 | 0.1394 |
| 0.1569 | 19.36 | 14500 | 0.2178 | 0.1396 |
| 0.1433 | 20.03 | 15000 | 0.2200 | 0.1413 |
| 0.1512 | 20.69 | 15500 | 0.2193 | 0.1382 |
| 0.1375 | 21.36 | 16000 | 0.2174 | 0.1393 |
| 0.1302 | 22.03 | 16500 | 0.2246 | 0.1391 |
| 0.146 | 22.7 | 17000 | 0.2222 | 0.1392 |
| 0.1265 | 23.36 | 17500 | 0.2188 | 0.1391 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Campqt/ppo-LunarLander-v2-unit8 | Campqt | 2023-09-02T14:39:07Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T14:24:15Z | ---
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: -78.14 +/- 80.44
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': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Campqt/ppo-LunarLander-v2-unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
rrozb/Reinforce-1 | rrozb | 2023-09-02T14:36:41Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T14:36:31Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-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
|
Lenouche/Sblerky | Lenouche | 2023-09-02T14:30:42Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-08-13T23:01:35Z | ---
license: openrail
language:
- fr
--- |
Lenouche/Conkerax | Lenouche | 2023-09-02T14:30:03Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-08-13T22:13:05Z | ---
license: openrail
language :
- fr
--- |
Lenouche/GiaTechAndGaming | Lenouche | 2023-09-02T14:28:46Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-08-17T01:44:54Z | ---
language:
- fr
license: openrail
--- |
Zevin2023/MoC-IQA | Zevin2023 | 2023-09-02T14:28:05Z | 0 | 0 | null | [
"aa",
"license:openrail",
"region:us"
]
| null | 2023-09-02T14:02:17Z | ---
license: openrail
language:
- aa
metrics:
- accuracy
--- |
Lenouche/TevIciJapon | Lenouche | 2023-09-02T14:27:59Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-08-17T18:47:02Z | ---
language:
- fr
license: openrail
--- |
Lenouche/LouisSan | Lenouche | 2023-09-02T14:27:01Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-08-27T00:10:33Z | ---
language:
- fr
license: openrail
--- |
Lenouche/BenjaminCode | Lenouche | 2023-09-02T14:26:29Z | 0 | 0 | null | [
"fr",
"license:openrail",
"region:us"
]
| null | 2023-09-02T00:06:50Z | ---
language:
- fr
license: openrail
--- |
CzarnyRycerz/ppo-Huggy | CzarnyRycerz | 2023-09-02T14:16:53Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-09-02T14:16:42Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: CzarnyRycerz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
VinayHajare/ppo-LunarLander-v2 | VinayHajare | 2023-09-02T13:51:21Z | 5 | 3 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T06:37:42Z | ---
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: 263.26 +/- 19.25
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)
```python
# !pip gymnasium huggingface-sb3 stable_baselines3[extra]
import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
repo_id = "VinayHajare/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
eval_env = gym.make("LunarLander-v2", render_mode="human")
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,print_system_info=True)
mean_reward, std_reward = evaluate_policy(model,eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Enjoy trained agent
observation, info = eval_env.reset()
for _ in range(1000):
action, _states = model.predict(observation, deterministic=True)
