librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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haonan-li/bactrian-vi-bloom-7b1-lora	haonan-li	2023-06-13T13:27:20Z	0	0	null	[ "arxiv:2305.15011", "license:mit", "region:us" ]	null	2023-06-13T13:27:08Z	--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Vietnamese. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-vi-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
haonan-li/bactrian-sw-bloom-7b1-lora	haonan-li	2023-06-13T13:27:08Z	0	0	null	[ "arxiv:2305.15011", "license:mit", "region:us" ]	null	2023-06-13T13:26:54Z	--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Swahili. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-sw-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
haonan-li/bactrian-zh-bloom-7b1-lora	haonan-li	2023-06-13T13:26:53Z	0	1	null	[ "arxiv:2305.15011", "license:mit", "region:us" ]	null	2023-06-13T13:26:29Z	--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Chinese. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-zh-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
haonan-li/bactrian-ar-bloom-7b1-lora	haonan-li	2023-06-13T13:25:48Z	0	0	null	[ "arxiv:2305.15011", "license:mit", "region:us" ]	null	2023-06-13T13:25:36Z	--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Arabic. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-ar-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
irfanamal/bert-base-uncased-classification-flat	irfanamal	2023-06-13T13:14:45Z	104	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T07:20:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-uncased-classification-flat 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-classification-flat This model is a fine-tuned version of [irfanamal/bert-base-uncased-finetuned-amazonreviews](https://huggingface.co/irfanamal/bert-base-uncased-finetuned-amazonreviews) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4951 - Accuracy: 0.4957 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.7227 \| 1.0 \| 1250 \| 3.3098 \| 0.3826 \| \| 2.6109 \| 2.0 \| 2500 \| 2.7897 \| 0.4568 \| \| 2.2396 \| 3.0 \| 3750 \| 2.5943 \| 0.4809 \| \| 1.9093 \| 4.0 \| 5000 \| 2.5155 \| 0.4937 \| \| 1.7949 \| 5.0 \| 6250 \| 2.4951 \| 0.4957 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
kejolong/SNI	kejolong	2023-06-13T13:00:30Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T12:58:55Z	--- license: creativeml-openrail-m ---
diallomama/fr-summarization	diallomama	2023-06-13T12:56:59Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:GEM/wiki_lingua", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-09T20:52:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - GEM/wiki_lingua metrics: - rouge model-index: - name: fr-summarization results: - task: name: Summarization type: summarization dataset: name: GEM/wiki_lingua fr type: GEM/wiki_lingua config: fr split: validation args: fr metrics: - name: Rouge1 type: rouge value: 100.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fr-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the GEM/wiki_lingua fr dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge1: 100.0 - Rouge2: 100.0 - Rougel: 100.0 - Rougelsum: 100.0 - Gen Len: 13.9390 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
LarryAIDraw/Positions_Lora32	LarryAIDraw	2023-06-13T12:53:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T11:31:39Z	--- license: creativeml-openrail-m --- https://civitai.com/models/54901/norian-hentaicore-lora-extracted
MarcoLYH/bert-base-uncased-finetuned-v2	MarcoLYH	2023-06-13T12:35:30Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T12:12:50Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: MarcoLYH/bert-base-uncased-finetuned-v2 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. --> # MarcoLYH/bert-base-uncased-finetuned-v2 This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6988 - Train End Logits Accuracy: 0.8958 - Train Start Logits Accuracy: 0.7917 - Validation Loss: 0.5760 - Validation End Logits Accuracy: 0.8500 - Validation Start Logits Accuracy: 0.8500 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 3, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.9477 \| 0.4375 \| 0.5417 \| 1.3534 \| 0.6000 \| 0.7000 \| 0 \| \| 1.7097 \| 0.4583 \| 0.5208 \| 1.0429 \| 0.7000 \| 0.7000 \| 1 \| \| 1.3797 \| 0.6875 \| 0.5833 \| 0.8816 \| 0.75 \| 0.7000 \| 2 \| \| 1.1394 \| 0.625 \| 0.6875 \| 0.7849 \| 0.75 \| 0.7000 \| 3 \| \| 1.0558 \| 0.6875 \| 0.625 \| 0.7057 \| 0.8000 \| 0.7000 \| 4 \| \| 0.8213 \| 0.8542 \| 0.8125 \| 0.6496 \| 0.8500 \| 0.75 \| 5 \| \| 0.8661 \| 0.8125 \| 0.7292 \| 0.6152 \| 0.8500 \| 0.8000 \| 6 \| \| 0.7864 \| 0.8542 \| 0.7917 \| 0.5948 \| 0.8500 \| 0.8500 \| 7 \| \| 0.8933 \| 0.8333 \| 0.75 \| 0.5822 \| 0.8500 \| 0.8500 \| 8 \| \| 0.6988 \| 0.8958 \| 0.7917 \| 0.5760 \| 0.8500 \| 0.8500 \| 9 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
clarin-knext/plt5-base-msmarco	clarin-knext	2023-06-13T12:24:51Z	118	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "pl", "arxiv:2305.19840", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-21T15:01:26Z	--- license: cc-by-sa-4.0 language: - pl --- Part of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language. Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf Contact: [email protected]
BlueAvenir/sti_modern_workplace_V_0_1	BlueAvenir	2023-06-13T12:18:56Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-06-13T12:18:27Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{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: `torch.utils.data.dataloader.DataLoader` of length 200 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "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": 200, "warmup_steps": 20, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ThirdEyeData/Text_Summarization	ThirdEyeData	2023-06-13T11:58:18Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-13T09:27:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: Text_Summarization results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1324 --- <!-- 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. --> # Text_Summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.7235 - Rouge1: 0.1324 - Rouge2: 0.0397 - Rougel: 0.1114 - Rougelsum: 0.111 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 74 \| 2.8406 \| 0.1286 \| 0.04 \| 0.1087 \| 0.1088 \| 19.0 \| \| No log \| 2.0 \| 148 \| 2.7235 \| 0.1324 \| 0.0397 \| 0.1114 \| 0.111 \| 19.0 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
yoninazarathy/distilbert-base-uncased-finetuned-cola	yoninazarathy	2023-06-13T11:52:00Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T11:45:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5363967157085073 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8120 - Matthews Correlation: 0.5364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5227 \| 1.0 \| 535 \| 0.5222 \| 0.4210 \| \| 0.3466 \| 2.0 \| 1070 \| 0.5042 \| 0.4832 \| \| 0.2335 \| 3.0 \| 1605 \| 0.5640 \| 0.5173 \| \| 0.1812 \| 4.0 \| 2140 \| 0.7634 \| 0.5200 \| \| 0.1334 \| 5.0 \| 2675 \| 0.8120 \| 0.5364 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
MarcoLYH/bert-base-uncased-finetuned-v1	MarcoLYH	2023-06-13T11:51:22Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T11:45:14Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: MarcoLYH/bert-base-uncased-finetuned-v1 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. --> # MarcoLYH/bert-base-uncased-finetuned-v1 This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0187 - Train End Logits Accuracy: 0.6875 - Train Start Logits Accuracy: 0.7083 - Validation Loss: 0.7458 - Validation End Logits Accuracy: 0.75 - Validation Start Logits Accuracy: 0.8000 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.6743 \| 0.4375 \| 0.5625 \| 1.0957 \| 0.7000 \| 0.7000 \| 0 \| \| 1.1601 \| 0.6458 \| 0.6458 \| 0.8086 \| 0.75 \| 0.75 \| 1 \| \| 1.0187 \| 0.6875 \| 0.7083 \| 0.7458 \| 0.75 \| 0.8000 \| 2 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
asenella/mnist_svhn_classifiers	asenella	2023-06-13T11:47:00Z	0	0	null	[ "region:us" ]	null	2023-06-13T11:43:47Z	Classifiers for MNIST and SVHN. Snippet of code on how to load them after downloading the files in `data_path`. ```python import os import torch import torch.nn.functional as F from torch import nn, optim from torch.utils.data import DataLoader from torchvision.datasets import MNIST, SVHN from torchvision.transforms import ToTensor class SVHN_Classifier(nn.Module): def __init__(self): super(SVHN_Classifier, self).__init__() self.conv1 = nn.Conv2d(3, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(500, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 500) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) class MNIST_Classifier(nn.Module): def __init__(self): super(MNIST_Classifier, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def load_mnist_svhn_classifiers(data_path, device="cuda"): c1 = MNIST_Classifier() c1.load_state_dict(torch.load(f"{data_path}/mnist.pt", map_location=device)) c2 = SVHN_Classifier() c2.load_state_dict(torch.load(f"{data_path}/svhn.pt", map_location=device)) return {"mnist": c1.to(device).eval(), "svhn": c2.to(device).eval()} ```
ml-projects/clickbait-ml_model-one	ml-projects	2023-06-13T11:36:59Z	0	0	keras	[ "keras", "tf-keras", "text-classification", "de", "dataset:ml-projects/clickbait-ml_dataset", "arxiv:1910.09700", "license:openrail", "region:us" ]	text-classification	2023-06-13T11:15:45Z	--- license: openrail language: - de metrics: - accuracy library_name: keras pipeline_tag: text-classification datasets: - ml-projects/clickbait-ml_dataset --- # Clickbait-ML Model One <!-- 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]
ml-projects/clickbait-ml_setfit	ml-projects	2023-06-13T11:34:01Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "de", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-06-12T14:21:50Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification language: - de --- # /var/folders/2x/fdpqscbs113ftxcylzlb9sx40000gn/T/tmpjza_ogmp/ml-projects/clickbait-ml-setfit This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("/var/folders/2x/fdpqscbs113ftxcylzlb9sx40000gn/T/tmpjza_ogmp/ml-projects/clickbait-ml-setfit") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
shrinath-suresh/llama-finetune	shrinath-suresh	2023-06-13T11:32:11Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:shrinath-suresh/blogs-docs-splitted", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-11T14:54:56Z	--- datasets: - shrinath-suresh/blogs-docs-splitted ---
Avenger12321/taxi	Avenger12321	2023-06-13T11:30:24Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T11:30:22Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.73 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="Avenger12321/taxi", 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"]) ```
Avenger12321/q-FrozenLake-v1-4x4-noSlippery	Avenger12321	2023-06-13T11:28:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T11:28:03Z	--- 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="Avenger12321/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"]) ```
MarcoLYH/distilbert-base-uncased-finetuned-v4	MarcoLYH	2023-06-13T11:24:35Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T10:43:34Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v4 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. --> # MarcoLYH/distilbert-base-uncased-finetuned-v4 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6362 - Train End Logits Accuracy: 0.8125 - Train Start Logits Accuracy: 0.8333 - Validation Loss: 0.5952 - Validation End Logits Accuracy: 0.9000 - Validation Start Logits Accuracy: 0.8000 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 3, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.8589 \| 0.4792 \| 0.5625 \| 1.3334 \| 0.6500 \| 0.7000 \| 0 \| \| 1.5485 \| 0.5417 \| 0.5625 \| 0.9904 \| 0.6500 \| 0.8000 \| 1 \| \| 1.0423 \| 0.7292 \| 0.7292 \| 0.8812 \| 0.7000 \| 0.8000 \| 2 \| \| 1.1265 \| 0.7083 \| 0.7917 \| 0.7830 \| 0.75 \| 0.8000 \| 3 \| \| 0.8686 \| 0.75 \| 0.7292 \| 0.7052 \| 0.8000 \| 0.8500 \| 4 \| \| 0.6936 \| 0.8542 \| 0.8333 \| 0.6547 \| 0.8000 \| 0.8500 \| 5 \| \| 0.6408 \| 0.875 \| 0.8542 \| 0.6239 \| 0.9000 \| 0.8000 \| 6 \| \| 0.7243 \| 0.8333 \| 0.7083 \| 0.6059 \| 0.9000 \| 0.8000 \| 7 \| \| 0.6146 \| 0.8333 \| 0.8958 \| 0.5976 \| 0.9000 \| 0.8000 \| 8 \| \| 0.6362 \| 0.8125 \| 0.8333 \| 0.5952 \| 0.9000 \| 0.8000 \| 9 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
trumanplus/trumanplus	trumanplus	2023-06-13T11:07:26Z	0	0	allennlp	[ "allennlp", "finance", "Health", "license:openrail", "region:us" ]	null	2023-06-13T10:59:48Z	--- license: openrail library_name: allennlp tags: - finance - Health --- Introducing <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a>, the ultimate solution for better enhancement and energy! Are you ready to take your performance to new heights? Look no further, as Truman Plus is here to revolutionize your experience. With its powerful formula and remarkable benefits, Truman Plus is the go-to choice for those seeking an exceptional boost. Get ready to unlock your true potential! Experience a surge of energy like never before. <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a> provides you with the vitality you need to conquer each day with enthusiasm. Bid farewell to fatigue and embrace a rejuvenated spirit that will keep you going from dawn till dusk. Imagine the endless possibilities that await you when you're armed with boundless energy. <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">OFFICIAL WEBSITE-” CLICK HERE”!</a> But that's not all. <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a> offers remarkable enhancement benefits that will leave you astounded. Discover a newfound confidence as you embrace heightened focus and mental clarity. With Truman Plus, you can break through limitations and achieve peak performance in all aspects of your life. Whether you're tackling a challenging task or aiming for success in the gym, Truman Plus empowers you to exceed your own expectations. What sets <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a> apart is its cutting-edge formula, meticulously crafted to provide you with the best results. Each pill is packed with a potent blend of premium ingredients, scientifically proven to enhance your energy levels, mental agility, and overall performance. Don't settle for mediocre when you can strive for greatness. <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Click Here to Go To the "OFFICIAL Site"!</a> It's time to take control of your life and unlock the best version of yourself. Experience the remarkable benefits of <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a> and elevate your performance to new heights. Don't wait any longer - seize this opportunity to enhance your energy and empower yourself. Upgrade your life with <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Truman Plus</a> today and embark on a journey of limitless possibilities. Place your order now and let Truman Plus be the catalyst that propels you towards success. Take the leap and embrace the extraordinary! <a href="https://www.topofferlink.com/7TZS934/5K6QMGG/">Call to Action: Order Truman Plus now and unleash your true potential!</a> license: openrail ---
nyxabhi/my_awesome_qa_model	nyxabhi	2023-06-13T11:02:44Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-12T08:55:28Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.7171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.7072 \| \| 2.9316 \| 2.0 \| 500 \| 1.8371 \| \| 2.9316 \| 3.0 \| 750 \| 1.7171 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.3
KrishnAI7/autotrain-aniai1-66240136433	KrishnAI7	2023-06-13T10:42:25Z	107	0	transformers	[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "en", "dataset:KrishnAI7/autotrain-data-aniai1", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T10:41:59Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain" datasets: - KrishnAI7/autotrain-data-aniai1 co2_eq_emissions: emissions: 0.0473575460314297 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 66240136433 - CO2 Emissions (in grams): 0.0474 ## Validation Metrics - Loss: 2.632 - Accuracy: 0.100 - Macro F1: 0.013 - Micro F1: 0.100 - Weighted F1: 0.018 - Macro Precision: 0.007 - Micro Precision: 0.100 - Weighted Precision: 0.010 - Macro Recall: 0.071 - Micro Recall: 0.100 - Weighted Recall: 0.100 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/KrishnAI7/autotrain-aniai1-66240136433 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("KrishnAI7/autotrain-aniai1-66240136433", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("KrishnAI7/autotrain-aniai1-66240136433", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
wootwoot/abyssorangemix3-popupparade-fp16	wootwoot	2023-06-13T10:42:14Z	156	2	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-12T14:43:01Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image --- ### Based off [WarriorMama777/OrangeMixs](https://huggingface.co/WarriorMama777/OrangeMixs) All credits go to the original author and all the author of AbyssOrangeMix3's ancestor models ### Merged with [Pop Up Parade](https://civitai.com/models/78997) ### Diffusers The original AbyssOrangeMix3 model converted to be used with the [🧨Diffusers library](https://github.com/huggingface/diffusers)
BlueAvenir/sti_cyber_security_model_updated	BlueAvenir	2023-06-13T10:31:23Z	16	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-06-12T04:13:00Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{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: `torch.utils.data.dataloader.DataLoader` of length 200 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "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": 200, "warmup_steps": 20, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
kevinpro/Vicuna-13B-CoT_v2	kevinpro	2023-06-13T10:25:04Z	0	1	null	[ "en", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	null	2023-06-12T15:35:02Z	--- license: apache-2.0 language: - en --- # Model Card for Model ID SFT to enhance the CoT capabiliy of Vicuna. We tune the model on 55W "CoT Capabiliy" related Instruction Data If you find the model helpful, please click "like" to support us. We also welcome feedback on your usage experience and any issues you encounter in the issues section. ## 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]