observation, rewards, terminated, truncated, info = eval_env.step(action)
eval_env.render()
```
|
DrishtiSharma/mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.0 | DrishtiSharma | 2023-09-02T13:50:23Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-09-02T10:31:15Z | ---
license: mit
base_model: facebook/mbart-large-50
tags:
- translation
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.0
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. -->
# mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.0
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9549
- Bleu: 45.0307
- Rouge: {'rouge1': 0.7049318825090395, 'rouge2': 0.5238048751750992, 'rougeL': 0.684187379601513, 'rougeLsum': 0.6843574853855577}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|
| 1.4627 | 1.0 | 4500 | 1.0255 | 42.1880 | {'rouge1': 0.6725633216905762, 'rouge2': 0.48605402524493657, 'rougeL': 0.6498853764470456, 'rougeLsum': 0.6501981166312041} |
| 0.8878 | 2.0 | 9000 | 0.9572 | 44.1734 | {'rouge1': 0.6912686406245903, 'rouge2': 0.5093695171345348, 'rougeL': 0.6701896043455414, 'rougeLsum': 0.6703473419504804} |
| 0.7125 | 3.0 | 13500 | 0.9414 | 44.8709 | {'rouge1': 0.7051197958532004, 'rouge2': 0.5210482863677958, 'rougeL': 0.6843075431636916, 'rougeLsum': 0.6846265298079588} |
| 0.6092 | 4.0 | 18000 | 0.9549 | 45.0821 | {'rouge1': 0.7047932899349161, 'rouge2': 0.523739339466653, 'rougeL': 0.6840127607742443, 'rougeLsum': 0.684202100852132} |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
mohammadhossein/bert-base-uncased-riddle-finetuned | mohammadhossein | 2023-09-02T13:42:47Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"mhs",
"generated_from_trainer",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2023-09-02T13:38:38Z | ---
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- mhs
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sentence_puzzle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0932
- Accuracy: 0.9365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 36 | 0.2763 | 0.9048 |
| No log | 2.0 | 72 | 0.2388 | 0.9206 |
| No log | 3.0 | 108 | 0.2465 | 0.9206 |
| No log | 4.0 | 144 | 0.0958 | 0.9206 |
| No log | 5.0 | 180 | 0.0932 | 0.9365 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ckandemir/xlm-roberta-base-finetuned-panx-de | ckandemir | 2023-09-02T13:28:15Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-02T08:51:32Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
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: 0.6993243243243242
---
<!-- 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-de
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: 0.3902
- F1: 0.6993
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1085 | 1.0 | 50 | 0.5687 | 0.5579 |
| 0.5001 | 2.0 | 100 | 0.4186 | 0.6781 |
| 0.3535 | 3.0 | 150 | 0.3902 | 0.6993 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Kamer/NoDuplicates | Kamer | 2023-09-02T13:27:46Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-01T16:09:33Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NoDuplicates
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. -->
# NoDuplicates
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- Accuracy: 0.9128
- F1 Macro: 0.8384
- F1 Class 0: 0.9406
- F1 Class 1: 0.3333
- F1 Class 2: 0.9127