facebook/mms-lid-1024	facebook	2023-06-13T10:18:46Z	2,350	9	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-06-13T08:59:15Z	--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 1024 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 1024 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 1024 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` Note: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-1024" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 1024 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - kmr - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kik - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - cak - fao - glg - ltz - xog - lao - mlt - sin - aka - sna - che - mam - ita - quc - aiw - srp - mri - tuv - nno - pus - eus - kbp - gur - ory - lug - crh - bre - luo - nhx - slk - ewe - xsm - fin - rif - dan - saq - yid - yao - mos - quh - hne - xon - new - dtp - quy - est - ddn - dyu - ttq - bam - pse - uig - sck - ngl - tso - mup - dga - seh - lis - wal - ctg - mip - bfz - bxk - ceb - kru - war - khg - bbc - thl - nzi - vmw - mzi - ycl - zne - sid - asa - tpi - bmq - box - zpu - gof - nym - cla - bgq - bfy - hlb - qxl - teo - fon - sda - kfx - bfa - mag - tzh - pil - maj - maa - kdt - ksb - lns - btd - rej - pap - ayr - any - mnk - adx - gud - krc - onb - xal - ctd - nxq - ava - blt - lbw - hyw - udm - zar - tzo - kpv - san - xnj - kek - chv - kcg - kri - ati - bgw - mxt - ybb - btx - dgi - nhy - dnj - zpz - yba - lon - smo - men - ium - mgd - taq - nga - nsu - zaj - tly - prk - zpt - akb - mhr - mxb - nuj - obo - kir - bom - run - zpg - hwc - mnw - ubl - kin - xtm - hnj - mpm - rkt - miy - luc - mih - kne - mib - flr - myv - xmm - knk - iba - gux - pis - zmz - ses - dav - lif - qxr - dig - kdj - wsg - tir - gbm - mai - zpc - kus - nyy - mim - nan - nyn - gog - ngu - tbz - hoc - nyf - sus - guk - gwr - yaz - bcc - sbd - spp - hak - grt - kno - oss - suk - spy - nij - lsm - kaa - bem - rmy - kqn - nim - ztq - nus - bib - xtd - ach - mil - keo - mpg - gjn - zaq - kdh - dug - sah - awa - kff - dip - rim - nhe - pcm - kde - tem - quz - mfq - las - bba - kbr - taj - dyo - zao - lom - shk - dik - dgo - zpo - fij - bgc - xnr - bud - kac - laj - mev - maw - quw - kao - dag - ktb - lhu - zab - mgh - shn - otq - lob - pbb - oci - zyb - bsq - mhi - dzo - zas - guc - alz - ctu - wol - guw - mnb - nia - zaw - mxv - bci - sba - kab - dwr - nnb - ilo - mfe - srx - ruf - srn - zad - xpe - pce - ahk - bcl - myk - haw - mad - ljp - bky - gmv - nag - nav - nyo - kxm - nod - sag - zpl - sas - myx - sgw - old - irk - acf - mak - kfy - zai - mie - zpm - zpi - ote - jam - kpz - lgg - lia - nhi - mzm - bdq - xtn - mey - mjl - sgj - kdi - kxc - miz - adh - tap - hay - kss - pam - gor - heh - nhw - ziw - gej - yua - itv - shi - qvw - mrw - hil - mbt - pag - vmy - lwo - cce - kum - klu - ann - mbb - npl - zca - pww - toc - ace - mio - izz - kam - zaa - krj - bts - eza - zty - hns - kki - min - led - alw - tll - rng - pko - toi - iqw - ncj - toh - umb - mog - hno - wob - gxx - hig - nyu - kby - ban - syl - bxg - nse - xho - zae - mkw - nch - ibg - mas - qvz - bum - bgd - mww - epo - tzm - zul - bcq - lrc - xdy - tyv - ibo - loz - mza - abk - azz - guz - arn - ksw - lus - tos - gvr - top - ckb - mer - pov - lun - rhg - knc - sfw - bev - tum - lag - nso - bho - ndc - maf - gkp - bax - awn - ijc - qug - lub - srr - mni - zza - ige - dje - mkn - bft - tiv - otn - kck - kqs - gle - lua - pdt - swk - mgw - ebu - ada - lic - skr - gaa - mfa - vmk - mcn - bto - lol - bwr - unr - dzg - hdy - kea - bhi - glk - mua - ast - nup - sat - ktu - bhb - zpq - coh - bkm - gya - sgc - dks - ncl - tui - emk - urh - ego - ogo - tsc - idu - igb - ijn - njz - ngb - tod - jra - mrt - zav - tke - its - ady - bzw - kng - kmb - lue - jmx - tsn - bin - ble - gom - ven - sef - sco - her - iso - trp - glv - haq - toq - okr - kha - wof - rmn - sot - kaj - bbj - sou - mjt - trd - gno - mwn - igl - rag - eyo - div - efi - nde - mfv - mix - rki - kjg - fan - khw - wci - bjn - pmy - bqi - ina - hni - mjx - kuj - aoz - the - tog - tet - nuz - ajg - ccp - mau - ymm - fmu - tcz - xmc - nyk - ztg - knx - snk - zac - esg - srb - thq - pht - wes - rah - pnb - ssy - zpv - kpo - phr - atd - eto - xta - mxx - mui - uki - tkt - mgp - xsq - enq - nnh - qxp - zam - bug - bxr - maq - tdt - khb - mrr - kas - zgb - kmw - lir - vah - dar - ssw - hmd - jab - iii - peg - shr - brx - rwr - bmb - kmc - mji - dib - pcc - nbe - mrd - ish - kai - yom - zyn - hea - ewo - bas - hms - twh - kfq - thr - xtl - wbr - bfb - wtm - mjc - blk - lot - dhd - swv - wbm - zzj - kge - mgm - niq - zpj - bwx - bde - mtr - gju - kjp - mbz - haz - lpo - yig - qud - shy - gjk - ztp - nbl - aii - kun - say - mde - sjp - bns - brh - ywq - msi - anr - mrg - mjg - tan - tsg - tcy - kbl - mdr - mks - noe - tyz - zpa - ahr - aar - wuu - khr - kbd - kex - bca - nku - pwr - hsn - ort - ott - swi - kua - tdd - msm - bgp - nbm - mxy - abs - zlj - ebo - lea - dub - sce - xkb - vav - bra - ssb - sss - nhp - kad - kvx - lch - tts - zyj - kxp - lmn - qvi - lez - scl - cqd - ayb - xbr - nqg - dcc - cjk - bfr - zyg - mse - gru - mdv - bew - wti - arg - dso - zdj - pll - mig - qxs - bol - drs - anp - chw - bej - vmc - otx - xty - bjj - vmz - ibb - gby - twx - tig - thz - tku - hmz - pbm - mfn - nut - cyo - mjw - cjm - tlp - naq - rnd - stj - sym - jax - btg - tdg - sng - nlv - kvr - pch - fvr - mxs - wni - mlq - kfr - mdj - osi - nhn - ukw - tji - qvj - nih - bcy - hbb - zpx - hoj - cpx - ogc - cdo - bgn - bfs - vmx - tvn - ior - mxa - btm - anc - jit - mfb - mls - ets - goa - bet - ikw - pem - trf - daq - max - rad - njo - bnx - mxl - mbi - nba - zpn - zts - mut - hnd - mta - hav - hac - ryu - abr - yer - cld - zag - ndo - sop - vmm - gcf - chr - cbk - sbk - bhp - odk - mbd - nap - gbr - mii - czh - xti - vls - gdx - sxw - zaf - wem - mqh - ank - yaf - vmp - otm - sdh - anw - src - mne - wss - meh - kzc - tma - ttj - ots - ilp - zpr - saz - ogb - akl - nhg - pbv - rcf - cgg - mku - bez - mwe - mtb - gul - ifm - mdh - scn - lki - xmf - sgd - aba - cos - luz - zpy - stv - kjt - mbf - kmz - nds - mtq - tkq - aee - knn - mbs - mnp - ema - bar - unx - plk - psi - mzn - cja - sro - mdw - ndh - vmj - zpw - kfu - bgx - gsw - fry - zpe - zpd - bta - psh - zat </details> ## Model details - Developed by: Vineel Pratap et al. - Model type: Multi-Lingual Automatic Speech Recognition model - Language(s): 1024 languages, see [supported languages](#supported-languages) - License: CC-BY-NC 4.0 license - Num parameters: 1 billion - Audio sampling rate: 16,000 kHz - Cite as: @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other MMS checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)
DuhAlbared/autotrain-dru-textclassification-66230136430	DuhAlbared	2023-06-13T10:16:24Z	110	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "autotrain", "ar", "dataset:DuhAlbared/autotrain-data-dru-textclassification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T10:13:57Z	--- tags: - autotrain - text-classification language: - ar widget: - text: "I love AutoTrain" datasets: - DuhAlbared/autotrain-data-dru-textclassification co2_eq_emissions: emissions: 1.2953982175499286 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 66230136430 - CO2 Emissions (in grams): 1.2954 ## Validation Metrics - Loss: 0.296 - Accuracy: 0.916 - Macro F1: 0.916 - Micro F1: 0.916 - Weighted F1: 0.916 - Macro Precision: 0.917 - Micro Precision: 0.916 - Weighted Precision: 0.917 - Macro Recall: 0.916 - Micro Recall: 0.916 - Weighted Recall: 0.916 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/DuhAlbared/autotrain-dru-textclassification-66230136430 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("DuhAlbared/autotrain-dru-textclassification-66230136430", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("DuhAlbared/autotrain-dru-textclassification-66230136430", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
facebook/mms-lid-512	facebook	2023-06-13T10:16:19Z	216	2	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-06-13T08:59:08Z	--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 512 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 512 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 512 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` Note: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-512" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 512 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - kmr - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kik - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - cak - fao - glg - ltz - xog - lao - mlt - sin - aka - sna - che - mam - ita - quc - srp - mri - tuv - nno - pus - eus - kbp - ory - lug - bre - luo - nhx - slk - ewe - fin - rif - dan - yid - yao - mos - quh - hne - xon - new - quy - est - dyu - ttq - bam - pse - uig - sck - ngl - tso - mup - dga - seh - lis - wal - ctg - bfz - bxk - ceb - kru - war - khg - bbc - thl - vmw - zne - sid - tpi - nym - bgq - bfy - hlb - teo - fon - kfx - bfa - mag - ayr - any - mnk - adx - ava - hyw - san - kek - chv - kri - btx - nhy - dnj - lon - men - ium - nga - nsu - prk - kir - bom - run - hwc - mnw - ubl - kin - rkt - xmm - iba - gux - ses - wsg - tir - gbm - mai - nyy - nan - nyn - gog - ngu - hoc - nyf - sus - bcc - hak - grt - suk - nij - kaa - bem - rmy - nus - ach - awa - dip - rim - nhe - pcm - kde - tem - quz - bba - kbr - taj - dik - dgo - bgc - xnr - kac - laj - dag - ktb - mgh - shn - oci - zyb - alz - wol - guw - nia - bci - sba - kab - nnb - ilo - mfe - xpe - bcl - haw - mad - ljp - gmv - nyo - kxm - nod - sag - sas - myx - sgw - mak - kfy - jam - lgg - nhi - mey - sgj - hay - pam - heh - nhw - yua - shi - mrw - hil - pag - cce - npl - ace - kam - min - pko - toi - ncj - umb - hno - ban - syl - bxg - nse - xho - mkw - nch - mas - bum - mww - epo - tzm - zul - lrc - ibo - abk - azz - guz - ksw - lus - ckb - mer - pov - rhg - knc - tum - nso - bho - ndc - ijc - qug - lub - srr - mni - zza - dje - tiv - gle - lua - swk - ada - lic - skr - mfa - bto - unr - hdy - kea - glk - ast - nup - sat - ktu - bhb - sgc - dks - ncl - emk - urh - tsc - idu - igb - its - kng - kmb - tsn - bin - gom - ven - sef - sco - trp - glv - haq - kha - rmn - sot - sou - gno - igl - efi - nde - rki - kjg - fan - wci - bjn - pmy - bqi - ina - hni - the - nuz - ajg - ymm - fmu - nyk - snk - esg - thq - pht - wes - pnb - phr - mui - tkt - bug - mrr - kas - zgb - lir - vah - ssw - iii - brx - rwr - kmc - dib - pcc - zyn - hea - hms - thr - wbr - bfb - wtm - blk - dhd - swv - zzj - niq - mtr - gju - kjp - haz - shy - nbl - aii - sjp - bns - brh - msi - tsg - tcy - kbl - noe - tyz - ahr - aar - wuu - kbd - bca - pwr - hsn - kua - tdd - bgp - abs - zlj - ebo - bra - nhp - tts - zyj - lmn - cqd - dcc - cjk - bfr - bew - arg - drs - chw - bej - bjj - ibb - tig - nut - jax - tdg - nlv - pch - fvr - mlq - kfr - nhn - tji - hoj - cpx - cdo - bgn - btm - trf - daq - max - nba - mut - hnd - ryu - abr - sop - odk - nap - gbr - czh - vls - gdx - yaf - sdh - anw - ttj - nhg - cgg - ifm - mdh - scn - lki - luz - stv - kmz - nds - mtq - knn - mnp - bar - mzn - gsw - fry </details> ## Model details - Developed by: Vineel Pratap et al. - Model type: Multi-Lingual Automatic Speech Recognition model - Language(s): 512 languages, see [supported languages](#supported-languages) - License: CC-BY-NC 4.0 license - Num parameters: 1 billion - Audio sampling rate: 16,000 kHz - Cite as: @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other MMS checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)
facebook/mms-lid-126	facebook	2023-06-13T10:15:48Z	3,043	26	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-06-13T08:44:05Z	--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 126 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 126 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 126 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` Note: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-126" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 126 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - fao - glg - ltz - lao - mlt - sin - sna - ita - srp - mri - nno - pus - eus - ory - lug - bre - luo - slk - fin - dan - yid - est - ceb - war - san - kir - oci - wol - haw - kam - umb - xho - epo - zul - ibo - abk - ckb - nso - gle - kea - ast - sco - glv - ina </details> ## Model details - Developed by: Vineel Pratap et al. - Model type: Multi-Lingual Automatic Speech Recognition model - Language(s): 126 languages, see [supported languages](#supported-languages) - License: CC-BY-NC 4.0 license - Num parameters: 1 billion - Audio sampling rate: 16,000 kHz - Cite as: @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other MMS checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)
hasu234/image_classifier	hasu234	2023-06-13T10:15:18Z	0	0	null	[ "image-classification", "en", "license:apache-2.0", "region:us" ]	image-classification	2023-06-13T09:50:12Z	--- license: apache-2.0 language: - en pipeline_tag: image-classification --- This is the model for [https://github.com/hasu234/SDPDSSample](https://github.com/hasu234/SDPDSSample). this repository. This classifier can classify between ```dog```, ```berry```, ```flower``` and ```bird``` images. ![Sample Dataset](images/dataset.png) ## Dependencies * Python 3.9 ## Setting up conda Environment * Clone the repository by running ``` git clone https://github.com/hasu234/SDPDSSample.git ``` * Change current directory to SDPDSSample ``` cd SDPDSSample ``` * Create a conda environmet ``` conda create -n myenv python=3.9 ``` * Activate the environment ``` conda activate myenv ``` * Install the required library from ```requirment.txt``` by running ``` pip install -r requirmen.txt ``` * Or, create a conda environment from ```environment.yml``` by running ``` conda env create -f environment.yml ``` ## Trining your data * To train your own dataset Make sure you have a data folder having the same folder hiararchy like below ``` ├── dataset \| ├── train │ │ ├── class1 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class2 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class3 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class4 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg \| ├── test │ │ ├── class1 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class2 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class3 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class4 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg ``` or make some chainges on ```train.py``` according to to your dataset directory. * Make sure you are in the project directory and run the ```train.py``` script with the folder directory of your dataset ``` python train.py /path/to/dataset_directory ``` ## Running inference * To run the inference on your test data make sure you downloaded the pretrained model from [this link](https://drive.google.com/uc?id=197Kuuo4LhHunYLgGKfGeouNTL0WguP0T&export=download). * Then run the ```infer.py``` script from terminal specifying the test image location and downloaded pretrained model location ``` python infer.py path/to/image.jpg path/to/model.pth ``` ## Running on Docker * Clone the repository by running ``` git clone https://github.com/hasu234/SDPDSSample.git ``` * Change current directory to SDPDSSample ``` cd SDPDSSample ``` Before Building the docker image transfer the files (model, test image, dataset) to current working directory if you don't want to deal with docker volume * Build the Docker image by running ``` docker build -t sdpdsample . ``` * Run the docker image ``` docker run -d sdpdsample ``` if the container failed to run in background, run it on foreground using ```docker run -it sdpdsample``` then exit to get the running container id * Get the container id ``` docker ps ``` * Getting inside the container ``` docker exec -it <container id> bash ``` You will get a Linux like command-line interface * Running the project ``` # for training your data python train.py /path/to/dataset_directory # for running inference python infer.py path/to/image.jpg path/to/model.pth ```
MarcoLYH/distilbert-base-uncased-finetuned-v2	MarcoLYH	2023-06-13T09:53:44Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T09:47:30Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v2 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. --> # MarcoLYH/distilbert-base-uncased-finetuned-v2 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8512 - Train End Logits Accuracy: 0.7917 - Train Start Logits Accuracy: 0.7708 - Validation Loss: 0.9185 - Validation End Logits Accuracy: 0.7000 - Validation Start Logits Accuracy: 0.8000 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 2.0417 \| 0.4583 \| 0.5833 \| 1.2444 \| 0.6000 \| 0.7000 \| 0 \| \| 1.5102 \| 0.5625 \| 0.6875 \| 1.0279 \| 0.7000 \| 0.75 \| 1 \| \| 1.1881 \| 0.6458 \| 0.6875 \| 0.9774 \| 0.7000 \| 0.8000 \| 2 \| \| 1.1344 \| 0.6875 \| 0.6875 \| 0.9360 \| 0.7000 \| 0.8000 \| 3 \| \| 0.8512 \| 0.7917 \| 0.7708 \| 0.9185 \| 0.7000 \| 0.8000 \| 4 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
paumena/QA-DistilBERT	paumena	2023-06-13T09:52:16Z	61	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T08:07:51Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: paumena/QA-DistilBERT 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. --> # paumena/QA-DistilBERT This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4747 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Evaluation metrics {'exact_match': 76.27246925260171, 'f1': 84.99301423719345} ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.4947 \| 0 \| \| 0.9554 \| 1 \| \| 0.7266 \| 2 \| \| 0.5744 \| 3 \| \| 0.4747 \| 4 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
MarcoLYH/distilbert-base-uncased-finetuned-v1	MarcoLYH	2023-06-13T09:39:02Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T09:35:58Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v1 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. --> # MarcoLYH/distilbert-base-uncased-finetuned-v1 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4309 - Train End Logits Accuracy: 0.625 - Train Start Logits Accuracy: 0.6042 - Validation Loss: 1.1252 - Validation End Logits Accuracy: 0.7000 - Validation Start Logits Accuracy: 0.75 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.8703 \| 0.4792 \| 0.5625 \| 1.3666 \| 0.6500 \| 0.75 \| 0 \| \| 1.2717 \| 0.6875 \| 0.6875 \| 1.1863 \| 0.7000 \| 0.75 \| 1 \| \| 1.4309 \| 0.625 \| 0.6042 \| 1.1252 \| 0.7000 \| 0.75 \| 2 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
slone/mbart-large-51-myv-mul-v1	slone	2023-06-13T09:38:10Z	134	1	transformers	[ "transformers", "pytorch", "safetensors", "mbart", "text2text-generation", "erzya", "mordovian", "translation", "myv", "ru", "fi", "de", "es", "en", "hi", "zh", "tr", "uk", "fr", "ar", "dataset:slone/myv_ru_2022", "dataset:yhavinga/ccmatrix", "arxiv:2209.09368", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-09-15T05:55:21Z	--- language: - myv - ru - fi - de - es - en - hi - zh - tr - uk - fr - ar tags: - erzya - mordovian - translation license: cc-by-sa-4.0 datasets: - slone/myv_ru_2022 - yhavinga/ccmatrix --- This a model to translate texts to the Erzya language (`myv`, cyrillic script) from 11 other languages: `ru,fi,de,es,en,hi,zh,tr,uk,fr,ar`. See its [demo](https://huggingface.co/spaces/slone/myv-translation-2022-demo)! It is described in the paper [The first neural machine translation system for the Erzya language](https://arxiv.org/abs/2209.09368). This model is based on [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50), but with updated vocabulary and checkpoint: - Added an extra language token `myv_XX` and 19K new BPE tokens for the Erzya language; - Fine-tuned to translate from Erzya: first to Russian, then to all 11 languages. The following code can be used to run translation using the model ```Python from transformers import MBartForConditionalGeneration, MBart50Tokenizer def fix_tokenizer(tokenizer): """ Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """ old_len = len(tokenizer) - int('myv_XX' in tokenizer.added_tokens_encoder) tokenizer.lang_code_to_id['myv_XX'] = old_len-1 tokenizer.id_to_lang_code[old_len-1] = 'myv_XX' tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id) tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()} if 'myv_XX' not in tokenizer._additional_special_tokens: tokenizer._additional_special_tokens.append('myv_XX') tokenizer.added_tokens_encoder = {} def translate(text, model, tokenizer, src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False, n_out=None, *kwargs): tokenizer.src_lang = src encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024) if max_length == 'auto': max_length = int(32 + 1.5 encoded.input_ids.shape[1]) if train_mode: model.train() else: model.eval() generated_tokens = model.generate( encoded.to(model.device), forced_bos_token_id=tokenizer.lang_code_to_id[trg], max_length=max_length, num_beams=num_beams, repetition_penalty=repetition_penalty, num_return_sequences=n_out or 1, kwargs ) out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) if isinstance(text, str) and n_out is None: return out[0] return out mname = 'slone/mbart-large-51-myv-mul-v1' model = MBartForConditionalGeneration.from_pretrained(mname) tokenizer = MBart50Tokenizer.from_pretrained(mname) fix_tokenizer(tokenizer) print(translate('Шумбрат, киска!', model, tokenizer, src='myv_XX', trg='ru_RU')) # Привет, собака! # действительно, "киска" с эрзянского переводится именно так print(translate('Шумбрат, киска!', model, tokenizer, src='myv_XX', trg='en_XX')) # Hi, dog! ```
yswill/LLaVA-13b-delta-v1-1	yswill	2023-06-13T09:36:03Z	12	0	transformers	[ "transformers", "pytorch", "llava", "text-generation", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2023-06-13T09:18:24Z	--- license: apache-2.0 inference: false --- NOTE: This "delta model" cannot be used directly. Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights. See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. <br> <br> # LLaVA Model Card ## Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Model date: LLaVA was trained in May 2023. Paper or resources for more information: https://llava-vl.github.io/ License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues ## Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset 595K filtered image-text pairs from CC3M. 150K GPT-generated multimodal instruction-following data. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.
OpenDILabCommunity/LunarLander-v2-PPOOffPolicy	OpenDILabCommunity	2023-06-13T09:35:16Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "LunarLander-v2", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-06-13T09:35:07Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - LunarLander-v2 benchmark_name: OpenAI/Gym/Box2d task_name: LunarLander-v2 pipeline_tag: reinforcement-learning model-index: - name: PPOOffPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Box2d-LunarLander-v2 type: OpenAI/Gym/Box2d-LunarLander-v2 metrics: - type: mean_reward value: 240.63 +/- 20.68 name: mean_reward --- # Play LunarLander-v2 with PPOOffPolicy Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple PPOOffPolicy implementation to OpenAI/Gym/Box2d LunarLander-v2 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import PPOOffPolicyAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py")) # Instantiate the agent agent = PPOOffPolicyAgent( env="lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import PPOOffPolicyAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/LunarLander-v2-PPOOffPolicy") # Instantiate the agent agent = PPOOffPolicyAgent( env="lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import PPOOffPolicyAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = PPOOffPolicyAgent("lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy") # Train the agent return_ = agent.train(step=int(4000000), collector_env_num=4, evaluator_env_num=4) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Box2d", task_name="LunarLander-v2", algo_name="PPOOffPolicy", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/ppo.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./ppo_offpolicy/lunarlander_ppo_offpolicy_deploy.py", usage_file_by_huggingface_ding="./ppo_offpolicy/lunarlander_ppo_offpolicy_download.py", train_file="./ppo_offpolicy/lunarlander_ppo_offpolicy.py", repo_id="OpenDILabCommunity/LunarLander-v2-PPOOffPolicy" ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 240, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'LunarLander-v2' }, 'policy': { 'model': { 'obs_shape': 8, 'action_shape': 4 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 4, 'batch_size': 64, 'learning_rate': 0.001, 'value_weight': 0.5, 'entropy_weight': 0.01, 'clip_ratio': 0.2, 'adv_norm': True, 'ignore_done': False, 'nstep': 1, 'nstep_return': False }, 'collect': { 'collector': {}, 'unroll_len': 1, 'discount_factor': 0.99, 'gae_lambda': 0.95, 'n_sample': 128 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 240, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 10000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'ppo', 'priority': False, 'priority_IS_weight': False, 'nstep_return': False, 'nstep': 3, 'transition_with_policy_data': True, 'cfg_type': 'PPOOffPolicyDict' }, 'exp_name': 'LunarLander-v2-PPOOffPolicy', 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False }, 'seed': 0 } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/LunarLander-v2-PPOOffPolicy) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/ppo.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-PPOOffPolicy/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-PPOOffPolicy/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 371.29 KB - Last Update Date: 2023-06-13 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Box2d - Task: LunarLander-v2 - Gym version: 0.25.1 - DI-engine version: v0.4.8 - PyTorch version: 1.7.1 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html)
Josias-Ounsinli/my_awesome_model32	Josias-Ounsinli	2023-06-13T09:33:36Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T09:14:34Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model32 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. --> # Josias-Ounsinli/my_awesome_model32 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0986 - Validation Loss: 1.0986 - Train Accuracy: 0.3333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.0986 \| 1.0986 \| 0.3333 \| 0 \| \| 1.0986 \| 1.0986 \| 0.3333 \| 1 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
kejolong/SNI2017	kejolong	2023-06-13T09:26:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T09:22:53Z	--- license: creativeml-openrail-m ---
Nayan924/carmine	Nayan924	2023-06-13T09:23:58Z	0	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-06-13T09:23:58Z	--- license: cc-by-nc-nd-4.0 ---
Jamli/AmeliaLoRa	Jamli	2023-06-13T09:15:22Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T09:07:01Z	--- license: creativeml-openrail-m ---
addy88/bert-finetuned-bpmn	addy88	2023-06-13T09:15:21Z	120	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-13T09:06:45Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-bpmn 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-finetuned-bpmn 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: 0.3456 - Precision: 0.8113 - Recall: 0.86 - F1: 0.8350 - Accuracy: 0.9341 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.2716 \| 0.7778 \| 0.84 \| 0.8077 \| 0.9115 \| \| No log \| 2.0 \| 20 \| 0.2428 \| 0.7669 \| 0.8333 \| 0.7987 \| 0.9160 \| \| No log \| 3.0 \| 30 \| 0.2726 \| 0.7875 \| 0.84 \| 0.8129 \| 0.9205 \| \| No log \| 4.0 \| 40 \| 0.2658 \| 0.7862 \| 0.8333 \| 0.8091 \| 0.9214 \| \| No log \| 5.0 \| 50 \| 0.2470 \| 0.7914 \| 0.86 \| 0.8243 \| 0.9268 \| \| No log \| 6.0 \| 60 \| 0.2745 \| 0.7791 \| 0.8467 \| 0.8115 \| 0.9250 \| \| No log \| 7.0 \| 70 \| 0.3415 \| 0.8280 \| 0.8667 \| 0.8469 \| 0.9259 \| \| No log \| 8.0 \| 80 \| 0.3524 \| 0.775 \| 0.8267 \| 0.8000 \| 0.9178 \| \| No log \| 9.0 \| 90 \| 0.3307 \| 0.8313 \| 0.8867 \| 0.8581 \| 0.9322 \| \| No log \| 10.0 \| 100 \| 0.3161 \| 0.7778 \| 0.84 \| 0.8077 \| 0.9214 \| \| No log \| 11.0 \| 110 \| 0.3646 \| 0.8387 \| 0.8667 \| 0.8525 \| 0.9322 \| \| No log \| 12.0 \| 120 \| 0.3262 \| 0.7925 \| 0.84 \| 0.8155 \| 0.9223 \| \| No log \| 13.0 \| 130 \| 0.3436 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9350 \| \| No log \| 14.0 \| 140 \| 0.3427 \| 0.8516 \| 0.88 \| 0.8656 \| 0.9377 \| \| No log \| 15.0 \| 150 \| 0.3163 \| 0.7950 \| 0.8533 \| 0.8232 \| 0.9322 \| \| No log \| 16.0 \| 160 \| 0.3233 \| 0.8291 \| 0.8733 \| 0.8506 \| 0.9377 \| \| No log \| 17.0 \| 170 \| 0.3354 \| 0.8050 \| 0.8533 \| 0.8285 \| 0.9322 \| \| No log \| 18.0 \| 180 \| 0.3468 \| 0.8291 \| 0.8733 \| 0.8506 \| 0.9341 \| \| No log \| 19.0 \| 190 \| 0.3457 \| 0.8176 \| 0.8667 \| 0.8414 \| 0.9341 \| \| No log \| 20.0 \| 200 \| 0.3456 \| 0.8113 \| 0.86 \| 0.8350 \| 0.9341 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
franfj/media-bias-ukraine-dataset-all-removed	franfj	2023-06-13T09:08:29Z	115	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-11T23:45:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: media-bias-ukraine-dataset-all-removed 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. --> # media-bias-ukraine-dataset-all-removed 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: 0.1717 - F1: 0.8014 ## 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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3845 \| 1.0 \| 114 \| 0.2296 \| 0.6101 \| \| 0.1412 \| 2.0 \| 228 \| 0.1759 \| 0.7486 \| \| 0.0215 \| 3.0 \| 342 \| 0.2275 \| 0.7439 \| \| 0.0506 \| 4.0 \| 456 \| 0.2064 \| 0.7651 \| \| 0.0366 \| 5.0 \| 570 \| 0.1717 \| 0.8014 \| \| 0.2428 \| 6.0 \| 684 \| 0.1955 \| 0.7878 \| \| 0.005 \| 7.0 \| 798 \| 0.2297 \| 0.7839 \| \| 0.003 \| 8.0 \| 912 \| 0.2428 \| 0.8005 \| \| 0.0037 \| 9.0 \| 1026 \| 0.2577 \| 0.7884 \| \| 0.0099 \| 10.0 \| 1140 \| 0.2641 \| 0.7957 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2
Josias-Ounsinli/my_awesome_model39	Josias-Ounsinli	2023-06-13T08:53:12Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T08:33:51Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model39 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. --> # Josias-Ounsinli/my_awesome_model39 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5308 - Validation Loss: 0.6272 - Train Accuracy: 0.7307 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 7500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6912 \| 0.6544 \| 0.7063 \| 0 \| \| 0.5308 \| 0.6272 \| 0.7307 \| 1 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
ThirdEyeData/Question_Answer	ThirdEyeData	2023-06-13T08:49:37Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T08:39:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: Question_Answer 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. --> # Question_Answer This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 5.2904 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 10 \| 5.6647 \| \| No log \| 2.0 \| 20 \| 5.4060 \| \| No log \| 3.0 \| 30 \| 5.2904 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cpu - Datasets 2.12.0 - Tokenizers 0.13.3
srb1smo/lizard	srb1smo	2023-06-13T08:44:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T08:44:41Z	--- license: creativeml-openrail-m ---
Josias-Ounsinli/my_awesome_model30	Josias-Ounsinli	2023-06-13T08:39:36Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T08:00:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model30 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. --> # Josias-Ounsinli/my_awesome_model30 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6122 - Validation Loss: 0.6266 - Train Accuracy: 0.7293 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 3750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.7425 \| 0.6462 \| 0.7127 \| 0 \| \| 0.6122 \| 0.6266 \| 0.7293 \| 1 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
Josias-Ounsinli/my_awesome_model2	Josias-Ounsinli	2023-06-13T08:37:32Z	63	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-12T10:29:57Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model2 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. --> # Josias-Ounsinli/my_awesome_model2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Validation Loss: nan - Train Accuracy: 0.3333 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| nan \| nan \| 0.3333 \| 0 \| \| nan \| nan \| 0.3333 \| 1 \| \| nan \| nan \| 0.3333 \| 2 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
calculus7e/distilbert-base-uncased-finetuned-emotion	calculus7e	2023-06-13T08:33:42Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T08:11:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9248071018161593 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2155 - Accuracy: 0.9245 - F1: 0.9248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8359 \| 1.0 \| 250 \| 0.3054 \| 0.906 \| 0.9044 \| \| 0.2439 \| 2.0 \| 500 \| 0.2155 \| 0.9245 \| 0.9248 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Josias-Ounsinli/my_awesome_model40	Josias-Ounsinli	2023-06-13T08:29:15Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T07:58:55Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model40 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. --> # Josias-Ounsinli/my_awesome_model40 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5230 - Validation Loss: 0.6182 - Train Accuracy: 0.728 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5625, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.7533 \| 0.6294 \| 0.727 \| 0 \| \| 0.5619 \| 0.6182 \| 0.728 \| 1 \| \| 0.5230 \| 0.6182 \| 0.728 \| 2 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
undrwolf/taxi-RL-agent	undrwolf	2023-06-13T08:24:09Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T08:24:06Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-RL-agent 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="undrwolf/taxi-RL-agent", 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"]) ```
undrwolf/q-FrozenLake-v1-4x4-noSlippery	undrwolf	2023-06-13T08:18:29Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T08:18:26Z	--- 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="undrwolf/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"]) ```
ikasou/ppo-LunarLander-v2	ikasou	2023-06-13T08:17:07Z	8	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-31T16:53:23Z	--- 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: 292.00 +/- 15.21 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 ... ```
ThirdEyeData/Text_Classification	ThirdEyeData	2023-06-13T08:11:23Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T07:55:44Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: text_classification_v1 results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train[:1%] args: plain_text metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # text_classification_v1 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: 0.0198 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 16 \| 0.0445 \| 1.0 \| \| No log \| 2.0 \| 32 \| 0.0198 \| 1.0 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cpu - Datasets 2.12.0 - Tokenizers 0.13.3
TGiang/finetuning_t5	TGiang	2023-06-13T08:09:21Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-12T17:27:16Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: finetuning_t5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning_t5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3628 \| 1.0 \| 444 \| 0.2424 \| \| 0.2569 \| 2.0 \| 889 \| 0.2220 \| \| 0.3852 \| 3.0 \| 1333 \| 0.2854 \| \| 0.4332 \| 4.0 \| 1778 \| 0.2795 \| \| 0.4322 \| 5.0 \| 2222 \| 0.2794 \| \| 0.4276 \| 6.0 \| 2667 \| 0.2794 \| \| 0.4275 \| 7.0 \| 3111 \| 0.2794 \| \| 0.4269 \| 8.0 \| 3556 \| 0.2795 \| \| 0.429 \| 9.0 \| 4001 \| 0.2795 \| \| 0.4308 \| 9.99 \| 4440 \| 0.2795 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
mamun4105/dqn-SpaceInvadersNoFrameskip-v4	mamun4105	2023-06-13T07:31:22Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T06:10:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 640.00 +/- 199.71 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mamun4105 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mamun4105 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mamun4105 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
casque/MuscleGirl_v1	casque	2023-06-13T06:59:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T06:57:46Z	--- license: creativeml-openrail-m ---
ugshanyu/hello_world	ugshanyu	2023-06-13T06:40:17Z	168	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-13T04:34:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: hello_world results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: mn split: test args: mn metrics: - name: Wer type: wer value: 0.4679207811551829 --- <!-- 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. --> # hello_world This model is a fine-tuned version of [tugstugi/wav2vec2-large-xlsr-53-mongolian](https://huggingface.co/tugstugi/wav2vec2-large-xlsr-53-mongolian) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8235 - Wer: 0.4679 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.1725 \| 6.78 \| 400 \| 0.8343 \| 0.5449 \| \| 0.1406 \| 13.56 \| 800 \| 0.8587 \| 0.5158 \| \| 0.1013 \| 20.34 \| 1200 \| 0.8260 \| 0.4990 \| \| 0.0701 \| 27.12 \| 1600 \| 0.8235 \| 0.4679 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
pilgrim222/Taxi_v3_Qlearn	pilgrim222	2023-06-13T06:39:21Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T06:39:14Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_v3_Qlearn 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="pilgrim222/Taxi_v3_Qlearn", 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"]) ```
addy88/distilroberta-base	addy88	2023-06-13T06:39:09Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-12T09:16:21Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilroberta-base 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. --> # distilroberta-base This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6935 - Precision: 0.7556 - Recall: 0.7556 - F1: 0.7556 - Accuracy: 0.7556 ## 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 - distributed_type: multi-GPU - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2481 \| 1.0 \| 2355 \| 1.5506 \| 0.7409 \| 0.7409 \| 0.7409 \| 0.7409 \| \| 0.3473 \| 2.0 \| 4710 \| 1.5572 \| 0.7428 \| 0.7428 \| 0.7428 \| 0.7428 \| \| 0.2614 \| 3.0 \| 7065 \| 1.6423 \| 0.7539 \| 0.7539 \| 0.7539 \| 0.7539 \| \| 0.1337 \| 4.0 \| 9420 \| 1.6935 \| 0.7556 \| 0.7556 \| 0.7556 \| 0.7556 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
pilgrim222/q-FrozenLake-v1-4x4-noSlippery	pilgrim222	2023-06-13T06:24:44Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T06:24:42Z	--- 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="pilgrim222/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"]) ```
casque/Angewomon-Digimon-v1	casque	2023-06-13T06:21:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T06:19:51Z	--- license: creativeml-openrail-m ---
Catears/AnythingVAEStorage	Catears	2023-06-13T06:17:58Z	0	0	diffusers	[ "diffusers", "license:unknown", "region:us" ]	null	2023-06-13T06:09:45Z	--- license: unknown --- ## This is just a direct copy of AnythingV4 vae. I want to load it manually in diffusers to avoid loading failure, but cannot do it using "from_pretrained" directly. That's why I create this model card to resolve the issue
LemonFace0309/Reinforce-Pixelcopter-PLE-v0	LemonFace0309	2023-06-13T06:07:08Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T06:06:42Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 8.20 +/- 7.98 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
rashxyz/ariv2	rashxyz	2023-06-13T05:55:40Z	29	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-13T05:50:08Z	--- license: openrail tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: \|- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model ---
ppsingh/action-policy-plans-classifier	ppsingh	2023-06-13T05:50:19Z	110	0	transformers	[ "transformers", "pytorch", "mpnet", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T05:49:22Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: action-policy-plans-classifier 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. --> # action-policy-plans-classifier This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6839 - Precision Micro: 0.7089 - Precision Weighted: 0.7043 - Precision Samples: 0.4047 - Recall Micro: 0.7066 - Recall Weighted: 0.7066 - Recall Samples: 0.4047 - F1-score: 0.4041 ## 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: 2.915e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision Micro \| Precision Weighted \| Precision Samples \| Recall Micro \| Recall Weighted \| Recall Samples \| F1-score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------------:\|:------------------:\|:-----------------:\|:------------:\|:---------------:\|:--------------:\|:--------:\| \| 0.7333 \| 1.0 \| 253 \| 0.5828 \| 0.625 \| 0.6422 \| 0.4047 \| 0.7098 \| 0.7098 \| 0.4065 \| 0.4047 \| \| 0.5905 \| 2.0 \| 506 \| 0.5593 \| 0.6292 \| 0.6318 \| 0.4437 \| 0.7760 \| 0.7760 \| 0.4446 \| 0.4434 \| \| 0.4934 \| 3.0 \| 759 \| 0.5269 \| 0.6630 \| 0.6637 \| 0.4319 \| 0.7571 \| 0.7571 \| 0.4347 \| 0.4325 \| \| 0.4018 \| 4.0 \| 1012 \| 0.5645 \| 0.6449 \| 0.6479 \| 0.4456 \| 0.7792 \| 0.7792 \| 0.4465 \| 0.4453 \| \| 0.3235 \| 5.0 \| 1265 \| 0.6101 \| 0.6964 \| 0.6929 \| 0.4220 \| 0.7382 \| 0.7382 \| 0.4229 \| 0.4217 \| \| 0.2638 \| 6.0 \| 1518 \| 0.6692 \| 0.6888 \| 0.6841 \| 0.4111 \| 0.7192 \| 0.7192 \| 0.4120 \| 0.4108 \| \| 0.2197 \| 7.0 \| 1771 \| 0.6839 \| 0.7089 \| 0.7043 \| 0.4047 \| 0.7066 \| 0.7066 \| 0.4047 \| 0.4041 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