- F1 Class 3: 0.6471
- F1 Class 4: 0.8254
- F1 Class 5: 0.8293
- F1 Class 6: 0.8767
- F1 Class 7: 0.7606
- F1 Class 8: 0.7500
- F1 Class 9: 0.9878
- F1 Class 10: 0.9444
- F1 Class 11: 0.9630
- F1 Class 12: 0.9265
- F1 Class 13: 0.8980
- F1 Class 14: 0.8444
- F1 Class 15: 0.8132
- F1 Class 16: 0.7778
- F1 Class 17: 0.9651
- F1 Class 18: 0.9574
- F1 Class 19: 0.8148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.4862 | 0.27 | 300 | 0.8201 | 0.7845 | 0.4484 | 0.8675 | 0.0 | 0.8627 | 0.0 | 0.6733 | 0.0 | 0.6627 | 0.0 | 0.0 | 0.9862 | 0.1935 | 0.9600 | 0.8299 | 0.0833 | 0.2353 | 0.24 | 0.0400 | 0.8852 | 0.9451 | 0.5033 |
| 0.7269 | 0.53 | 600 | 0.5951 | 0.8491 | 0.6504 | 0.9048 | 0.0 | 0.8567 | 0.0 | 0.7596 | 0.6111 | 0.6887 | 0.0 | 0.0 | 0.9877 | 0.8033 | 0.9286 | 0.8798 | 0.9167 | 0.74 | 0.6857 | 0.5823 | 0.9506 | 0.9485 | 0.7640 |
| 0.5429 | 0.8 | 900 | 0.5375 | 0.8637 | 0.7086 | 0.8904 | 0.0 | 0.8589 | 0.0 | 0.7254 | 0.7805 | 0.8215 | 0.6769 | 0.0 | 0.9877 | 0.7833 | 1.0 | 0.9022 | 0.9130 | 0.7912 | 0.7733 | 0.7048 | 0.9032 | 0.9474 | 0.7119 |
| 0.4594 | 1.06 | 1200 | 0.5110 | 0.8805 | 0.7113 | 0.9099 | 0.0 | 0.8925 | 0.0 | 0.7706 | 0.7391 | 0.8139 | 0.4091 | 0.0 | 0.9908 | 0.8785 | 1.0 | 0.8983 | 0.8936 | 0.8090 | 0.7556 | 0.7907 | 0.9529 | 0.9574 | 0.7647 |
| 0.3484 | 1.33 | 1500 | 0.4679 | 0.8951 | 0.7667 | 0.9180 | 0.0 | 0.9080 | 0.6957 | 0.8 | 0.7619 | 0.8299 | 0.6875 | 0.0 | 0.9908 | 0.8909 | 1.0 | 0.9196 | 0.9130 | 0.8172 | 0.7865 | 0.7527 | 0.9398 | 0.9474 | 0.7755 |
| 0.3744 | 1.59 | 1800 | 0.4359 | 0.8951 | 0.7774 | 0.9290 | 0.0 | 0.8815 | 0.8462 | 0.8049 | 0.7805 | 0.8449 | 0.7059 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.9143 | 0.8980 | 0.8387 | 0.7475 | 0.7179 | 0.9647 | 0.9583 | 0.7895 |
| 0.3514 | 1.86 | 2100 | 0.5161 | 0.8903 | 0.7592 | 0.9109 | 0.0 | 0.8973 | 0.6429 | 0.7603 | 0.7907 | 0.8571 | 0.7077 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.8971 | 0.8936 | 0.7042 | 0.7324 | 0.7857 | 0.9595 | 0.9574 | 0.7609 |
| 0.3111 | 2.12 | 2400 | 0.4327 | 0.9080 | 0.8027 | 0.9283 | 0.3333 | 0.9141 | 0.7407 | 0.8207 | 0.8095 | 0.8622 | 0.7606 | 0.0 | 0.9908 | 0.9298 | 0.9630 | 0.9215 | 0.9167 | 0.8041 | 0.8 | 0.8132 | 0.9651 | 0.9574 | 0.8224 |
| 0.2088 | 2.39 | 2700 | 0.4356 | 0.9128 | 0.8452 | 0.9386 | 0.3333 | 0.9058 | 0.8462 | 0.8265 | 0.8 | 0.8562 | 0.7429 | 0.7500 | 0.9893 | 0.9346 | 0.9630 | 0.9322 | 0.8936 | 0.8205 | 0.8372 | 0.7765 | 0.9651 | 0.9574 | 0.8350 |
| 0.2317 | 2.65 | 3000 | 0.4294 | 0.9137 | 0.8217 | 0.9365 | 0.3333 | 0.9102 | 0.625 | 0.8243 | 0.8293 | 0.875 | 0.8056 | 0.3333 | 0.9893 | 0.9444 | 0.9630 | 0.9284 | 0.8980 | 0.8478 | 0.8471 | 0.7816 | 0.9651 | 0.9574 | 0.8400 |
| 0.1816 | 2.92 | 3300 | 0.4279 | 0.9128 | 0.8384 | 0.9406 | 0.3333 | 0.9127 | 0.6471 | 0.8254 | 0.8293 | 0.8767 | 0.7606 | 0.7500 | 0.9878 | 0.9444 | 0.9630 | 0.9265 | 0.8980 | 0.8444 | 0.8132 | 0.7778 | 0.9651 | 0.9574 | 0.8148 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
LiChenYi/QA | LiChenYi | 2023-09-02T13:05:16Z | 0 | 0 | null | [
"license:unknown",
"region:us"
]
| null | 2023-09-02T12:55:15Z | ---
license: unknown
---
在AI使用过程中，遇到的问题进行记录，供后来者避坑
# 2colab 使用过程的问题
1. 在colab中拉去 huggingface仓库中的数据报如下错误：
Connecting to [huggingface.co](http://huggingface.co/) ([huggingface.co](http://huggingface.co/))|18.239.50.16|:443... connected.