gokuls/bert_12_layer_model_v1_complete_training_new_48_KD	gokuls	2023-06-13T05:49:08Z	50	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-10T03:43:10Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v1_complete_training_new_48_KD 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_12_layer_model_v1_complete_training_new_48_KD This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 326.4413 - Accuracy: 0.3018 ## 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: 36 - eval_batch_size: 36 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 849.2694 \| 0.06 \| 10000 \| 802.2138 \| 0.1435 \| \| 603.4255 \| 0.12 \| 20000 \| 597.5114 \| 0.1445 \| \| 552.5588 \| 0.18 \| 30000 \| 549.1310 \| 0.1454 \| \| 525.5738 \| 0.25 \| 40000 \| 523.0781 \| 0.1460 \| \| 508.5192 \| 0.31 \| 50000 \| 507.5772 \| 0.1463 \| \| 496.0482 \| 0.37 \| 60000 \| 494.5385 \| 0.1457 \| \| 487.2105 \| 0.43 \| 70000 \| 484.7273 \| 0.1464 \| \| 476.1281 \| 0.49 \| 80000 \| 473.3444 \| 0.1490 \| \| 456.0017 \| 0.55 \| 90000 \| 445.0464 \| 0.1662 \| \| 421.6633 \| 0.61 \| 100000 \| 404.1071 \| 0.2046 \| \| 382.6604 \| 0.68 \| 110000 \| 369.2148 \| 0.2446 \| \| 358.6727 \| 0.74 \| 120000 \| 341.1114 \| 0.2776 \| \| 339.9395 \| 0.8 \| 130000 \| 326.4413 \| 0.3018 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.12.0 - Tokenizers 0.13.3
Deojaklah/Memeyy	Deojaklah	2023-06-13T05:44:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T05:35:02Z	--- license: creativeml-openrail-m ---
DeskHyper/testingmodel	DeskHyper	2023-06-13T05:41:46Z	4	0	transformers	[ "transformers", "resnet50", "license:bigscience-bloom-rail-1.0", "endpoints_compatible", "region:us" ]	null	2023-06-13T04:46:01Z	--- license: bigscience-bloom-rail-1.0 ---
or90/results	or90	2023-06-13T05:39:16Z	0	0	null	[ "generated_from_trainer", "region:us" ]	null	2023-06-13T05:34:48Z	--- tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
el254/digits	el254	2023-06-13T05:37:35Z	0	0	keras	[ "keras", "region:us" ]	null	2023-06-12T18:28:15Z	--- library_name: keras --- #_Моя_модель_для_распознавания_цифр Натренирована на наборе данных mnist ![](pic.png)
PlengP/phishoff-bert-300k-II	PlengP	2023-06-13T05:29:22Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T05:29:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: phishoff-bert-300k-II 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. --> # phishoff-bert-300k-II 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Tokenizers 0.13.3
mamun4105/q-Taxi-v3	mamun4105	2023-06-13T05:25:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-12T18:57:07Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="mamun4105/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
julianzy/CheckGPT	julianzy	2023-06-13T05:25:21Z	0	1	null	[ "dataset:julianzy/GPABenchmark", "region:us" ]	null	2023-06-13T05:23:14Z	--- datasets: - julianzy/GPABenchmark --- The official repository of paper: "Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT".
Zemulax/masked-lm-tpu	Zemulax	2023-06-13T05:18:23Z	4	0	transformers	[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-13T00:21:57Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: Zemulax/masked-lm-tpu 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. --> # Zemulax/masked-lm-tpu 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: - Train Loss: 7.7770 - Train Accuracy: 0.0241 - Validation Loss: 7.7589 - Validation Accuracy: 0.0230 - Epoch: 98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 223250, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 11750, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 10.2868 \| 0.0 \| 10.2891 \| 0.0 \| 0 \| \| 10.2817 \| 0.0000 \| 10.2764 \| 0.0 \| 1 \| \| 10.2772 \| 0.0000 \| 10.2667 \| 0.0000 \| 2 \| \| 10.2604 \| 0.0000 \| 10.2521 \| 0.0 \| 3 \| \| 10.2421 \| 0.0000 \| 10.2282 \| 0.0000 \| 4 \| \| 10.2219 \| 0.0 \| 10.2010 \| 0.0 \| 5 \| \| 10.1957 \| 0.0 \| 10.1669 \| 0.0 \| 6 \| \| 10.1667 \| 0.0000 \| 10.1388 \| 0.0000 \| 7 \| \| 10.1278 \| 0.0000 \| 10.0908 \| 0.0000 \| 8 \| \| 10.0848 \| 0.0000 \| 10.0405 \| 0.0001 \| 9 \| \| 10.0496 \| 0.0002 \| 9.9921 \| 0.0007 \| 10 \| \| 9.9940 \| 0.0010 \| 9.9422 \| 0.0039 \| 11 \| \| 9.9424 \| 0.0035 \| 9.8765 \| 0.0110 \| 12 \| \| 9.8826 \| 0.0092 \| 9.8156 \| 0.0182 \| 13 \| \| 9.8225 \| 0.0155 \| 9.7461 \| 0.0209 \| 14 \| \| 9.7670 \| 0.0201 \| 9.6768 \| 0.0222 \| 15 \| \| 9.7065 \| 0.0219 \| 9.6127 \| 0.0222 \| 16 \| \| 9.6352 \| 0.0227 \| 9.5445 \| 0.0220 \| 17 \| \| 9.5757 \| 0.0226 \| 9.4795 \| 0.0219 \| 18 \| \| 9.4894 \| 0.0232 \| 9.3985 \| 0.0222 \| 19 \| \| 9.4277 \| 0.0234 \| 9.3386 \| 0.0222 \| 20 \| \| 9.3676 \| 0.0229 \| 9.2753 \| 0.0220 \| 21 \| \| 9.2980 \| 0.0229 \| 9.2170 \| 0.0219 \| 22 \| \| 9.2361 \| 0.0233 \| 9.1518 \| 0.0219 \| 23 \| \| 9.1515 \| 0.0236 \| 9.0827 \| 0.0223 \| 24 \| \| 9.1171 \| 0.0228 \| 9.0406 \| 0.0218 \| 25 \| \| 9.0447 \| 0.0234 \| 8.9867 \| 0.0218 \| 26 \| \| 9.0119 \| 0.0229 \| 8.9307 \| 0.0221 \| 27 \| \| 8.9625 \| 0.0229 \| 8.8969 \| 0.0221 \| 28 \| \| 8.9098 \| 0.0230 \| 8.8341 \| 0.0223 \| 29 \| \| 8.8726 \| 0.0227 \| 8.8118 \| 0.0220 \| 30 \| \| 8.8574 \| 0.0223 \| 8.7910 \| 0.0219 \| 31 \| \| 8.7798 \| 0.0231 \| 8.7506 \| 0.0221 \| 32 \| \| 8.7535 \| 0.0231 \| 8.7055 \| 0.0222 \| 33 \| \| 8.7333 \| 0.0228 \| 8.6801 \| 0.0223 \| 34 \| \| 8.6985 \| 0.0231 \| 8.6837 \| 0.0220 \| 35 \| \| 8.6816 \| 0.0229 \| 8.6243 \| 0.0223 \| 36 \| \| 8.6356 \| 0.0228 \| 8.6323 \| 0.0217 \| 37 \| \| 8.6392 \| 0.0225 \| 8.5603 \| 0.0225 \| 38 \| \| 8.5802 \| 0.0233 \| 8.5722 \| 0.0219 \| 39 \| \| 8.5825 \| 0.0228 \| 8.5548 \| 0.0220 \| 40 \| \| 8.5625 \| 0.0228 \| 8.5272 \| 0.0220 \| 41 \| \| 8.5415 \| 0.0228 \| 8.5200 \| 0.0222 \| 42 \| \| 8.5124 \| 0.0230 \| 8.4787 \| 0.0222 \| 43 \| \| 8.4999 \| 0.0229 \| 8.4819 \| 0.0218 \| 44 \| \| 8.4561 \| 0.0235 \| 8.4453 \| 0.0221 \| 45 \| \| 8.4854 \| 0.0223 \| 8.4378 \| 0.0220 \| 46 \| \| 8.4367 \| 0.0229 \| 8.4212 \| 0.0222 \| 47 \| \| 8.4096 \| 0.0232 \| 8.4033 \| 0.0221 \| 48 \| \| 8.4162 \| 0.0228 \| 8.3869 \| 0.0221 \| 49 \| \| 8.4005 \| 0.0229 \| 8.3768 \| 0.0218 \| 50 \| \| 8.3583 \| 0.0235 \| 8.3470 \| 0.0224 \| 51 \| \| 8.3428 \| 0.0235 \| 8.3540 \| 0.0221 \| 52 \| \| 8.3491 \| 0.0231 \| 8.3201 \| 0.0225 \| 53 \| \| 8.3551 \| 0.0231 \| 8.3382 \| 0.0221 \| 54 \| \| 8.3186 \| 0.0231 \| 8.3136 \| 0.0219 \| 55 \| \| 8.3139 \| 0.0226 \| 8.2844 \| 0.0222 \| 56 \| \| 8.3170 \| 0.0229 \| 8.2740 \| 0.0221 \| 57 \| \| 8.2886 \| 0.0231 \| 8.2485 \| 0.0223 \| 58 \| \| 8.2648 \| 0.0233 \| 8.2336 \| 0.0223 \| 59 \| \| 8.2714 \| 0.0225 \| 8.2321 \| 0.0221 \| 60 \| \| 8.2446 \| 0.0233 \| 8.2135 \| 0.0223 \| 61 \| \| 8.2303 \| 0.0230 \| 8.1980 \| 0.0223 \| 62 \| \| 8.2022 \| 0.0237 \| 8.1996 \| 0.0222 \| 63 \| \| 8.2222 \| 0.0227 \| 8.1822 \| 0.0222 \| 64 \| \| 8.1690 \| 0.0236 \| 8.2005 \| 0.0220 \| 65 \| \| 8.1741 \| 0.0233 \| 8.1446 \| 0.0226 \| 66 \| \| 8.1990 \| 0.0224 \| 8.1586 \| 0.0219 \| 67 \| \| 8.1395 \| 0.0236 \| 8.1243 \| 0.0225 \| 68 \| \| 8.1675 \| 0.0229 \| 8.1275 \| 0.0222 \| 69 \| \| 8.1432 \| 0.0229 \| 8.1374 \| 0.0217 \| 70 \| \| 8.1197 \| 0.0234 \| 8.1078 \| 0.0221 \| 71 \| \| 8.1046 \| 0.0232 \| 8.0991 \| 0.0221 \| 72 \| \| 8.1013 \| 0.0231 \| 8.0794 \| 0.0222 \| 73 \| \| 8.0887 \| 0.0228 \| 8.0720 \| 0.0221 \| 74 \| \| 8.0661 \| 0.0233 \| 8.0573 \| 0.0222 \| 75 \| \| 8.0548 \| 0.0231 \| 8.0313 \| 0.0226 \| 76 \| \| 8.0307 \| 0.0235 \| 8.0278 \| 0.0222 \| 77 \| \| 8.0626 \| 0.0226 \| 8.0084 \| 0.0224 \| 78 \| \| 8.0276 \| 0.0229 \| 8.0099 \| 0.0221 \| 79 \| \| 8.0213 \| 0.0231 \| 7.9930 \| 0.0222 \| 80 \| \| 7.9798 \| 0.0237 \| 7.9742 \| 0.0224 \| 81 \| \| 8.0135 \| 0.0226 \| 7.9857 \| 0.0218 \| 82 \| \| 7.9500 \| 0.0235 \| 7.9505 \| 0.0223 \| 83 \| \| 7.9519 \| 0.0234 \| 7.9711 \| 0.0217 \| 84 \| \| 7.9616 \| 0.0228 \| 7.9288 \| 0.0223 \| 85 \| \| 7.9803 \| 0.0225 \| 7.8997 \| 0.0226 \| 86 \| \| 7.9369 \| 0.0227 \| 7.9015 \| 0.0225 \| 87 \| \| 7.9309 \| 0.0229 \| 7.9010 \| 0.0224 \| 88 \| \| 7.9367 \| 0.0226 \| 7.8988 \| 0.0220 \| 89 \| \| 7.8840 \| 0.0230 \| 7.8774 \| 0.0216 \| 90 \| \| 7.8785 \| 0.0233 \| 7.8527 \| 0.0225 \| 91 \| \| 7.8998 \| 0.0226 \| 7.8509 \| 0.0219 \| 92 \| \| 7.8451 \| 0.0232 \| 7.8488 \| 0.0221 \| 93 \| \| 7.8596 \| 0.0231 \| 7.8310 \| 0.0222 \| 94 \| \| 7.8434 \| 0.0231 \| 7.8168 \| 0.0229 \| 95 \| \| 7.7929 \| 0.0238 \| 7.7815 \| 0.0233 \| 96 \| \| 7.8174 \| 0.0236 \| 7.7857 \| 0.0232 \| 97 \| \| 7.7770 \| 0.0241 \| 7.7589 \| 0.0230 \| 98 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Tokenizers 0.13.3
kejolong/Route246	kejolong	2023-06-13T05:15:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-13T05:13:09Z	--- license: creativeml-openrail-m ---
AustinCarthy/MixGPT2_Suffix_100KP_BFall_fromP_90K_topP_0.75_ratio5	AustinCarthy	2023-06-13T04:58:54Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-06-10T02:19:23Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_Domain_100KP_BFall_fromP_90K_topP_0.75_ratio5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_Domain_100KP_BFall_fromP_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2_using_benigh_95K_top_p_0.75suffix dataset. It achieves the following results on the evaluation set: - Loss: 0.0218 - Accuracy: 0.9976 - F1: 0.9743 - Precision: 0.9987 - Recall: 0.951 - Roc Auc Score: 0.9755 - Tpr At Fpr 0.01: 0.9608 ## 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: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.003 \| 1.0 \| 35625 \| 0.0165 \| 0.9972 \| 0.9705 \| 0.9921 \| 0.9498 \| 0.9747 \| 0.888 \| \| 0.0023 \| 2.0 \| 71250 \| 0.0171 \| 0.9976 \| 0.9739 \| 0.9985 \| 0.9504 \| 0.9752 \| 0.9526 \| \| 0.003 \| 3.0 \| 106875 \| 0.0151 \| 0.9978 \| 0.9765 \| 0.9983 \| 0.9556 \| 0.9778 \| 0.9584 \| \| 0.0017 \| 4.0 \| 142500 \| 0.0170 \| 0.9977 \| 0.9753 \| 0.9971 \| 0.9544 \| 0.9771 \| 0.9512 \| \| 0.0012 \| 5.0 \| 178125 \| 0.0218 \| 0.9976 \| 0.9743 \| 0.9987 \| 0.951 \| 0.9755 \| 0.9608 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
djifg/grow_classification_kobert-lm	djifg	2023-06-13T04:44:25Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T04:36:12Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification_kobert-lm 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. --> # grow_classification_kobert-lm This model is a fine-tuned version of [monologg/kobert-lm](https://huggingface.co/monologg/kobert-lm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0751 - Accuracy: 0.6946 - F1: 0.6970 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8394 \| 1.0 \| 329 \| 0.8209 \| 0.6664 \| 0.6719 \| \| 0.5789 \| 2.0 \| 658 \| 0.8653 \| 0.6962 \| 0.7018 \| \| 0.5125 \| 3.0 \| 987 \| 0.9554 \| 0.6942 \| 0.6919 \| \| 0.4852 \| 4.0 \| 1316 \| 0.9838 \| 0.7057 \| 0.7120 \| \| 0.464 \| 5.0 \| 1645 \| 0.9969 \| 0.6910 \| 0.6934 \| \| 0.4519 \| 6.0 \| 1974 \| 1.0354 \| 0.6918 \| 0.6973 \| \| 0.4437 \| 7.0 \| 2303 \| 1.0301 \| 0.6871 \| 0.6940 \| \| 0.4342 \| 8.0 \| 2632 \| 1.0676 \| 0.6950 \| 0.6972 \| \| 0.4299 \| 9.0 \| 2961 \| 1.0691 \| 0.6962 \| 0.6984 \| \| 0.4276 \| 10.0 \| 3290 \| 1.0751 \| 0.6946 \| 0.6970 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
ettsai/sentiment_analysis-DistilBERT-emo	ettsai	2023-06-13T04:38:25Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emo", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-12T13:51:39Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emo metrics: - accuracy model-index: - name: sentiment_analysis-DistilBERT-emo results: - task: name: Text Classification type: text-classification dataset: name: emo type: emo config: emo2019 split: test args: emo2019 metrics: - name: Accuracy type: accuracy value: 0.882 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment_analysis-DistilBERT-emo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emo dataset. It achieves the following results on the evaluation set: - Loss: 0.3929 - Accuracy: 0.882 - F1 Score: 0.8928 ## 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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Trickshotblaster/leetcoder-qa	Trickshotblaster	2023-06-13T04:37:14Z	63	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-13T04:36:42Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: leetcoder-qa 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. --> # leetcoder-qa This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1922 - Validation Loss: 3.8900 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 6.3985 \| 4.7493 \| 0 \| \| 4.8760 \| 4.4969 \| 1 \| \| 4.7016 \| 4.3654 \| 2 \| \| 4.5848 \| 4.2650 \| 3 \| \| 4.5086 \| 4.1827 \| 4 \| \| 4.4307 \| 4.1106 \| 5 \| \| 4.3581 \| 4.0471 \| 6 \| \| 4.2923 \| 3.9898 \| 7 \| \| 4.2397 \| 3.9375 \| 8 \| \| 4.1922 \| 3.8900 \| 9 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
danielthomas45a/pure-frankincense-essential-oils	danielthomas45a	2023-06-13T04:20:50Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-06-13T04:04:06Z	--- license: openrail --- Experience the captivating essence of nature with our <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a>. ✨ Uncover the secrets of this ancient treasure, known for its remarkable therapeutic properties and heavenly aroma. Elevate your senses and embark on a journey of serenity and rejuvenation. <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a> <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a> <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a>
irfanamal/distilroberta-base-finetuned-wikitext2	irfanamal	2023-06-13T04:18:14Z	164	0	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-12T11:00:37Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1005 \| 1.0 \| 1203 \| 1.9467 \| \| 2.034 \| 2.0 \| 2406 \| 1.8616 \| \| 1.9683 \| 3.0 \| 3609 \| 1.8253 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
mjlaali/ppo-LunarLander-v2	mjlaali	2023-06-13T04:14:35Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T04:14:21Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.65 +/- 25.35 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 ... ```
Dans-Archive/Dans-PersonalityEngine-13b	Dans-Archive	2023-06-13T04:14:23Z	53	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-11T23:42:20Z	--- language: - en --- ### Description: This is a multipurpose chat / chat instruct hybrid model in the same vein as the Pygmalion team's Metharme. It uses a curated pile of training data that has been normalized into a consistent training format. It has been trained on a wide array of one shot instructions, multi round instructions, and role playing scenarios. ### Prompt format: Metharme The prompt should start with the cursor on the same line directly after "<\|model\|>" with no space. The following are all valid formats and can be extended to as many rounds as desired. ``` <\|system\|>system message here<\|user\|>user message here<\|model\|> ``` ``` <\|system\|>system message here<\|user\|>user message here<\|model\|>model message<\|user\|>user message here<\|model\|> ``` ``` <\|system\|>system message here<\|model\|> ``` ``` <\|system\|>system message here<\|model\|>model message<\|user\|>user message here<\|model\|> ``` Some example prompts: ``` <\|system\|>The following is a transcript between a helpful assistant and a user.<\|user\|>Why is the sky blue?<\|model\|> ``` ``` <\|system\|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<\|user\|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<\|model\|> ``` ``` <\|system\|>You are a professional editor with decades of experience, help the user with any task they have for you.<\|user\|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<\|model\|> ``` More will be added at a later date. ### Perplexity Benchmarks: - TBA ### Training information: [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl) - GPTQ 4 bit LoRA - 7 Epochs - 64 / 32 R / A - 2048 Cutoff - 18 hours on 4x RTX 4090s ### Data used in training: - TBA ### Models used: For training: https://huggingface.co/PocketDoc/llama-13b-gptq-4bit-128g For merging: https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA and https://huggingface.co/huggyllama/llama-13b ### Disclaimer: It has not been aligned and no warranty is given for the quality or safety of its outputs.
silpakanneganti/biobert-finetuned-squad-insurance	silpakanneganti	2023-06-13T03:47:55Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-06-12T10:07:55Z	--- tags: - generated_from_trainer model-index: - name: biobert-finetuned-squad-insurance 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. --> # biobert-finetuned-squad-insurance This model is a fine-tuned version of [dmis-lab/biobert-large-cased-v1.1-squad](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1-squad) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
gsn-codes/a2c-AntBulletEnv-v0	gsn-codes	2023-06-13T03:43:09Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T02:20:41Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1407.58 +/- 108.66 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_120	gokuls	2023-06-13T03:39:09Z	53	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-11T21:07:16Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v1_complete_training_new_wt_init_120 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_12_layer_model_v1_complete_training_new_wt_init_120 This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_96](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_96) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1966 - Accuracy: 0.5856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 2.3673 \| 0.08 \| 10000 \| 2.2852 \| 0.5732 \| \| 2.356 \| 0.16 \| 20000 \| 2.2772 \| 0.5744 \| \| 2.3424 \| 0.25 \| 30000 \| 2.2640 \| 0.5765 \| \| 2.3442 \| 0.33 \| 40000 \| 2.2525 \| 0.5778 \| \| 2.3228 \| 0.41 \| 50000 \| 2.2427 \| 0.5793 \| \| 2.3179 \| 0.49 \| 60000 \| 2.2313 \| 0.5810 \| \| 2.2993 \| 0.57 \| 70000 \| 2.2237 \| 0.5822 \| \| 2.2911 \| 0.66 \| 80000 \| 2.2128 \| 0.5831 \| \| 2.279 \| 0.74 \| 90000 \| 2.2008 \| 0.5842 \| \| 2.2715 \| 0.82 \| 100000 \| 2.1966 \| 0.5856 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.12.0 - Tokenizers 0.13.3
NeoCodes-dev/Reinforce-pixel_copter_v1	NeoCodes-dev	2023-06-13T03:25:44Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-13T03:25:39Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixel_copter_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.90 +/- 52.86 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
killah-t-cell/model_out	killah-t-cell	2023-06-13T03:19:15Z	1	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-06-12T22:33:37Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-killah-t-cell/model_out These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below. prompt: Crowd of men wearing hats with trees in the background ![images_0)](./images_0.png) prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality ![images_1)](./images_1.png) prompt: Group photo of clowns, oil on canvas, bittersweet expression ![images_2)](./images_2.png)
ugiugi/inisw08-DistilBERT-mlm-sgd	ugiugi	2023-06-13T03:15:19Z	20	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-13T01:22:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: inisw08-RoBERT-mlm-sgd 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. --> # inisw08-RoBERT-mlm-sgd This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4290 - Accuracy: 0.3133 ## 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 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
ugiugi/inisw08-DistilBERT-mlm-adamw_torch	ugiugi	2023-06-13T03:10:38Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-13T00:47:42Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: inisw08-RoBERT-mlm-adamw_torch 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. --> # inisw08-RoBERT-mlm-adamw_torch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1120 - Accuracy: 0.4689 ## 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 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