HTTP request sent, awaiting response... 401 Unauthorized
解决方案：
找到huggingface设置，用户的访问请求【User Access requests】：设置为禁用 |
quantumaikr/KoreanLM-3B | quantumaikr | 2023-09-02T12:55:53Z | 109 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"korean",
"foundation",
"ko",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-21T09:02:18Z | ---
language:
- ko
- en
pipeline_tag: text-generation
tags:
- llama
- korean
- foundation
---
<p align="center" width="100%">
<img src="https://i.imgur.com/snFDU0P.png" alt="KoreanLM icon" style="width: 500px; display: block; margin: auto; border-radius: 10%;">
</p>
# KoreanLM: 한국어 언어모델 프로젝트
KoreanLM은 한국어 언어모델을 개발하기 위한 오픈소스 프로젝트입니다. 현재 대부분의 언어모델들은 영어에 초점을 맞추고 있어, 한국어에 대한 학습이 상대적으로 부족하고 토큰화 과정에서 비효율적인 경우가 있습니다. 이러한 문제를 해결하고 한국어에 최적화된 언어모델을 제공하기 위해 KoreanLM 프로젝트를 시작하게 되었습니다.
## 프로젝트 목표
1. 한국어에 특화된 언어모델 개발: 한국어의 문법, 어휘, 문화적 특성을 반영하여 한국어를 더 정확하게 이해하고 생성할 수 있는 언어모델을 개발합니다.
2. 효율적인 토큰화 방식 도입: 한국어 텍스트의 토큰화 과정에서 효율적이고 정확한 분석이 가능한 새로운 토큰화 방식을 도입하여 언어모델의 성능을 향상시킵니다.
3. 거대 언어모델의 사용성 개선: 현재 거대한 사이즈의 언어모델들은 기업이 자사의 데이터를 파인튜닝하기 어려운 문제가 있습니다. 이를 해결하기 위해 한국어 언어모델의 크기를 조절하여 사용성을 개선하고, 자연어 처리 작업에 더 쉽게 적용할 수 있도록 합니다.
## 사용 방법
다음은 transformers 라이브러리를 통해 모델과 토크나이저를 로딩하는 예제입니다.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained("quantumaikr/KoreanLM-3B")
tokenizer = transformers.AutoTokenizer.from_pretrained("quantumaikr/KoreanLM-3B")
```
## 기술 문의
[email protected]
www.quantumai.kr |
HorcruxNo13/swinv2-small-patch4-window8-256-finetuned-eurosat | HorcruxNo13 | 2023-09-02T12:44:00Z | 146 | 0 | transformers | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-small-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-small-patch4-window8-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-02T12:25:25Z | ---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7333333333333333
---
<!-- 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. -->
# swinv2-small-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5868
- Accuracy: 0.7333
## 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: 0.05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.1951 | 0.2667 |
| 5.0901 | 2.0 | 16 | 1.4301 | 0.7333 |
| 2.785 | 3.0 | 24 | 1.1514 | 0.2667 |
| 0.8599 | 4.0 | 32 | 0.5810 | 0.7333 |
| 0.6058 | 5.0 | 40 | 0.5868 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
simlamkr1/llama2-simtestmodel1 | simlamkr1 | 2023-09-02T12:32:06Z | 0 | 0 | peft | [
"peft",
"pytorch",
"llama",
"region:us"
]
| null | 2023-09-01T13:56:00Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
penguinman73/xlm-roberta-base-finetuned-panx-en | penguinman73 | 2023-09-02T12:25:02Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-02T12:22:08Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-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: 0.4028
- F1: 0.6831
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1353 | 1.0 | 50 | 0.6267 | 0.5068 |
| 0.5283 | 2.0 | 100 | 0.4369 | 0.6552 |
| 0.358 | 3.0 | 150 | 0.4028 | 0.6831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
NiscR/Reinforce-Pixel1 | NiscR | 2023-09-02T12:19:12Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T11:35:10Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixel1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.20 +/- 23.29
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
|