djifg/grow_classification	djifg	2023-06-13T02:16:31Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T02:04:23Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification 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. --> # grow_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2151 - Accuracy: 0.7979 - F1: 0.8011 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7378 \| 1.0 \| 329 \| 0.7427 \| 0.7542 \| 0.7576 \| \| 0.2507 \| 2.0 \| 658 \| 0.8395 \| 0.7744 \| 0.7803 \| \| 0.1459 \| 3.0 \| 987 \| 0.9161 \| 0.7776 \| 0.7818 \| \| 0.0897 \| 4.0 \| 1316 \| 1.1515 \| 0.7617 \| 0.7695 \| \| 0.0625 \| 5.0 \| 1645 \| 1.0914 \| 0.7855 \| 0.7925 \| \| 0.0505 \| 6.0 \| 1974 \| 1.1111 \| 0.7907 \| 0.7941 \| \| 0.039 \| 7.0 \| 2303 \| 1.3218 \| 0.7673 \| 0.7762 \| \| 0.0344 \| 8.0 \| 2632 \| 1.2019 \| 0.7943 \| 0.7970 \| \| 0.0262 \| 9.0 \| 2961 \| 1.2520 \| 0.7907 \| 0.7949 \| \| 0.0238 \| 10.0 \| 3290 \| 1.2151 \| 0.7979 \| 0.8011 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
yyabuki/pegasus-samsum	yyabuki	2023-06-13T01:35:21Z	96	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-12T16:06:37Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Framework versions - Transformers 4.28.1 - Pytorch 1.10.2 - Datasets 2.11.0 - Tokenizers 0.13.3
withxiao/alpaca-llama-7b-fp16	withxiao	2023-06-13T01:27:18Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:yahma/alpaca-cleaned", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-10T05:00:41Z	--- datasets: - yahma/alpaca-cleaned pipeline_tag: text-generation ---
GoGD/xlm-roberta-base-finetuned-panx-all	GoGD	2023-06-13T01:25:21Z	101	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-13T01:20:24Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1297 - F1: 0.8803 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2887 \| 1.0 \| 620 \| 0.1770 \| 0.8207 \| \| 0.1426 \| 2.0 \| 1240 \| 0.1468 \| 0.8578 \| \| 0.091 \| 3.0 \| 1860 \| 0.1297 \| 0.8803 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3
timdettmers/qlora-longform-65b	timdettmers	2023-06-13T01:24:28Z	0	0	null	[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]	null	2023-05-22T20:40:37Z	# QLoRA Instruction Tuned Models \| [Paper](https://arxiv.org/abs/2305.14314) \| [Code](https://github.com/artidoro/qlora) \| [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) \| The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes. Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets. ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - Strong performance on MMLU following the QLoRA instruction tuning. - Replicable and efficient instruction tuning procedure that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - Rigorous comparison to 16-bit methods (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - Lightweight checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card Architecture: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. Base Model: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. Finetuning Data: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. Languages: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters \| Parameters \| Dataset \| Batch size \| LR \| Steps \| Source Length \| Target Length \| \|------------\|----------\|------------\|------\|-------\|---------------\|---------------\| \| 7B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 7B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 7B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 7B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 13B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 13B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 13B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 13B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 33B \| All \| 32 \| 1e-4 \| 5000 \| 384 \| 128 \| \| 33B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 33B \| HH-RLHF \| 32 \| 1e-4 \| 5000 \| - \| 768 \| \| 33B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| \| 65B \| All \| 64 \| 1e-4 \| 2500 \| 384 \| 128 \| \| 65B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 65B \| HH-RLHF \| 64 \| 1e-4 \| 2500 \| - \| 768 \| \| 65B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset \| 7B \| 13B \| 33B \| 65B ---\|---\|---\|---\|--- LLaMA no tuning \| 35.1 \| 46.9 \| 57.8 \| 63.4 Self-Instruct \| 36.4 \| 33.3 \| 53.0 \| 56.7 Longform \| 32.1 \| 43.2 \| 56.6 \| 59.7 Chip2 \| 34.5 \| 41.6 \| 53.6 \| 59.8 HH-RLHF \| 34.9 \| 44.6 \| 55.8 \| 60.1 Unnatural Instruct \| 41.9 \| 48.1 \| 57.3 \| 61.3 OASST1 (Guanaco) \| 36.6 \| 46.4 \| 57.0 \| 62.2 Alpaca \| 38.8 \| 47.8 \| 57.3 \| 62.5 FLAN v2 \| 44.5 \| 51.4 \| 59.2 \| 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. \| Model / Dataset \| Params \| Model bits \| Memory \| ChatGPT vs Sys \| Sys vs ChatGPT \| Mean \| 95\% CI \| \|------------------\|--------\|------------\|--------\|----------------\|----------------\|------------------\|---------\| \| GPT-4 \| - \| - \| - \| 119.4\% \| 110.1\% \| 114.5\% \| 2.6\% \| \| Bard \| - \| - \| - \| 93.2\% \| 96.4\% \| 94.8\% \| 4.1\% \| \| Guanaco \| 65B \| 4-bit \| 41 GB \| 96.7\% \| 101.9\% \| 99.3\% \| 4.4\% \| \| Alpaca \| 65B \| 4-bit \| 41 GB \| 63.0\% \| 77.9\% \| 70.7\% \| 4.3\% \| \| FLAN v2 \| 65B \| 4-bit \| 41 GB \| 37.0\% \| 59.6\% \| 48.4\% \| 4.6\% \| \| Guanaco \| 33B \| 4-bit \| 21 GB \| 96.5\% \| 99.2\% \| 97.8\% \| 4.4\% \| \| Open Assistant \| 33B \| 16-bit \| 66 GB \| 73.4\% \| 85.7\% \| 78.1\% \| 5.3\% \| \| Alpaca \| 33B \| 4-bit \| 21 GB \| 67.2\% \| 79.7\% \| 73.6\% \| 4.2\% \| \| FLAN v2 \| 33B \| 4-bit \| 21 GB \| 26.3\% \| 49.7\% \| 38.0\% \| 3.9\% \| \| Vicuna \| 13B \| 16-bit \| 26 GB \| 91.2\% \| 98.7\% \| 94.9\% \| 4.5\% \| \| Guanaco \| 13B \| 4-bit \| 10 GB \| 87.3\% \| 93.4\% \| 90.4\% \| 5.2\% \| \| Alpaca \| 13B \| 4-bit \| 10 GB \| 63.8\% \| 76.7\% \| 69.4\% \| 4.2\% \| \| HH-RLHF \| 13B \| 4-bit \| 10 GB \| 55.5\% \| 69.1\% \| 62.5\% \| 4.7\% \| \| Unnatural Instr. \| 13B \| 4-bit \| 10 GB \| 50.6\% \| 69.8\% \| 60.5\% \| 4.2\% \| \| Chip2 \| 13B \| 4-bit \| 10 GB \| 49.2\% \| 69.3\% \| 59.5\% \| 4.7\% \| \| Longform \| 13B \| 4-bit \| 10 GB \| 44.9\% \| 62.0\% \| 53.6\% \| 5.2\% \| \| Self-Instruct \| 13B \| 4-bit \| 10 GB \| 38.0\% \| 60.5\% \| 49.1\% \| 4.6\% \| \| FLAN v2 \| 13B \| 4-bit \| 10 GB \| 32.4\% \| 61.2\% \| 47.0\% \| 3.6\% \| \| Guanaco \| 7B \| 4-bit \| 5 GB \| 84.1\% \| 89.8\% \| 87.0\% \| 5.4\% \| \| Alpaca \| 7B \| 4-bit \| 5 GB \| 57.3\% \| 71.2\% \| 64.4\% \| 5.0\% \| \| FLAN v2 \| 7B \| 4-bit \| 5 GB \| 33.3\% \| 56.1\% \| 44.8\% \| 4.0\% \| ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```
timdettmers/qlora-chip2-65b	timdettmers	2023-06-13T01:24:26Z	0	0	null	[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]	null	2023-05-22T20:31:11Z	# QLoRA Instruction Tuned Models \| [Paper](https://arxiv.org/abs/2305.14314) \| [Code](https://github.com/artidoro/qlora) \| [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) \| The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes. Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets. ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - Strong performance on MMLU following the QLoRA instruction tuning. - Replicable and efficient instruction tuning procedure that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - Rigorous comparison to 16-bit methods (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - Lightweight checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card Architecture: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. Base Model: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. Finetuning Data: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. Languages: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters \| Parameters \| Dataset \| Batch size \| LR \| Steps \| Source Length \| Target Length \| \|------------\|----------\|------------\|------\|-------\|---------------\|---------------\| \| 7B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 7B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 7B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 7B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 13B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 13B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 13B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 13B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 33B \| All \| 32 \| 1e-4 \| 5000 \| 384 \| 128 \| \| 33B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 33B \| HH-RLHF \| 32 \| 1e-4 \| 5000 \| - \| 768 \| \| 33B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| \| 65B \| All \| 64 \| 1e-4 \| 2500 \| 384 \| 128 \| \| 65B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 65B \| HH-RLHF \| 64 \| 1e-4 \| 2500 \| - \| 768 \| \| 65B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset \| 7B \| 13B \| 33B \| 65B ---\|---\|---\|---\|--- LLaMA no tuning \| 35.1 \| 46.9 \| 57.8 \| 63.4 Self-Instruct \| 36.4 \| 33.3 \| 53.0 \| 56.7 Longform \| 32.1 \| 43.2 \| 56.6 \| 59.7 Chip2 \| 34.5 \| 41.6 \| 53.6 \| 59.8 HH-RLHF \| 34.9 \| 44.6 \| 55.8 \| 60.1 Unnatural Instruct \| 41.9 \| 48.1 \| 57.3 \| 61.3 OASST1 (Guanaco) \| 36.6 \| 46.4 \| 57.0 \| 62.2 Alpaca \| 38.8 \| 47.8 \| 57.3 \| 62.5 FLAN v2 \| 44.5 \| 51.4 \| 59.2 \| 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. \| Model / Dataset \| Params \| Model bits \| Memory \| ChatGPT vs Sys \| Sys vs ChatGPT \| Mean \| 95\% CI \| \|------------------\|--------\|------------\|--------\|----------------\|----------------\|------------------\|---------\| \| GPT-4 \| - \| - \| - \| 119.4\% \| 110.1\% \| 114.5\% \| 2.6\% \| \| Bard \| - \| - \| - \| 93.2\% \| 96.4\% \| 94.8\% \| 4.1\% \| \| Guanaco \| 65B \| 4-bit \| 41 GB \| 96.7\% \| 101.9\% \| 99.3\% \| 4.4\% \| \| Alpaca \| 65B \| 4-bit \| 41 GB \| 63.0\% \| 77.9\% \| 70.7\% \| 4.3\% \| \| FLAN v2 \| 65B \| 4-bit \| 41 GB \| 37.0\% \| 59.6\% \| 48.4\% \| 4.6\% \| \| Guanaco \| 33B \| 4-bit \| 21 GB \| 96.5\% \| 99.2\% \| 97.8\% \| 4.4\% \| \| Open Assistant \| 33B \| 16-bit \| 66 GB \| 73.4\% \| 85.7\% \| 78.1\% \| 5.3\% \| \| Alpaca \| 33B \| 4-bit \| 21 GB \| 67.2\% \| 79.7\% \| 73.6\% \| 4.2\% \| \| FLAN v2 \| 33B \| 4-bit \| 21 GB \| 26.3\% \| 49.7\% \| 38.0\% \| 3.9\% \| \| Vicuna \| 13B \| 16-bit \| 26 GB \| 91.2\% \| 98.7\% \| 94.9\% \| 4.5\% \| \| Guanaco \| 13B \| 4-bit \| 10 GB \| 87.3\% \| 93.4\% \| 90.4\% \| 5.2\% \| \| Alpaca \| 13B \| 4-bit \| 10 GB \| 63.8\% \| 76.7\% \| 69.4\% \| 4.2\% \| \| HH-RLHF \| 13B \| 4-bit \| 10 GB \| 55.5\% \| 69.1\% \| 62.5\% \| 4.7\% \| \| Unnatural Instr. \| 13B \| 4-bit \| 10 GB \| 50.6\% \| 69.8\% \| 60.5\% \| 4.2\% \| \| Chip2 \| 13B \| 4-bit \| 10 GB \| 49.2\% \| 69.3\% \| 59.5\% \| 4.7\% \| \| Longform \| 13B \| 4-bit \| 10 GB \| 44.9\% \| 62.0\% \| 53.6\% \| 5.2\% \| \| Self-Instruct \| 13B \| 4-bit \| 10 GB \| 38.0\% \| 60.5\% \| 49.1\% \| 4.6\% \| \| FLAN v2 \| 13B \| 4-bit \| 10 GB \| 32.4\% \| 61.2\% \| 47.0\% \| 3.6\% \| \| Guanaco \| 7B \| 4-bit \| 5 GB \| 84.1\% \| 89.8\% \| 87.0\% \| 5.4\% \| \| Alpaca \| 7B \| 4-bit \| 5 GB \| 57.3\% \| 71.2\% \| 64.4\% \| 5.0\% \| \| FLAN v2 \| 7B \| 4-bit \| 5 GB \| 33.3\% \| 56.1\% \| 44.8\% \| 4.0\% \| ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```
timdettmers/qlora-flan-65b	timdettmers	2023-06-13T01:24:21Z	0	1	null	[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]	null	2023-05-22T19:14:00Z	# QLoRA Instruction Tuned Models \| [Paper](https://arxiv.org/abs/2305.14314) \| [Code](https://github.com/artidoro/qlora) \| [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) \| The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes. Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets. ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - Strong performance on MMLU following the QLoRA instruction tuning. - Replicable and efficient instruction tuning procedure that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - Rigorous comparison to 16-bit methods (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - Lightweight checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card Architecture: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. Base Model: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. Finetuning Data: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. Languages: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters \| Parameters \| Dataset \| Batch size \| LR \| Steps \| Source Length \| Target Length \| \|------------\|----------\|------------\|------\|-------\|---------------\|---------------\| \| 7B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 7B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 7B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 7B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 13B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 13B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 13B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 13B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 33B \| All \| 32 \| 1e-4 \| 5000 \| 384 \| 128 \| \| 33B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 33B \| HH-RLHF \| 32 \| 1e-4 \| 5000 \| - \| 768 \| \| 33B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| \| 65B \| All \| 64 \| 1e-4 \| 2500 \| 384 \| 128 \| \| 65B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 65B \| HH-RLHF \| 64 \| 1e-4 \| 2500 \| - \| 768 \| \| 65B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset \| 7B \| 13B \| 33B \| 65B ---\|---\|---\|---\|--- LLaMA no tuning \| 35.1 \| 46.9 \| 57.8 \| 63.4 Self-Instruct \| 36.4 \| 33.3 \| 53.0 \| 56.7 Longform \| 32.1 \| 43.2 \| 56.6 \| 59.7 Chip2 \| 34.5 \| 41.6 \| 53.6 \| 59.8 HH-RLHF \| 34.9 \| 44.6 \| 55.8 \| 60.1 Unnatural Instruct \| 41.9 \| 48.1 \| 57.3 \| 61.3 OASST1 (Guanaco) \| 36.6 \| 46.4 \| 57.0 \| 62.2 Alpaca \| 38.8 \| 47.8 \| 57.3 \| 62.5 FLAN v2 \| 44.5 \| 51.4 \| 59.2 \| 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. \| Model / Dataset \| Params \| Model bits \| Memory \| ChatGPT vs Sys \| Sys vs ChatGPT \| Mean \| 95\% CI \| \|------------------\|--------\|------------\|--------\|----------------\|----------------\|------------------\|---------\| \| GPT-4 \| - \| - \| - \| 119.4\% \| 110.1\% \| 114.5\% \| 2.6\% \| \| Bard \| - \| - \| - \| 93.2\% \| 96.4\% \| 94.8\% \| 4.1\% \| \| Guanaco \| 65B \| 4-bit \| 41 GB \| 96.7\% \| 101.9\% \| 99.3\% \| 4.4\% \| \| Alpaca \| 65B \| 4-bit \| 41 GB \| 63.0\% \| 77.9\% \| 70.7\% \| 4.3\% \| \| FLAN v2 \| 65B \| 4-bit \| 41 GB \| 37.0\% \| 59.6\% \| 48.4\% \| 4.6\% \| \| Guanaco \| 33B \| 4-bit \| 21 GB \| 96.5\% \| 99.2\% \| 97.8\% \| 4.4\% \| \| Open Assistant \| 33B \| 16-bit \| 66 GB \| 73.4\% \| 85.7\% \| 78.1\% \| 5.3\% \| \| Alpaca \| 33B \| 4-bit \| 21 GB \| 67.2\% \| 79.7\% \| 73.6\% \| 4.2\% \| \| FLAN v2 \| 33B \| 4-bit \| 21 GB \| 26.3\% \| 49.7\% \| 38.0\% \| 3.9\% \| \| Vicuna \| 13B \| 16-bit \| 26 GB \| 91.2\% \| 98.7\% \| 94.9\% \| 4.5\% \| \| Guanaco \| 13B \| 4-bit \| 10 GB \| 87.3\% \| 93.4\% \| 90.4\% \| 5.2\% \| \| Alpaca \| 13B \| 4-bit \| 10 GB \| 63.8\% \| 76.7\% \| 69.4\% \| 4.2\% \| \| HH-RLHF \| 13B \| 4-bit \| 10 GB \| 55.5\% \| 69.1\% \| 62.5\% \| 4.7\% \| \| Unnatural Instr. \| 13B \| 4-bit \| 10 GB \| 50.6\% \| 69.8\% \| 60.5\% \| 4.2\% \| \| Chip2 \| 13B \| 4-bit \| 10 GB \| 49.2\% \| 69.3\% \| 59.5\% \| 4.7\% \| \| Longform \| 13B \| 4-bit \| 10 GB \| 44.9\% \| 62.0\% \| 53.6\% \| 5.2\% \| \| Self-Instruct \| 13B \| 4-bit \| 10 GB \| 38.0\% \| 60.5\% \| 49.1\% \| 4.6\% \| \| FLAN v2 \| 13B \| 4-bit \| 10 GB \| 32.4\% \| 61.2\% \| 47.0\% \| 3.6\% \| \| Guanaco \| 7B \| 4-bit \| 5 GB \| 84.1\% \| 89.8\% \| 87.0\% \| 5.4\% \| \| Alpaca \| 7B \| 4-bit \| 5 GB \| 57.3\% \| 71.2\% \| 64.4\% \| 5.0\% \| \| FLAN v2 \| 7B \| 4-bit \| 5 GB \| 33.3\% \| 56.1\% \| 44.8\% \| 4.0\% \| ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```
timdettmers/qlora-hh-rlhf-33b	timdettmers	2023-06-13T01:24:14Z	0	1	null	[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]	null	2023-05-22T21:33:20Z	# QLoRA Instruction Tuned Models \| [Paper](https://arxiv.org/abs/2305.14314) \| [Code](https://github.com/artidoro/qlora) \| [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) \| The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes. Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets. ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - Strong performance on MMLU following the QLoRA instruction tuning. - Replicable and efficient instruction tuning procedure that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - Rigorous comparison to 16-bit methods (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - Lightweight checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card Architecture: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. Base Model: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. Finetuning Data: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. Languages: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters \| Parameters \| Dataset \| Batch size \| LR \| Steps \| Source Length \| Target Length \| \|------------\|----------\|------------\|------\|-------\|---------------\|---------------\| \| 7B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 7B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 7B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 7B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 13B \| All \| 16 \| 2e-4 \| 10000 \| 384 \| 128 \| \| 13B \| OASST1 \| 16 \| 2e-4 \| 1875 \| - \| 512 \| \| 13B \| HH-RLHF \| 16 \| 2e-4 \| 10000 \| - \| 768 \| \| 13B \| Longform \| 16 \| 2e-4 \| 4000 \| 512 \| 1024 \| \| 33B \| All \| 32 \| 1e-4 \| 5000 \| 384 \| 128 \| \| 33B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 33B \| HH-RLHF \| 32 \| 1e-4 \| 5000 \| - \| 768 \| \| 33B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| \| 65B \| All \| 64 \| 1e-4 \| 2500 \| 384 \| 128 \| \| 65B \| OASST1 \| 16 \| 1e-4 \| 1875 \| - \| 512 \| \| 65B \| HH-RLHF \| 64 \| 1e-4 \| 2500 \| - \| 768 \| \| 65B \| Longform \| 32 \| 1e-4 \| 2343 \| 512 \| 1024 \| ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset \| 7B \| 13B \| 33B \| 65B ---\|---\|---\|---\|--- LLaMA no tuning \| 35.1 \| 46.9 \| 57.8 \| 63.4 Self-Instruct \| 36.4 \| 33.3 \| 53.0 \| 56.7 Longform \| 32.1 \| 43.2 \| 56.6 \| 59.7 Chip2 \| 34.5 \| 41.6 \| 53.6 \| 59.8 HH-RLHF \| 34.9 \| 44.6 \| 55.8 \| 60.1 Unnatural Instruct \| 41.9 \| 48.1 \| 57.3 \| 61.3 OASST1 (Guanaco) \| 36.6 \| 46.4 \| 57.0 \| 62.2 Alpaca \| 38.8 \| 47.8 \| 57.3 \| 62.5 FLAN v2 \| 44.5 \| 51.4 \| 59.2 \| 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. \| Model / Dataset \| Params \| Model bits \| Memory \| ChatGPT vs Sys \| Sys vs ChatGPT \| Mean \| 95\% CI \| \|------------------\|--------\|------------\|--------\|----------------\|----------------\|------------------\|---------\| \| GPT-4 \| - \| - \| - \| 119.4\% \| 110.1\% \| 114.5\% \| 2.6\% \| \| Bard \| - \| - \| - \| 93.2\% \| 96.4\% \| 94.8\% \| 4.1\% \| \| Guanaco \| 65B \| 4-bit \| 41 GB \| 96.7\% \| 101.9\% \| 99.3\% \| 4.4\% \| \| Alpaca \| 65B \| 4-bit \| 41 GB \| 63.0\% \| 77.9\% \| 70.7\% \| 4.3\% \| \| FLAN v2 \| 65B \| 4-bit \| 41 GB \| 37.0\% \| 59.6\% \| 48.4\% \| 4.6\% \| \| Guanaco \| 33B \| 4-bit \| 21 GB \| 96.5\% \| 99.2\% \| 97.8\% \| 4.4\% \| \| Open Assistant \| 33B \| 16-bit \| 66 GB \| 73.4\% \| 85.7\% \| 78.1\% \| 5.3\% \| \| Alpaca \| 33B \| 4-bit \| 21 GB \| 67.2\% \| 79.7\% \| 73.6\% \| 4.2\% \| \| FLAN v2 \| 33B \| 4-bit \| 21 GB \| 26.3\% \| 49.7\% \| 38.0\% \| 3.9\% \| \| Vicuna \| 13B \| 16-bit \| 26 GB \| 91.2\% \| 98.7\% \| 94.9\% \| 4.5\% \| \| Guanaco \| 13B \| 4-bit \| 10 GB \| 87.3\% \| 93.4\% \| 90.4\% \| 5.2\% \| \| Alpaca \| 13B \| 4-bit \| 10 GB \| 63.8\% \| 76.7\% \| 69.4\% \| 4.2\% \| \| HH-RLHF \| 13B \| 4-bit \| 10 GB \| 55.5\% \| 69.1\% \| 62.5\% \| 4.7\% \| \| Unnatural Instr. \| 13B \| 4-bit \| 10 GB \| 50.6\% \| 69.8\% \| 60.5\% \| 4.2\% \| \| Chip2 \| 13B \| 4-bit \| 10 GB \| 49.2\% \| 69.3\% \| 59.5\% \| 4.7\% \| \| Longform \| 13B \| 4-bit \| 10 GB \| 44.9\% \| 62.0\% \| 53.6\% \| 5.2\% \| \| Self-Instruct \| 13B \| 4-bit \| 10 GB \| 38.0\% \| 60.5\% \| 49.1\% \| 4.6\% \| \| FLAN v2 \| 13B \| 4-bit \| 10 GB \| 32.4\% \| 61.2\% \| 47.0\% \| 3.6\% \| \| Guanaco \| 7B \| 4-bit \| 5 GB \| 84.1\% \| 89.8\% \| 87.0\% \| 5.4\% \| \| Alpaca \| 7B \| 4-bit \| 5 GB \| 57.3\% \| 71.2\% \| 64.4\% \| 5.0\% \| \| FLAN v2 \| 7B \| 4-bit \| 5 GB \| 33.3\% \| 56.1\% \| 44.8\% \| 4.0\% \| ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```

haonan-li/bactrian-vi-bloom-7b1-lora

haonan-li

2023-06-13T13:27:20Z

0

null

[ "arxiv:2305.15011", "license:mit", "region:us" ]

null

2023-06-13T13:27:08Z

--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Vietnamese. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-vi-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

haonan-li/bactrian-sw-bloom-7b1-lora

haonan-li

2023-06-13T13:27:08Z

0

null

[ "arxiv:2305.15011", "license:mit", "region:us" ]

null

2023-06-13T13:26:54Z

--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Swahili. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-sw-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

haonan-li/bactrian-zh-bloom-7b1-lora

haonan-li

2023-06-13T13:26:53Z

0

1

null

[ "arxiv:2305.15011", "license:mit", "region:us" ]

null

2023-06-13T13:26:29Z

--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Chinese. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-zh-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

haonan-li/bactrian-ar-bloom-7b1-lora

haonan-li

2023-06-13T13:25:48Z

0

null

[ "arxiv:2305.15011", "license:mit", "region:us" ]

null

2023-06-13T13:25:36Z

--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Arabic. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-ar-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

irfanamal/bert-base-uncased-classification-flat

irfanamal

2023-06-13T13:14:45Z

104

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T07:20:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-uncased-classification-flat results: [] ---  # bert-base-uncased-classification-flat This model is a fine-tuned version of [irfanamal/bert-base-uncased-finetuned-amazonreviews](https://huggingface.co/irfanamal/bert-base-uncased-finetuned-amazonreviews) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4951 - Accuracy: 0.4957 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.7227 | 1.0 | 1250 | 3.3098 | 0.3826 | | 2.6109 | 2.0 | 2500 | 2.7897 | 0.4568 | | 2.2396 | 3.0 | 3750 | 2.5943 | 0.4809 | | 1.9093 | 4.0 | 5000 | 2.5155 | 0.4937 | | 1.7949 | 5.0 | 6250 | 2.4951 | 0.4957 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

kejolong/SNI

kejolong

2023-06-13T13:00:30Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T12:58:55Z

--- license: creativeml-openrail-m ---

diallomama/fr-summarization

diallomama

2023-06-13T12:56:59Z

11

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:GEM/wiki_lingua", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-09T20:52:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - GEM/wiki_lingua metrics: - rouge model-index: - name: fr-summarization results: - task: name: Summarization type: summarization dataset: name: GEM/wiki_lingua fr type: GEM/wiki_lingua config: fr split: validation args: fr metrics: - name: Rouge1 type: rouge value: 100.0 ---  # fr-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the GEM/wiki_lingua fr dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge1: 100.0 - Rouge2: 100.0 - Rougel: 100.0 - Rougelsum: 100.0 - Gen Len: 13.9390 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

LarryAIDraw/Positions_Lora32

LarryAIDraw

2023-06-13T12:53:17Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T11:31:39Z

--- license: creativeml-openrail-m --- https://civitai.com/models/54901/norian-hentaicore-lora-extracted

MarcoLYH/bert-base-uncased-finetuned-v2

MarcoLYH

2023-06-13T12:35:30Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T12:12:50Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: MarcoLYH/bert-base-uncased-finetuned-v2 results: [] ---  # MarcoLYH/bert-base-uncased-finetuned-v2 This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6988 - Train End Logits Accuracy: 0.8958 - Train Start Logits Accuracy: 0.7917 - Validation Loss: 0.5760 - Validation End Logits Accuracy: 0.8500 - Validation Start Logits Accuracy: 0.8500 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 3, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.9477 | 0.4375 | 0.5417 | 1.3534 | 0.6000 | 0.7000 | 0 | | 1.7097 | 0.4583 | 0.5208 | 1.0429 | 0.7000 | 0.7000 | 1 | | 1.3797 | 0.6875 | 0.5833 | 0.8816 | 0.75 | 0.7000 | 2 | | 1.1394 | 0.625 | 0.6875 | 0.7849 | 0.75 | 0.7000 | 3 | | 1.0558 | 0.6875 | 0.625 | 0.7057 | 0.8000 | 0.7000 | 4 | | 0.8213 | 0.8542 | 0.8125 | 0.6496 | 0.8500 | 0.75 | 5 | | 0.8661 | 0.8125 | 0.7292 | 0.6152 | 0.8500 | 0.8000 | 6 | | 0.7864 | 0.8542 | 0.7917 | 0.5948 | 0.8500 | 0.8500 | 7 | | 0.8933 | 0.8333 | 0.75 | 0.5822 | 0.8500 | 0.8500 | 8 | | 0.6988 | 0.8958 | 0.7917 | 0.5760 | 0.8500 | 0.8500 | 9 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

clarin-knext/plt5-base-msmarco

clarin-knext

2023-06-13T12:24:51Z

118

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "pl", "arxiv:2305.19840", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-02-21T15:01:26Z

--- license: cc-by-sa-4.0 language: - pl --- Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**. Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf Contact: [email protected]

BlueAvenir/sti_modern_workplace_V_0_1

BlueAvenir

2023-06-13T12:18:56Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-06-13T12:18:27Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{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  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**: `torch.utils.data.dataloader.DataLoader` of length 200 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "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": 200, "warmup_steps": 20, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

ThirdEyeData/Text_Summarization

ThirdEyeData

2023-06-13T11:58:18Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-13T09:27:17Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: Text_Summarization results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1324 ---  # Text_Summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.7235 - Rouge1: 0.1324 - Rouge2: 0.0397 - Rougel: 0.1114 - Rougelsum: 0.111 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 74 | 2.8406 | 0.1286 | 0.04 | 0.1087 | 0.1088 | 19.0 | | No log | 2.0 | 148 | 2.7235 | 0.1324 | 0.0397 | 0.1114 | 0.111 | 19.0 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

yoninazarathy/distilbert-base-uncased-finetuned-cola

yoninazarathy

2023-06-13T11:52:00Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T11:45:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5363967157085073 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8120 - Matthews Correlation: 0.5364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5227 | 1.0 | 535 | 0.5222 | 0.4210 | | 0.3466 | 2.0 | 1070 | 0.5042 | 0.4832 | | 0.2335 | 3.0 | 1605 | 0.5640 | 0.5173 | | 0.1812 | 4.0 | 2140 | 0.7634 | 0.5200 | | 0.1334 | 5.0 | 2675 | 0.8120 | 0.5364 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

MarcoLYH/bert-base-uncased-finetuned-v1

MarcoLYH

2023-06-13T11:51:22Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T11:45:14Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: MarcoLYH/bert-base-uncased-finetuned-v1 results: [] ---  # MarcoLYH/bert-base-uncased-finetuned-v1 This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0187 - Train End Logits Accuracy: 0.6875 - Train Start Logits Accuracy: 0.7083 - Validation Loss: 0.7458 - Validation End Logits Accuracy: 0.75 - Validation Start Logits Accuracy: 0.8000 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.6743 | 0.4375 | 0.5625 | 1.0957 | 0.7000 | 0.7000 | 0 | | 1.1601 | 0.6458 | 0.6458 | 0.8086 | 0.75 | 0.75 | 1 | | 1.0187 | 0.6875 | 0.7083 | 0.7458 | 0.75 | 0.8000 | 2 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

asenella/mnist_svhn_classifiers

asenella

2023-06-13T11:47:00Z

0

null

[ "region:us" ]

null

2023-06-13T11:43:47Z

Classifiers for MNIST and SVHN. Snippet of code on how to load them after downloading the files in `data_path`. ```python import os import torch import torch.nn.functional as F from torch import nn, optim from torch.utils.data import DataLoader from torchvision.datasets import MNIST, SVHN from torchvision.transforms import ToTensor class SVHN_Classifier(nn.Module): def __init__(self): super(SVHN_Classifier, self).__init__() self.conv1 = nn.Conv2d(3, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(500, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 500) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) class MNIST_Classifier(nn.Module): def __init__(self): super(MNIST_Classifier, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def load_mnist_svhn_classifiers(data_path, device="cuda"): c1 = MNIST_Classifier() c1.load_state_dict(torch.load(f"{data_path}/mnist.pt", map_location=device)) c2 = SVHN_Classifier() c2.load_state_dict(torch.load(f"{data_path}/svhn.pt", map_location=device)) return {"mnist": c1.to(device).eval(), "svhn": c2.to(device).eval()} ```

ml-projects/clickbait-ml_model-one

ml-projects

2023-06-13T11:36:59Z

0

keras

[ "keras", "tf-keras", "text-classification", "de", "dataset:ml-projects/clickbait-ml_dataset", "arxiv:1910.09700", "license:openrail", "region:us" ]

text-classification

2023-06-13T11:15:45Z

--- license: openrail language: - de metrics: - accuracy library_name: keras pipeline_tag: text-classification datasets: - ml-projects/clickbait-ml_dataset --- # Clickbait-ML Model One  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  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ml-projects/clickbait-ml_setfit

ml-projects

2023-06-13T11:34:01Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "de", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-06-12T14:21:50Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification language: - de --- # /var/folders/2x/fdpqscbs113ftxcylzlb9sx40000gn/T/tmpjza_ogmp/ml-projects/clickbait-ml-setfit This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("/var/folders/2x/fdpqscbs113ftxcylzlb9sx40000gn/T/tmpjza_ogmp/ml-projects/clickbait-ml-setfit") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

shrinath-suresh/llama-finetune

shrinath-suresh

2023-06-13T11:32:11Z

5

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:shrinath-suresh/blogs-docs-splitted", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-11T14:54:56Z

--- datasets: - shrinath-suresh/blogs-docs-splitted ---

Avenger12321/taxi

Avenger12321

2023-06-13T11:30:24Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T11:30:22Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.73 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="Avenger12321/taxi", 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"]) ```

Avenger12321/q-FrozenLake-v1-4x4-noSlippery

Avenger12321

2023-06-13T11:28:04Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T11:28:03Z

--- 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="Avenger12321/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"]) ```

MarcoLYH/distilbert-base-uncased-finetuned-v4

MarcoLYH

2023-06-13T11:24:35Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T10:43:34Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v4 results: [] ---  # MarcoLYH/distilbert-base-uncased-finetuned-v4 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6362 - Train End Logits Accuracy: 0.8125 - Train Start Logits Accuracy: 0.8333 - Validation Loss: 0.5952 - Validation End Logits Accuracy: 0.9000 - Validation Start Logits Accuracy: 0.8000 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 3, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.8589 | 0.4792 | 0.5625 | 1.3334 | 0.6500 | 0.7000 | 0 | | 1.5485 | 0.5417 | 0.5625 | 0.9904 | 0.6500 | 0.8000 | 1 | | 1.0423 | 0.7292 | 0.7292 | 0.8812 | 0.7000 | 0.8000 | 2 | | 1.1265 | 0.7083 | 0.7917 | 0.7830 | 0.75 | 0.8000 | 3 | | 0.8686 | 0.75 | 0.7292 | 0.7052 | 0.8000 | 0.8500 | 4 | | 0.6936 | 0.8542 | 0.8333 | 0.6547 | 0.8000 | 0.8500 | 5 | | 0.6408 | 0.875 | 0.8542 | 0.6239 | 0.9000 | 0.8000 | 6 | | 0.7243 | 0.8333 | 0.7083 | 0.6059 | 0.9000 | 0.8000 | 7 | | 0.6146 | 0.8333 | 0.8958 | 0.5976 | 0.9000 | 0.8000 | 8 | | 0.6362 | 0.8125 | 0.8333 | 0.5952 | 0.9000 | 0.8000 | 9 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

trumanplus/trumanplus

trumanplus

2023-06-13T11:07:26Z

0

allennlp

[ "allennlp", "finance", "Health", "license:openrail", "region:us" ]

null

2023-06-13T10:59:48Z

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nyxabhi/my_awesome_qa_model

nyxabhi

2023-06-13T11:02:44Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-12T08:55:28Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model results: [] ---  # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.7171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.7072 | | 2.9316 | 2.0 | 500 | 1.8371 | | 2.9316 | 3.0 | 750 | 1.7171 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.3

KrishnAI7/autotrain-aniai1-66240136433

KrishnAI7

2023-06-13T10:42:25Z

107

0

transformers

[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "en", "dataset:KrishnAI7/autotrain-data-aniai1", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T10:41:59Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain" datasets: - KrishnAI7/autotrain-data-aniai1 co2_eq_emissions: emissions: 0.0473575460314297 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 66240136433 - CO2 Emissions (in grams): 0.0474 ## Validation Metrics - Loss: 2.632 - Accuracy: 0.100 - Macro F1: 0.013 - Micro F1: 0.100 - Weighted F1: 0.018 - Macro Precision: 0.007 - Micro Precision: 0.100 - Weighted Precision: 0.010 - Macro Recall: 0.071 - Micro Recall: 0.100 - Weighted Recall: 0.100 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/KrishnAI7/autotrain-aniai1-66240136433 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("KrishnAI7/autotrain-aniai1-66240136433", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("KrishnAI7/autotrain-aniai1-66240136433", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

wootwoot/abyssorangemix3-popupparade-fp16

wootwoot

2023-06-13T10:42:14Z

156

2

diffusers

[ "diffusers", "safetensors", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-12T14:43:01Z

--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image --- ### Based off [WarriorMama777/OrangeMixs](https://huggingface.co/WarriorMama777/OrangeMixs) All credits go to the original author and all the author of AbyssOrangeMix3's ancestor models ### Merged with [Pop Up Parade](https://civitai.com/models/78997) ### Diffusers The original AbyssOrangeMix3 model converted to be used with the [🧨Diffusers library](https://github.com/huggingface/diffusers)

BlueAvenir/sti_cyber_security_model_updated

BlueAvenir

2023-06-13T10:31:23Z

16

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-06-12T04:13:00Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{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  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**: `torch.utils.data.dataloader.DataLoader` of length 200 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "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": 200, "warmup_steps": 20, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

kevinpro/Vicuna-13B-CoT_v2

kevinpro

2023-06-13T10:25:04Z

0

1

null

[ "en", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]

null

2023-06-12T15:35:02Z

--- license: apache-2.0 language: - en --- # Model Card for Model ID SFT to enhance the CoT capabiliy of Vicuna. We tune the model on 55W "CoT Capabiliy" related Instruction Data If you find the model helpful, please click "like" to support us. We also welcome feedback on your usage experience and any issues you encounter in the issues section. ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

facebook/mms-lid-1024

facebook

2023-06-13T10:18:46Z

2,350

9

transformers

[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-06-13T08:59:15Z

--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 1024 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 1024 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 1024 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-1024" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 1024 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - kmr - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kik - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - cak - fao - glg - ltz - xog - lao - mlt - sin - aka - sna - che - mam - ita - quc - aiw - srp - mri - tuv - nno - pus - eus - kbp - gur - ory - lug - crh - bre - luo - nhx - slk - ewe - xsm - fin - rif - dan - saq - yid - yao - mos - quh - hne - xon - new - dtp - quy - est - ddn - dyu - ttq - bam - pse - uig - sck - ngl - tso - mup - dga - seh - lis - wal - ctg - mip - bfz - bxk - ceb - kru - war - khg - bbc - thl - nzi - vmw - mzi - ycl - zne - sid - asa - tpi - bmq - box - zpu - gof - nym - cla - bgq - bfy - hlb - qxl - teo - fon - sda - kfx - bfa - mag - tzh - pil - maj - maa - kdt - ksb - lns - btd - rej - pap - ayr - any - mnk - adx - gud - krc - onb - xal - ctd - nxq - ava - blt - lbw - hyw - udm - zar - tzo - kpv - san - xnj - kek - chv - kcg - kri - ati - bgw - mxt - ybb - btx - dgi - nhy - dnj - zpz - yba - lon - smo - men - ium - mgd - taq - nga - nsu - zaj - tly - prk - zpt - akb - mhr - mxb - nuj - obo - kir - bom - run - zpg - hwc - mnw - ubl - kin - xtm - hnj - mpm - rkt - miy - luc - mih - kne - mib - flr - myv - xmm - knk - iba - gux - pis - zmz - ses - dav - lif - qxr - dig - kdj - wsg - tir - gbm - mai - zpc - kus - nyy - mim - nan - nyn - gog - ngu - tbz - hoc - nyf - sus - guk - gwr - yaz - bcc - sbd - spp - hak - grt - kno - oss - suk - spy - nij - lsm - kaa - bem - rmy - kqn - nim - ztq - nus - bib - xtd - ach - mil - keo - mpg - gjn - zaq - kdh - dug - sah - awa - kff - dip - rim - nhe - pcm - kde - tem - quz - mfq - las - bba - kbr - taj - dyo - zao - lom - shk - dik - dgo - zpo - fij - bgc - xnr - bud - kac - laj - mev - maw - quw - kao - dag - ktb - lhu - zab - mgh - shn - otq - lob - pbb - oci - zyb - bsq - mhi - dzo - zas - guc - alz - ctu - wol - guw - mnb - nia - zaw - mxv - bci - sba - kab - dwr - nnb - ilo - mfe - srx - ruf - srn - zad - xpe - pce - ahk - bcl - myk - haw - mad - ljp - bky - gmv - nag - nav - nyo - kxm - nod - sag - zpl - sas - myx - sgw - old - irk - acf - mak - kfy - zai - mie - zpm - zpi - ote - jam - kpz - lgg - lia - nhi - mzm - bdq - xtn - mey - mjl - sgj - kdi - kxc - miz - adh - tap - hay - kss - pam - gor - heh - nhw - ziw - gej - yua - itv - shi - qvw - mrw - hil - mbt - pag - vmy - lwo - cce - kum - klu - ann - mbb - npl - zca - pww - toc - ace - mio - izz - kam - zaa - krj - bts - eza - zty - hns - kki - min - led - alw - tll - rng - pko - toi - iqw - ncj - toh - umb - mog - hno - wob - gxx - hig - nyu - kby - ban - syl - bxg - nse - xho - zae - mkw - nch - ibg - mas - qvz - bum - bgd - mww - epo - tzm - zul - bcq - lrc - xdy - tyv - ibo - loz - mza - abk - azz - guz - arn - ksw - lus - tos - gvr - top - ckb - mer - pov - lun - rhg - knc - sfw - bev - tum - lag - nso - bho - ndc - maf - gkp - bax - awn - ijc - qug - lub - srr - mni - zza - ige - dje - mkn - bft - tiv - otn - kck - kqs - gle - lua - pdt - swk - mgw - ebu - ada - lic - skr - gaa - mfa - vmk - mcn - bto - lol - bwr - unr - dzg - hdy - kea - bhi - glk - mua - ast - nup - sat - ktu - bhb - zpq - coh - bkm - gya - sgc - dks - ncl - tui - emk - urh - ego - ogo - tsc - idu - igb - ijn - njz - ngb - tod - jra - mrt - zav - tke - its - ady - bzw - kng - kmb - lue - jmx - tsn - bin - ble - gom - ven - sef - sco - her - iso - trp - glv - haq - toq - okr - kha - wof - rmn - sot - kaj - bbj - sou - mjt - trd - gno - mwn - igl - rag - eyo - div - efi - nde - mfv - mix - rki - kjg - fan - khw - wci - bjn - pmy - bqi - ina - hni - mjx - kuj - aoz - the - tog - tet - nuz - ajg - ccp - mau - ymm - fmu - tcz - xmc - nyk - ztg - knx - snk - zac - esg - srb - thq - pht - wes - rah - pnb - ssy - zpv - kpo - phr - atd - eto - xta - mxx - mui - uki - tkt - mgp - xsq - enq - nnh - qxp - zam - bug - bxr - maq - tdt - khb - mrr - kas - zgb - kmw - lir - vah - dar - ssw - hmd - jab - iii - peg - shr - brx - rwr - bmb - kmc - mji - dib - pcc - nbe - mrd - ish - kai - yom - zyn - hea - ewo - bas - hms - twh - kfq - thr - xtl - wbr - bfb - wtm - mjc - blk - lot - dhd - swv - wbm - zzj - kge - mgm - niq - zpj - bwx - bde - mtr - gju - kjp - mbz - haz - lpo - yig - qud - shy - gjk - ztp - nbl - aii - kun - say - mde - sjp - bns - brh - ywq - msi - anr - mrg - mjg - tan - tsg - tcy - kbl - mdr - mks - noe - tyz - zpa - ahr - aar - wuu - khr - kbd - kex - bca - nku - pwr - hsn - ort - ott - swi - kua - tdd - msm - bgp - nbm - mxy - abs - zlj - ebo - lea - dub - sce - xkb - vav - bra - ssb - sss - nhp - kad - kvx - lch - tts - zyj - kxp - lmn - qvi - lez - scl - cqd - ayb - xbr - nqg - dcc - cjk - bfr - zyg - mse - gru - mdv - bew - wti - arg - dso - zdj - pll - mig - qxs - bol - drs - anp - chw - bej - vmc - otx - xty - bjj - vmz - ibb - gby - twx - tig - thz - tku - hmz - pbm - mfn - nut - cyo - mjw - cjm - tlp - naq - rnd - stj - sym - jax - btg - tdg - sng - nlv - kvr - pch - fvr - mxs - wni - mlq - kfr - mdj - osi - nhn - ukw - tji - qvj - nih - bcy - hbb - zpx - hoj - cpx - ogc - cdo - bgn - bfs - vmx - tvn - ior - mxa - btm - anc - jit - mfb - mls - ets - goa - bet - ikw - pem - trf - daq - max - rad - njo - bnx - mxl - mbi - nba - zpn - zts - mut - hnd - mta - hav - hac - ryu - abr - yer - cld - zag - ndo - sop - vmm - gcf - chr - cbk - sbk - bhp - odk - mbd - nap - gbr - mii - czh - xti - vls - gdx - sxw - zaf - wem - mqh - ank - yaf - vmp - otm - sdh - anw - src - mne - wss - meh - kzc - tma - ttj - ots - ilp - zpr - saz - ogb - akl - nhg - pbv - rcf - cgg - mku - bez - mwe - mtb - gul - ifm - mdh - scn - lki - xmf - sgd - aba - cos - luz - zpy - stv - kjt - mbf - kmz - nds - mtq - tkq - aee - knn - mbs - mnp - ema - bar - unx - plk - psi - mzn - cja - sro - mdw - ndh - vmj - zpw - kfu - bgx - gsw - fry - zpe - zpd - bta - psh - zat </details> ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 1024 languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)