penguinman73/xlm-roberta-base-finetuned-panx-fr | penguinman73 | 2023-09-02T12:18:32Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-02T12:13:41Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2760
- F1: 0.8452
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5839 | 1.0 | 191 | 0.3623 | 0.7527 |
| 0.2607 | 2.0 | 382 | 0.2836 | 0.8238 |
| 0.1745 | 3.0 | 573 | 0.2760 | 0.8452 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
penguinman73/xlm-roberta-base-finetuned-panx-de-fr | penguinman73 | 2023-09-02T12:12:18Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-02T11:58:38Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- F1: 0.8603
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1813 | 0.8232 |
| 0.1482 | 2.0 | 1430 | 0.1586 | 0.8462 |
| 0.0959 | 3.0 | 2145 | 0.1623 | 0.8603 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
darthruebezahl/alicia02092023 | darthruebezahl | 2023-09-02T12:09:23Z | 29 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-02T12:07:42Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: Alicia02092023
---
### Alicia02092023 Dreambooth model trained by darthruebezahl with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
Alicia02092023 (use that on your prompt)
|
inkoziev/chargpt-96M | inkoziev | 2023-09-02T12:08:27Z | 146 | 3 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"causal-lm",
"ru",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-15T11:18:43Z | ---
license: openrail
language:
- ru
library_name: transformers
tags:
- pytorch
- causal-lm
---
## CharGPT-96M
Это крошечная языковая модель с **посимвольной** токенизацией для всевозможных экспериментов, когда задача решается плохо из-за BPE токенизации на слова и их части.
Подробное описание и примеры использования можно посмотреть в карточке модели [charllama-35M](https://huggingface.co/inkoziev/charllama-35M).
|
fkc294/xlm-roberta-base-finetuned-panx-de | fkc294 | 2023-09-02T11:56:53Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-02T11:06:08Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8646808510638297
---
<!-- 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-de
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: 0.1361
- F1: 0.8647
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2595 | 1.0 | 525 | 0.1540 | 0.8302 |
| 0.1265 | 2.0 | 1050 | 0.1493 | 0.8468 |
| 0.0806 | 3.0 | 1575 | 0.1361 | 0.8647 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
penguinman73/xlm-roberta-base-finetuned-panx-de | penguinman73 | 2023-09-02T11:56:10Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-27T01:35:12Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
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: 0.2992
- F1: 0.8285
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6098 | 1.0 | 167 | 0.3570 | 0.7592 |
| 0.2633 | 2.0 | 334 | 0.2995 | 0.8171 |
| 0.1792 | 3.0 | 501 | 0.2992 | 0.8285 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
y22ma/sdxl-dabble-model | y22ma | 2023-09-02T11:46:15Z | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2023-09-01T14:12:19Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: y22ma/Dabble-interior-captions
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - y22ma/sdxl-dabble-model
This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **y22ma/Dabble-interior-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: a beautiful living room:
Special VAE used for training: None.