DuhAlbared/autotrain-dru-textclassification-66230136430

DuhAlbared

2023-06-13T10:16:24Z

110

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "autotrain", "ar", "dataset:DuhAlbared/autotrain-data-dru-textclassification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T10:13:57Z

--- tags: - autotrain - text-classification language: - ar widget: - text: "I love AutoTrain" datasets: - DuhAlbared/autotrain-data-dru-textclassification co2_eq_emissions: emissions: 1.2953982175499286 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 66230136430 - CO2 Emissions (in grams): 1.2954 ## Validation Metrics - Loss: 0.296 - Accuracy: 0.916 - Macro F1: 0.916 - Micro F1: 0.916 - Weighted F1: 0.916 - Macro Precision: 0.917 - Micro Precision: 0.916 - Weighted Precision: 0.917 - Macro Recall: 0.916 - Micro Recall: 0.916 - Weighted Recall: 0.916 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/DuhAlbared/autotrain-dru-textclassification-66230136430 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("DuhAlbared/autotrain-dru-textclassification-66230136430", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("DuhAlbared/autotrain-dru-textclassification-66230136430", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

facebook/mms-lid-512

facebook

2023-06-13T10:16:19Z

216

2

transformers

[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-06-13T08:59:08Z

--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 512 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 512 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 512 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-512" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 512 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - kmr - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kik - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - cak - fao - glg - ltz - xog - lao - mlt - sin - aka - sna - che - mam - ita - quc - srp - mri - tuv - nno - pus - eus - kbp - ory - lug - bre - luo - nhx - slk - ewe - fin - rif - dan - yid - yao - mos - quh - hne - xon - new - quy - est - dyu - ttq - bam - pse - uig - sck - ngl - tso - mup - dga - seh - lis - wal - ctg - bfz - bxk - ceb - kru - war - khg - bbc - thl - vmw - zne - sid - tpi - nym - bgq - bfy - hlb - teo - fon - kfx - bfa - mag - ayr - any - mnk - adx - ava - hyw - san - kek - chv - kri - btx - nhy - dnj - lon - men - ium - nga - nsu - prk - kir - bom - run - hwc - mnw - ubl - kin - rkt - xmm - iba - gux - ses - wsg - tir - gbm - mai - nyy - nan - nyn - gog - ngu - hoc - nyf - sus - bcc - hak - grt - suk - nij - kaa - bem - rmy - nus - ach - awa - dip - rim - nhe - pcm - kde - tem - quz - bba - kbr - taj - dik - dgo - bgc - xnr - kac - laj - dag - ktb - mgh - shn - oci - zyb - alz - wol - guw - nia - bci - sba - kab - nnb - ilo - mfe - xpe - bcl - haw - mad - ljp - gmv - nyo - kxm - nod - sag - sas - myx - sgw - mak - kfy - jam - lgg - nhi - mey - sgj - hay - pam - heh - nhw - yua - shi - mrw - hil - pag - cce - npl - ace - kam - min - pko - toi - ncj - umb - hno - ban - syl - bxg - nse - xho - mkw - nch - mas - bum - mww - epo - tzm - zul - lrc - ibo - abk - azz - guz - ksw - lus - ckb - mer - pov - rhg - knc - tum - nso - bho - ndc - ijc - qug - lub - srr - mni - zza - dje - tiv - gle - lua - swk - ada - lic - skr - mfa - bto - unr - hdy - kea - glk - ast - nup - sat - ktu - bhb - sgc - dks - ncl - emk - urh - tsc - idu - igb - its - kng - kmb - tsn - bin - gom - ven - sef - sco - trp - glv - haq - kha - rmn - sot - sou - gno - igl - efi - nde - rki - kjg - fan - wci - bjn - pmy - bqi - ina - hni - the - nuz - ajg - ymm - fmu - nyk - snk - esg - thq - pht - wes - pnb - phr - mui - tkt - bug - mrr - kas - zgb - lir - vah - ssw - iii - brx - rwr - kmc - dib - pcc - zyn - hea - hms - thr - wbr - bfb - wtm - blk - dhd - swv - zzj - niq - mtr - gju - kjp - haz - shy - nbl - aii - sjp - bns - brh - msi - tsg - tcy - kbl - noe - tyz - ahr - aar - wuu - kbd - bca - pwr - hsn - kua - tdd - bgp - abs - zlj - ebo - bra - nhp - tts - zyj - lmn - cqd - dcc - cjk - bfr - bew - arg - drs - chw - bej - bjj - ibb - tig - nut - jax - tdg - nlv - pch - fvr - mlq - kfr - nhn - tji - hoj - cpx - cdo - bgn - btm - trf - daq - max - nba - mut - hnd - ryu - abr - sop - odk - nap - gbr - czh - vls - gdx - yaf - sdh - anw - ttj - nhg - cgg - ifm - mdh - scn - lki - luz - stv - kmz - nds - mtq - knn - mnp - bar - mzn - gsw - fry </details> ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 512 languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)

facebook/mms-lid-126

facebook

2023-06-13T10:15:48Z

3,043

26

transformers

[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "mms", "ab", "af", "ak", "am", "ar", "as", "av", "ay", "az", "ba", "bm", "be", "bn", "bi", "bo", "sh", "br", "bg", "ca", "cs", "ce", "cv", "ku", "cy", "da", "de", "dv", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fa", "fj", "fi", "fr", "fy", "ff", "ga", "gl", "gn", "gu", "zh", "ht", "ha", "he", "hi", "hu", "hy", "ig", "ia", "ms", "is", "it", "jv", "ja", "kn", "ka", "kk", "kr", "km", "ki", "rw", "ky", "ko", "kv", "lo", "la", "lv", "ln", "lt", "lb", "lg", "mh", "ml", "mr", "mk", "mg", "mt", "mn", "mi", "my", "nl", "no", "ne", "ny", "oc", "om", "or", "os", "pa", "pl", "pt", "ps", "qu", "ro", "rn", "ru", "sg", "sk", "sl", "sm", "sn", "sd", "so", "es", "sq", "su", "sv", "sw", "ta", "tt", "te", "tg", "tl", "th", "ti", "ts", "tr", "uk", "vi", "wo", "xh", "yo", "zu", "za", "dataset:google/fleurs", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-06-13T08:44:05Z

--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - acc --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 126 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 126 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 126 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-126" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 126 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - fao - glg - ltz - lao - mlt - sin - sna - ita - srp - mri - nno - pus - eus - ory - lug - bre - luo - slk - fin - dan - yid - est - ceb - war - san - kir - oci - wol - haw - kam - umb - xho - epo - zul - ibo - abk - ckb - nso - gle - kea - ast - sco - glv - ina </details> ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 126 languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)

hasu234/image_classifier

hasu234

2023-06-13T10:15:18Z

0

null

[ "image-classification", "en", "license:apache-2.0", "region:us" ]

image-classification

2023-06-13T09:50:12Z

--- license: apache-2.0 language: - en pipeline_tag: image-classification --- This is the model for [https://github.com/hasu234/SDPDSSample](https://github.com/hasu234/SDPDSSample). this repository. This classifier can classify between ```dog```, ```berry```, ```flower``` and ```bird``` images. ![Sample Dataset](images/dataset.png) ## Dependencies * Python 3.9 ## Setting up conda Environment * Clone the repository by running ``` git clone https://github.com/hasu234/SDPDSSample.git ``` * Change current directory to SDPDSSample ``` cd SDPDSSample ``` * Create a conda environmet ``` conda create -n myenv python=3.9 ``` * Activate the environment ``` conda activate myenv ``` * Install the required library from ```requirment.txt``` by running ``` pip install -r requirmen.txt ``` * Or, create a conda environment from ```environment.yml``` by running ``` conda env create -f environment.yml ``` ## Trining your data * To train your own dataset Make sure you have a data folder having the same folder hiararchy like below ``` ├── dataset | ├── train │ │ ├── class1 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class2 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class3 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class4 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg | ├── test │ │ ├── class1 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class2 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class3 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg │ │ ├── class4 │ │ │ ├──image1.jpg │ │ │ ├──image2.jpg ``` or make some chainges on ```train.py``` according to to your dataset directory. * Make sure you are in the project directory and run the ```train.py``` script with the folder directory of your dataset ``` python train.py /path/to/dataset_directory ``` ## Running inference * To run the inference on your test data make sure you downloaded the pretrained model from [this link](https://drive.google.com/uc?id=197Kuuo4LhHunYLgGKfGeouNTL0WguP0T&export=download). * Then run the ```infer.py``` script from terminal specifying the test image location and downloaded pretrained model location ``` python infer.py path/to/image.jpg path/to/model.pth ``` ## Running on Docker * Clone the repository by running ``` git clone https://github.com/hasu234/SDPDSSample.git ``` * Change current directory to SDPDSSample ``` cd SDPDSSample ``` Before Building the docker image transfer the files (model, test image, dataset) to current working directory if you don't want to deal with docker volume * Build the Docker image by running ``` docker build -t sdpdsample . ``` * Run the docker image ``` docker run -d sdpdsample ``` if the container failed to run in background, run it on foreground using ```docker run -it sdpdsample``` then exit to get the running container id * Get the container id ``` docker ps ``` * Getting inside the container ``` docker exec -it <container id> bash ``` You will get a Linux like command-line interface * Running the project ``` # for training your data python train.py /path/to/dataset_directory # for running inference python infer.py path/to/image.jpg path/to/model.pth ```

MarcoLYH/distilbert-base-uncased-finetuned-v2

MarcoLYH

2023-06-13T09:53:44Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T09:47:30Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v2 results: [] ---  # MarcoLYH/distilbert-base-uncased-finetuned-v2 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8512 - Train End Logits Accuracy: 0.7917 - Train Start Logits Accuracy: 0.7708 - Validation Loss: 0.9185 - Validation End Logits Accuracy: 0.7000 - Validation Start Logits Accuracy: 0.8000 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 2.0417 | 0.4583 | 0.5833 | 1.2444 | 0.6000 | 0.7000 | 0 | | 1.5102 | 0.5625 | 0.6875 | 1.0279 | 0.7000 | 0.75 | 1 | | 1.1881 | 0.6458 | 0.6875 | 0.9774 | 0.7000 | 0.8000 | 2 | | 1.1344 | 0.6875 | 0.6875 | 0.9360 | 0.7000 | 0.8000 | 3 | | 0.8512 | 0.7917 | 0.7708 | 0.9185 | 0.7000 | 0.8000 | 4 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

paumena/QA-DistilBERT

paumena

2023-06-13T09:52:16Z

61

0

transformers

[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T08:07:51Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: paumena/QA-DistilBERT results: [] ---  # paumena/QA-DistilBERT This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4747 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Evaluation metrics {'exact_match': 76.27246925260171, 'f1': 84.99301423719345} ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.4947 | 0 | | 0.9554 | 1 | | 0.7266 | 2 | | 0.5744 | 3 | | 0.4747 | 4 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

MarcoLYH/distilbert-base-uncased-finetuned-v1

MarcoLYH

2023-06-13T09:39:02Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T09:35:58Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MarcoLYH/distilbert-base-uncased-finetuned-v1 results: [] ---  # MarcoLYH/distilbert-base-uncased-finetuned-v1 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4309 - Train End Logits Accuracy: 0.625 - Train Start Logits Accuracy: 0.6042 - Validation Loss: 1.1252 - Validation End Logits Accuracy: 0.7000 - Validation Start Logits Accuracy: 0.75 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.8703 | 0.4792 | 0.5625 | 1.3666 | 0.6500 | 0.75 | 0 | | 1.2717 | 0.6875 | 0.6875 | 1.1863 | 0.7000 | 0.75 | 1 | | 1.4309 | 0.625 | 0.6042 | 1.1252 | 0.7000 | 0.75 | 2 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

slone/mbart-large-51-myv-mul-v1

slone

2023-06-13T09:38:10Z

134

1

transformers

[ "transformers", "pytorch", "safetensors", "mbart", "text2text-generation", "erzya", "mordovian", "translation", "myv", "ru", "fi", "de", "es", "en", "hi", "zh", "tr", "uk", "fr", "ar", "dataset:slone/myv_ru_2022", "dataset:yhavinga/ccmatrix", "arxiv:2209.09368", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-09-15T05:55:21Z

--- language: - myv - ru - fi - de - es - en - hi - zh - tr - uk - fr - ar tags: - erzya - mordovian - translation license: cc-by-sa-4.0 datasets: - slone/myv_ru_2022 - yhavinga/ccmatrix --- This a model to translate texts to the Erzya language (`myv`, cyrillic script) from 11 other languages: `ru,fi,de,es,en,hi,zh,tr,uk,fr,ar`. See its [demo](https://huggingface.co/spaces/slone/myv-translation-2022-demo)! It is described in the paper [The first neural machine translation system for the Erzya language](https://arxiv.org/abs/2209.09368). This model is based on [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50), but with updated vocabulary and checkpoint: - Added an extra language token `myv_XX` and 19K new BPE tokens for the Erzya language; - Fine-tuned to translate from Erzya: first to Russian, then to all 11 languages. The following code can be used to run translation using the model ```Python from transformers import MBartForConditionalGeneration, MBart50Tokenizer def fix_tokenizer(tokenizer): """ Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """ old_len = len(tokenizer) - int('myv_XX' in tokenizer.added_tokens_encoder) tokenizer.lang_code_to_id['myv_XX'] = old_len-1 tokenizer.id_to_lang_code[old_len-1] = 'myv_XX' tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id) tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()} if 'myv_XX' not in tokenizer._additional_special_tokens: tokenizer._additional_special_tokens.append('myv_XX') tokenizer.added_tokens_encoder = {} def translate(text, model, tokenizer, src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False, n_out=None, **kwargs): tokenizer.src_lang = src encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024) if max_length == 'auto': max_length = int(32 + 1.5 * encoded.input_ids.shape[1]) if train_mode: model.train() else: model.eval() generated_tokens = model.generate( **encoded.to(model.device), forced_bos_token_id=tokenizer.lang_code_to_id[trg], max_length=max_length, num_beams=num_beams, repetition_penalty=repetition_penalty, num_return_sequences=n_out or 1, **kwargs ) out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) if isinstance(text, str) and n_out is None: return out[0] return out mname = 'slone/mbart-large-51-myv-mul-v1' model = MBartForConditionalGeneration.from_pretrained(mname) tokenizer = MBart50Tokenizer.from_pretrained(mname) fix_tokenizer(tokenizer) print(translate('Шумбрат, киска!', model, tokenizer, src='myv_XX', trg='ru_RU')) # Привет, собака! # действительно, "киска" с эрзянского переводится именно так print(translate('Шумбрат, киска!', model, tokenizer, src='myv_XX', trg='en_XX')) # Hi, dog! ```

yswill/LLaVA-13b-delta-v1-1

yswill

2023-06-13T09:36:03Z

12

0

transformers

[ "transformers", "pytorch", "llava", "text-generation", "license:apache-2.0", "autotrain_compatible", "region:us" ]

text-generation

2023-06-13T09:18:24Z

--- license: apache-2.0 inference: false --- **NOTE: This "delta model" cannot be used directly.** Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights. See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. **Model date:** LLaVA was trained in May 2023. **Paper or resources for more information:** https://llava-vl.github.io/ **License:** Apache License 2.0 **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset 595K filtered image-text pairs from CC3M. 150K GPT-generated multimodal instruction-following data. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.