|
amgodbole/bloom_prompt_tuning_1693653323.8270018 | amgodbole | 2023-09-02T11:36:37Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T11:36:36Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
softaken/softaken-dbx-to-pst-converter | softaken | 2023-09-02T11:35:00Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-09-02T11:18:55Z | Softaken DBX to PST Converter Software is a convenient computer program to export Outlook Express emails to Outlook PST file format. There are Users can export single and multiple DBX files and folders to Outlook PST file format. No need for any technical knowledge to operate this software, and convert DBX files to PST file format. Users can export unlimited DBX file conversion without any data limitation. The conversion tool provides a complete preview of the DBX file before the beginning of the conversion process. Users can export DBX files into multiple other world-famous file formats such as; PST, EML, EMLX, MSG, MBOX, etc. The software can also work with multiple MS Outlook versions such as; 2002, 2003, 2007, 2010, 2013, 2016, and 2019. Users can save their exported data as per the required location on the desktop. This is Windows-based tool that can work with all Windows systems such as; Windows 11, Windows 10 S, Windows 10, Windows 8/8.1, Windows 7, Windows Vista, Windows XP, and Windows 2000, etc. Grab the free demo version of this software to learn more features and functions of the software.
Read More: https://www.softaken.com/dbx-to-pst-converter |
casque/FilmVelvia3 | casque | 2023-09-02T11:34:13Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-02T11:32:49Z | ---
license: creativeml-openrail-m
---
|
Mustain/line_fujiki3 | Mustain | 2023-09-02T11:20:10Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T11:20:04Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
dwitidibyajyoti/fine_tune_layoutmlv3_model | dwitidibyajyoti | 2023-09-02T11:15:36Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-30T09:45:10Z | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- Precision: 0.5109
- Recall: 0.6026
- F1: 0.5529
- Accuracy: 0.9222
## 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: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.6800 | 0.3371 | 0.3846 | 0.3593 | 0.7682 |
| No log | 16.67 | 200 | 0.3088 | 0.5204 | 0.6538 | 0.5795 | 0.9156 |
| No log | 25.0 | 300 | 0.2142 | 0.5326 | 0.6282 | 0.5765 | 0.9305 |
| No log | 33.33 | 400 | 0.2301 | 0.5795 | 0.6538 | 0.6145 | 0.9288 |
| 0.4115 | 41.67 | 500 | 0.2426 | 0.5618 | 0.6410 | 0.5988 | 0.9272 |
| 0.4115 | 50.0 | 600 | 0.4171 | 0.6190 | 0.6667 | 0.6420 | 0.8924 |
| 0.4115 | 58.33 | 700 | 0.2265 | 0.5393 | 0.6154 | 0.5749 | 0.9371 |
| 0.4115 | 66.67 | 800 | 0.2869 | 0.5506 | 0.6282 | 0.5868 | 0.9156 |
| 0.4115 | 75.0 | 900 | 0.2633 | 0.5568 | 0.6282 | 0.5904 | 0.9272 |
| 0.0231 | 83.33 | 1000 | 0.2763 | 0.5109 | 0.6026 | 0.5529 | 0.9222 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
yaohuacn/a2c-PandaReachDense-v3 | yaohuacn | 2023-09-02T11:10:11Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T11:05:12Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
madroid/onnx-whisper | madroid | 2023-09-02T11:02:02Z | 0 | 0 | null | [
"onnx",
"whisper",
"openai",
"license:apache-2.0",
"region:us"
]
| null | 2023-09-02T07:14:04Z | ---
license: apache-2.0
tags:
- whisper
- onnx
- openai
--- |
casque/majicmixRealistic_betterV2V25 | casque | 2023-09-02T11:00:36Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-02T10:43:18Z | ---
license: creativeml-openrail-m
---
|
JanSt/gbert-base-finetuned-twitter | JanSt | 2023-09-02T10:57:40Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:deepset/gbert-base",
"base_model:finetune:deepset/gbert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-08-24T10:58:07Z | ---
license: mit
base_model: deepset/gbert-base
tags:
- generated_from_trainer
model-index:
- name: gbert-base-finetuned-twitter
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. -->
# gbert-base-finetuned-twitter
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7380
## 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: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.194 | 1.0 | 4180 | 1.9622 |
| 2.0075 | 2.0 | 8360 | 1.8813 |
| 1.9429 | 3.0 | 12540 | 1.8339 |
| 1.8985 | 4.0 | 16720 | 1.8057 |
| 1.8676 | 5.0 | 20900 | 1.7801 |
| 1.8446 | 6.0 | 25080 | 1.7793 |
| 1.829 | 7.0 | 29260 | 1.7580 |
| 1.815 | 8.0 | 33440 | 1.7445 |
| 1.8048 | 9.0 | 37620 | 1.7319 |
| 1.7997 | 10.0 | 41800 | 1.7331 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
andrewcho92/helloworld | andrewcho92 | 2023-09-02T10:33:10Z | 0 | 0 | null | [
"text-generation",
"en",
"license:openrail",
"region:us"
]
| text-generation | 2023-09-02T10:14:37Z | ---
license: openrail
language:
- en
pipeline_tag: text-generation
--- |
adimazuz/texi-v3 | adimazuz | 2023-09-02T10:30:56Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-02T10:30:54Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: texi-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="adimazuz/texi-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"])
```
|
jigglesaw/finetuning-sentiment-model-3000-samples | jigglesaw | 2023-09-02T10:16:22Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-02T08:56:24Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.870967741935484
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3394
- Accuracy: 0.8667
- F1: 0.8710
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gg4ever/trOCR-final | gg4ever | 2023-09-02T10:15:40Z | 126 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2023-08-22T11:31:10Z | ---
license: apache-2.0
language:
- ko
metrics:
- cer
- wer
pipeline_tag: image-to-text
---
# trOCR-final
fine-tuned for VisionEncoderDecoderModel(encoder , decoder)
encoder = 'facebook/deit-base-distilled-patch16-384'
decoder = 'klue/roberta-base'
## How to Get Started with the Model
```python
from transformers import VisionEncoderDecoderModel,AutoTokenizer, TrOCRProcessor
import torch
from PIL import Image
device = torch.device('cuda') # change 'cuda' if you need.
image_path='(your image path)'
image = Image.open(image_path)
#model can be .jpg or .png
#hugging face download: https://huggingface.co/gg4ever/trOCR-final
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
trocr_model = "gg4ever/trOCR-final"
model = VisionEncoderDecoderModel.from_pretrained(trocr_model).to(device)
tokenizer = AutoTokenizer.from_pretrained(trocr_model)
pixel_values = (processor(image, return_tensors="pt").pixel_values).to(device)
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```
## Training Details
### Training Data
1M words generated by TextRecognitionDataGenerator(trdg) : https://github.com/Belval/TextRecognitionDataGenerator/blob/master/trdg/run.py
1.1M words from AI-hub OCR words dataset : https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=81
### Training Hyperparameters
|hyperparameters|values|
|-----------------------------|-------|
|predict_with_generate|True|
|evaluation_strategy|"steps"|
|per_device_train_batch_size|32|
|per_device_eval_batch_size|32|
|num_train_epochs|2|
|fp16|True|
|learning_rate|4e-5|
|eval_stept|10000|
|warmup_steps|20000|
|weight_decay|0.01| |
muralee491/murale | muralee491 | 2023-09-02T10:14:33Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T10:12:40Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
KhalfounMehdi/mura_vit_224 | KhalfounMehdi | 2023-09-02T10:01:11Z | 192 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"autotrain",
"dataset:KhalfounMehdi/mura_dataset_processed_224px_train_val",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-02T06:30:20Z | ---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- KhalfounMehdi/mura_dataset_processed_224px_train_val
metrics:
- accuracy
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
{'accuracy': 0.7795551112221945,
'recall': 0.9037098791162984,
'precision': 0.7690670450514366,
'f1': 0.83096972019931,
'total_time_in_seconds': 81.18831510400014,
'samples_per_second': 49.28049060846776,
'latency_in_seconds': 0.020292005774556397} |
nichelia/example100 | nichelia | 2023-09-02T09:40:53Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-02T09:40:51Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
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