OpenDILabCommunity/LunarLander-v2-PPOOffPolicy

OpenDILabCommunity

2023-06-13T09:35:16Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "LunarLander-v2", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-06-13T09:35:07Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - LunarLander-v2 benchmark_name: OpenAI/Gym/Box2d task_name: LunarLander-v2 pipeline_tag: reinforcement-learning model-index: - name: PPOOffPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Box2d-LunarLander-v2 type: OpenAI/Gym/Box2d-LunarLander-v2 metrics: - type: mean_reward value: 240.63 +/- 20.68 name: mean_reward --- # Play **LunarLander-v2** with **PPOOffPolicy** Policy ## Model Description  This is a simple **PPOOffPolicy** implementation to OpenAI/Gym/Box2d **LunarLander-v2** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import PPOOffPolicyAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py")) # Instantiate the agent agent = PPOOffPolicyAgent( env="lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import PPOOffPolicyAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/LunarLander-v2-PPOOffPolicy") # Instantiate the agent agent = PPOOffPolicyAgent( env="lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import PPOOffPolicyAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = PPOOffPolicyAgent("lunarlander_discrete", exp_name="LunarLander-v2-PPOOffPolicy") # Train the agent return_ = agent.train(step=int(4000000), collector_env_num=4, evaluator_env_num=4) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Box2d", task_name="LunarLander-v2", algo_name="PPOOffPolicy", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/ppo.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./ppo_offpolicy/lunarlander_ppo_offpolicy_deploy.py", usage_file_by_huggingface_ding="./ppo_offpolicy/lunarlander_ppo_offpolicy_download.py", train_file="./ppo_offpolicy/lunarlander_ppo_offpolicy.py", repo_id="OpenDILabCommunity/LunarLander-v2-PPOOffPolicy" ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 240, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'LunarLander-v2' }, 'policy': { 'model': { 'obs_shape': 8, 'action_shape': 4 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 4, 'batch_size': 64, 'learning_rate': 0.001, 'value_weight': 0.5, 'entropy_weight': 0.01, 'clip_ratio': 0.2, 'adv_norm': True, 'ignore_done': False, 'nstep': 1, 'nstep_return': False }, 'collect': { 'collector': {}, 'unroll_len': 1, 'discount_factor': 0.99, 'gae_lambda': 0.95, 'n_sample': 128 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 240, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 10000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'ppo', 'priority': False, 'priority_IS_weight': False, 'nstep_return': False, 'nstep': 3, 'transition_with_policy_data': True, 'cfg_type': 'PPOOffPolicyDict' }, 'exp_name': 'LunarLander-v2-PPOOffPolicy', 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False }, 'seed': 0 } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/LunarLander-v2-PPOOffPolicy) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/ppo.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-PPOOffPolicy/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-PPOOffPolicy/blob/main/replay.mp4)  - **Parameters total size:** 371.29 KB - **Last Update Date:** 2023-06-13 ## Environments  - **Benchmark:** OpenAI/Gym/Box2d - **Task:** LunarLander-v2 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.8 - **PyTorch version:** 1.7.1 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html)

Josias-Ounsinli/my_awesome_model32

Josias-Ounsinli

2023-06-13T09:33:36Z

62

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T09:14:34Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model32 results: [] ---  # Josias-Ounsinli/my_awesome_model32 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0986 - Validation Loss: 1.0986 - Train Accuracy: 0.3333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 1.0986 | 1.0986 | 0.3333 | 0 | | 1.0986 | 1.0986 | 0.3333 | 1 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

kejolong/SNI2017

kejolong

2023-06-13T09:26:34Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T09:22:53Z

--- license: creativeml-openrail-m ---

Nayan924/carmine

Nayan924

2023-06-13T09:23:58Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-06-13T09:23:58Z

--- license: cc-by-nc-nd-4.0 ---

Jamli/AmeliaLoRa

Jamli

2023-06-13T09:15:22Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T09:07:01Z

--- license: creativeml-openrail-m ---

addy88/bert-finetuned-bpmn

addy88

2023-06-13T09:15:21Z

120

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-13T09:06:45Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-bpmn results: [] ---  # bert-finetuned-bpmn 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: 0.3456 - Precision: 0.8113 - Recall: 0.86 - F1: 0.8350 - Accuracy: 0.9341 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.2716 | 0.7778 | 0.84 | 0.8077 | 0.9115 | | No log | 2.0 | 20 | 0.2428 | 0.7669 | 0.8333 | 0.7987 | 0.9160 | | No log | 3.0 | 30 | 0.2726 | 0.7875 | 0.84 | 0.8129 | 0.9205 | | No log | 4.0 | 40 | 0.2658 | 0.7862 | 0.8333 | 0.8091 | 0.9214 | | No log | 5.0 | 50 | 0.2470 | 0.7914 | 0.86 | 0.8243 | 0.9268 | | No log | 6.0 | 60 | 0.2745 | 0.7791 | 0.8467 | 0.8115 | 0.9250 | | No log | 7.0 | 70 | 0.3415 | 0.8280 | 0.8667 | 0.8469 | 0.9259 | | No log | 8.0 | 80 | 0.3524 | 0.775 | 0.8267 | 0.8000 | 0.9178 | | No log | 9.0 | 90 | 0.3307 | 0.8313 | 0.8867 | 0.8581 | 0.9322 | | No log | 10.0 | 100 | 0.3161 | 0.7778 | 0.84 | 0.8077 | 0.9214 | | No log | 11.0 | 110 | 0.3646 | 0.8387 | 0.8667 | 0.8525 | 0.9322 | | No log | 12.0 | 120 | 0.3262 | 0.7925 | 0.84 | 0.8155 | 0.9223 | | No log | 13.0 | 130 | 0.3436 | 0.8462 | 0.88 | 0.8627 | 0.9350 | | No log | 14.0 | 140 | 0.3427 | 0.8516 | 0.88 | 0.8656 | 0.9377 | | No log | 15.0 | 150 | 0.3163 | 0.7950 | 0.8533 | 0.8232 | 0.9322 | | No log | 16.0 | 160 | 0.3233 | 0.8291 | 0.8733 | 0.8506 | 0.9377 | | No log | 17.0 | 170 | 0.3354 | 0.8050 | 0.8533 | 0.8285 | 0.9322 | | No log | 18.0 | 180 | 0.3468 | 0.8291 | 0.8733 | 0.8506 | 0.9341 | | No log | 19.0 | 190 | 0.3457 | 0.8176 | 0.8667 | 0.8414 | 0.9341 | | No log | 20.0 | 200 | 0.3456 | 0.8113 | 0.86 | 0.8350 | 0.9341 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

franfj/media-bias-ukraine-dataset-all-removed

franfj

2023-06-13T09:08:29Z

115

0

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-11T23:45:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: media-bias-ukraine-dataset-all-removed results: [] ---  # media-bias-ukraine-dataset-all-removed 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: 0.1717 - F1: 0.8014 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3845 | 1.0 | 114 | 0.2296 | 0.6101 | | 0.1412 | 2.0 | 228 | 0.1759 | 0.7486 | | 0.0215 | 3.0 | 342 | 0.2275 | 0.7439 | | 0.0506 | 4.0 | 456 | 0.2064 | 0.7651 | | 0.0366 | 5.0 | 570 | 0.1717 | 0.8014 | | 0.2428 | 6.0 | 684 | 0.1955 | 0.7878 | | 0.005 | 7.0 | 798 | 0.2297 | 0.7839 | | 0.003 | 8.0 | 912 | 0.2428 | 0.8005 | | 0.0037 | 9.0 | 1026 | 0.2577 | 0.7884 | | 0.0099 | 10.0 | 1140 | 0.2641 | 0.7957 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2

Josias-Ounsinli/my_awesome_model39

Josias-Ounsinli

2023-06-13T08:53:12Z

62

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T08:33:51Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model39 results: [] ---  # Josias-Ounsinli/my_awesome_model39 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5308 - Validation Loss: 0.6272 - Train Accuracy: 0.7307 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 7500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6912 | 0.6544 | 0.7063 | 0 | | 0.5308 | 0.6272 | 0.7307 | 1 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

ThirdEyeData/Question_Answer

ThirdEyeData

2023-06-13T08:49:37Z

104

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T08:39:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: Question_Answer results: [] ---  # Question_Answer This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 5.2904 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 10 | 5.6647 | | No log | 2.0 | 20 | 5.4060 | | No log | 3.0 | 30 | 5.2904 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cpu - Datasets 2.12.0 - Tokenizers 0.13.3

srb1smo/lizard

srb1smo

2023-06-13T08:44:41Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T08:44:41Z

--- license: creativeml-openrail-m ---

Josias-Ounsinli/my_awesome_model30

Josias-Ounsinli

2023-06-13T08:39:36Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T08:00:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model30 results: [] ---  # Josias-Ounsinli/my_awesome_model30 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6122 - Validation Loss: 0.6266 - Train Accuracy: 0.7293 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 3750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.7425 | 0.6462 | 0.7127 | 0 | | 0.6122 | 0.6266 | 0.7293 | 1 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Josias-Ounsinli/my_awesome_model2

Josias-Ounsinli

2023-06-13T08:37:32Z

63

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-12T10:29:57Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model2 results: [] ---  # Josias-Ounsinli/my_awesome_model2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Validation Loss: nan - Train Accuracy: 0.3333 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | nan | nan | 0.3333 | 0 | | nan | nan | 0.3333 | 1 | | nan | nan | 0.3333 | 2 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

calculus7e/distilbert-base-uncased-finetuned-emotion

calculus7e

2023-06-13T08:33:42Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T08:11:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9248071018161593 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2155 - Accuracy: 0.9245 - F1: 0.9248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3054 | 0.906 | 0.9044 | | 0.2439 | 2.0 | 500 | 0.2155 | 0.9245 | 0.9248 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Josias-Ounsinli/my_awesome_model40

Josias-Ounsinli

2023-06-13T08:29:15Z

62

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T07:58:55Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Josias-Ounsinli/my_awesome_model40 results: [] ---  # Josias-Ounsinli/my_awesome_model40 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5230 - Validation Loss: 0.6182 - Train Accuracy: 0.728 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5625, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.7533 | 0.6294 | 0.727 | 0 | | 0.5619 | 0.6182 | 0.728 | 1 | | 0.5230 | 0.6182 | 0.728 | 2 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

undrwolf/taxi-RL-agent

undrwolf

2023-06-13T08:24:09Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T08:24:06Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-RL-agent 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="undrwolf/taxi-RL-agent", 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"]) ```

undrwolf/q-FrozenLake-v1-4x4-noSlippery

undrwolf

2023-06-13T08:18:29Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T08:18:26Z

--- 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="undrwolf/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"]) ```

ikasou/ppo-LunarLander-v2

ikasou

2023-06-13T08:17:07Z

8

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-31T16:53:23Z

--- 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: 292.00 +/- 15.21 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 ... ```

ThirdEyeData/Text_Classification

ThirdEyeData

2023-06-13T08:11:23Z

104

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T07:55:44Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: text_classification_v1 results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train[:1%] args: plain_text metrics: - name: Accuracy type: accuracy value: 1.0 ---  # text_classification_v1 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: 0.0198 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 16 | 0.0445 | 1.0 | | No log | 2.0 | 32 | 0.0198 | 1.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cpu - Datasets 2.12.0 - Tokenizers 0.13.3

TGiang/finetuning_t5

TGiang

2023-06-13T08:09:21Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-12T17:27:16Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: finetuning_t5 results: [] ---  # finetuning_t5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3628 | 1.0 | 444 | 0.2424 | | 0.2569 | 2.0 | 889 | 0.2220 | | 0.3852 | 3.0 | 1333 | 0.2854 | | 0.4332 | 4.0 | 1778 | 0.2795 | | 0.4322 | 5.0 | 2222 | 0.2794 | | 0.4276 | 6.0 | 2667 | 0.2794 | | 0.4275 | 7.0 | 3111 | 0.2794 | | 0.4269 | 8.0 | 3556 | 0.2795 | | 0.429 | 9.0 | 4001 | 0.2795 | | 0.4308 | 9.99 | 4440 | 0.2795 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

mamun4105/dqn-SpaceInvadersNoFrameskip-v4

mamun4105

2023-06-13T07:31:22Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T06:10:32Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 640.00 +/- 199.71 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mamun4105 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mamun4105 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mamun4105 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

casque/MuscleGirl_v1

casque

2023-06-13T06:59:23Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T06:57:46Z

--- license: creativeml-openrail-m ---

ugshanyu/hello_world

ugshanyu

2023-06-13T06:40:17Z

168

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-13T04:34:40Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: hello_world results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: mn split: test args: mn metrics: - name: Wer type: wer value: 0.4679207811551829 ---  # hello_world This model is a fine-tuned version of [tugstugi/wav2vec2-large-xlsr-53-mongolian](https://huggingface.co/tugstugi/wav2vec2-large-xlsr-53-mongolian) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8235 - Wer: 0.4679 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1725 | 6.78 | 400 | 0.8343 | 0.5449 | | 0.1406 | 13.56 | 800 | 0.8587 | 0.5158 | | 0.1013 | 20.34 | 1200 | 0.8260 | 0.4990 | | 0.0701 | 27.12 | 1600 | 0.8235 | 0.4679 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

pilgrim222/Taxi_v3_Qlearn

pilgrim222

2023-06-13T06:39:21Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T06:39:14Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_v3_Qlearn 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="pilgrim222/Taxi_v3_Qlearn", 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"]) ```

addy88/distilroberta-base

addy88

2023-06-13T06:39:09Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-12T09:16:21Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilroberta-base results: [] ---  # distilroberta-base This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6935 - Precision: 0.7556 - Recall: 0.7556 - F1: 0.7556 - Accuracy: 0.7556 ## 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 - distributed_type: multi-GPU - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2481 | 1.0 | 2355 | 1.5506 | 0.7409 | 0.7409 | 0.7409 | 0.7409 | | 0.3473 | 2.0 | 4710 | 1.5572 | 0.7428 | 0.7428 | 0.7428 | 0.7428 | | 0.2614 | 3.0 | 7065 | 1.6423 | 0.7539 | 0.7539 | 0.7539 | 0.7539 | | 0.1337 | 4.0 | 9420 | 1.6935 | 0.7556 | 0.7556 | 0.7556 | 0.7556 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

pilgrim222/q-FrozenLake-v1-4x4-noSlippery

pilgrim222

2023-06-13T06:24:44Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T06:24:42Z

--- 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="pilgrim222/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"]) ```

casque/Angewomon-Digimon-v1

casque

2023-06-13T06:21:20Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T06:19:51Z

--- license: creativeml-openrail-m ---

Catears/AnythingVAEStorage

Catears

2023-06-13T06:17:58Z

0

diffusers

[ "diffusers", "license:unknown", "region:us" ]

null

2023-06-13T06:09:45Z

--- license: unknown --- ## This is just a direct copy of AnythingV4 vae. I want to load it manually in diffusers to avoid loading failure, but cannot do it using "from_pretrained" directly. That's why I create this model card to resolve the issue

LemonFace0309/Reinforce-Pixelcopter-PLE-v0

LemonFace0309

2023-06-13T06:07:08Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T06:06:42Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 8.20 +/- 7.98 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

rashxyz/ariv2

rashxyz

2023-06-13T05:55:40Z

29

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-13T05:50:08Z

--- license: openrail tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model ---

ppsingh/action-policy-plans-classifier

ppsingh

2023-06-13T05:50:19Z

110

0

transformers

[ "transformers", "pytorch", "mpnet", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T05:49:22Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: action-policy-plans-classifier results: [] ---  # action-policy-plans-classifier This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6839 - Precision Micro: 0.7089 - Precision Weighted: 0.7043 - Precision Samples: 0.4047 - Recall Micro: 0.7066 - Recall Weighted: 0.7066 - Recall Samples: 0.4047 - F1-score: 0.4041 ## 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: 2.915e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:| | 0.7333 | 1.0 | 253 | 0.5828 | 0.625 | 0.6422 | 0.4047 | 0.7098 | 0.7098 | 0.4065 | 0.4047 | | 0.5905 | 2.0 | 506 | 0.5593 | 0.6292 | 0.6318 | 0.4437 | 0.7760 | 0.7760 | 0.4446 | 0.4434 | | 0.4934 | 3.0 | 759 | 0.5269 | 0.6630 | 0.6637 | 0.4319 | 0.7571 | 0.7571 | 0.4347 | 0.4325 | | 0.4018 | 4.0 | 1012 | 0.5645 | 0.6449 | 0.6479 | 0.4456 | 0.7792 | 0.7792 | 0.4465 | 0.4453 | | 0.3235 | 5.0 | 1265 | 0.6101 | 0.6964 | 0.6929 | 0.4220 | 0.7382 | 0.7382 | 0.4229 | 0.4217 | | 0.2638 | 6.0 | 1518 | 0.6692 | 0.6888 | 0.6841 | 0.4111 | 0.7192 | 0.7192 | 0.4120 | 0.4108 | | 0.2197 | 7.0 | 1771 | 0.6839 | 0.7089 | 0.7043 | 0.4047 | 0.7066 | 0.7066 | 0.4047 | 0.4041 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

gokuls/bert_12_layer_model_v1_complete_training_new_48_KD

gokuls

2023-06-13T05:49:08Z

50

0

transformers

[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-10T03:43:10Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v1_complete_training_new_48_KD results: [] ---  # bert_12_layer_model_v1_complete_training_new_48_KD This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 326.4413 - Accuracy: 0.3018 ## 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: 36 - eval_batch_size: 36 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 849.2694 | 0.06 | 10000 | 802.2138 | 0.1435 | | 603.4255 | 0.12 | 20000 | 597.5114 | 0.1445 | | 552.5588 | 0.18 | 30000 | 549.1310 | 0.1454 | | 525.5738 | 0.25 | 40000 | 523.0781 | 0.1460 | | 508.5192 | 0.31 | 50000 | 507.5772 | 0.1463 | | 496.0482 | 0.37 | 60000 | 494.5385 | 0.1457 | | 487.2105 | 0.43 | 70000 | 484.7273 | 0.1464 | | 476.1281 | 0.49 | 80000 | 473.3444 | 0.1490 | | 456.0017 | 0.55 | 90000 | 445.0464 | 0.1662 | | 421.6633 | 0.61 | 100000 | 404.1071 | 0.2046 | | 382.6604 | 0.68 | 110000 | 369.2148 | 0.2446 | | 358.6727 | 0.74 | 120000 | 341.1114 | 0.2776 | | 339.9395 | 0.8 | 130000 | 326.4413 | 0.3018 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.12.0 - Tokenizers 0.13.3

Deojaklah/Memeyy

Deojaklah

2023-06-13T05:44:59Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T05:35:02Z

--- license: creativeml-openrail-m ---

DeskHyper/testingmodel

DeskHyper

2023-06-13T05:41:46Z

4

0

transformers

[ "transformers", "resnet50", "license:bigscience-bloom-rail-1.0", "endpoints_compatible", "region:us" ]

null

2023-06-13T04:46:01Z

--- license: bigscience-bloom-rail-1.0 ---

or90/results

or90

2023-06-13T05:39:16Z

0

null

[ "generated_from_trainer", "region:us" ]

null

2023-06-13T05:34:48Z

--- tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

el254/digits

el254

2023-06-13T05:37:35Z

0

keras

[ "keras", "region:us" ]

null

2023-06-12T18:28:15Z

--- library_name: keras --- #_Моя_модель_для_распознавания_цифр Натренирована на наборе данных mnist ![](pic.png)

PlengP/phishoff-bert-300k-II

PlengP

2023-06-13T05:29:22Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T05:29:05Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: phishoff-bert-300k-II results: [] ---  # phishoff-bert-300k-II 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Tokenizers 0.13.3

mamun4105/q-Taxi-v3

mamun4105

2023-06-13T05:25:55Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-12T18:57:07Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="mamun4105/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

julianzy/CheckGPT

julianzy

2023-06-13T05:25:21Z

0

1

null

[ "dataset:julianzy/GPABenchmark", "region:us" ]

null

2023-06-13T05:23:14Z

--- datasets: - julianzy/GPABenchmark --- The official repository of paper: "Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT".

Zemulax/masked-lm-tpu

Zemulax

2023-06-13T05:18:23Z

4

0

transformers

[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-13T00:21:57Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: Zemulax/masked-lm-tpu results: [] ---  # Zemulax/masked-lm-tpu 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: - Train Loss: 7.7770 - Train Accuracy: 0.0241 - Validation Loss: 7.7589 - Validation Accuracy: 0.0230 - Epoch: 98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 223250, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 11750, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 10.2868 | 0.0 | 10.2891 | 0.0 | 0 | | 10.2817 | 0.0000 | 10.2764 | 0.0 | 1 | | 10.2772 | 0.0000 | 10.2667 | 0.0000 | 2 | | 10.2604 | 0.0000 | 10.2521 | 0.0 | 3 | | 10.2421 | 0.0000 | 10.2282 | 0.0000 | 4 | | 10.2219 | 0.0 | 10.2010 | 0.0 | 5 | | 10.1957 | 0.0 | 10.1669 | 0.0 | 6 | | 10.1667 | 0.0000 | 10.1388 | 0.0000 | 7 | | 10.1278 | 0.0000 | 10.0908 | 0.0000 | 8 | | 10.0848 | 0.0000 | 10.0405 | 0.0001 | 9 | | 10.0496 | 0.0002 | 9.9921 | 0.0007 | 10 | | 9.9940 | 0.0010 | 9.9422 | 0.0039 | 11 | | 9.9424 | 0.0035 | 9.8765 | 0.0110 | 12 | | 9.8826 | 0.0092 | 9.8156 | 0.0182 | 13 | | 9.8225 | 0.0155 | 9.7461 | 0.0209 | 14 | | 9.7670 | 0.0201 | 9.6768 | 0.0222 | 15 | | 9.7065 | 0.0219 | 9.6127 | 0.0222 | 16 | | 9.6352 | 0.0227 | 9.5445 | 0.0220 | 17 | | 9.5757 | 0.0226 | 9.4795 | 0.0219 | 18 | | 9.4894 | 0.0232 | 9.3985 | 0.0222 | 19 | | 9.4277 | 0.0234 | 9.3386 | 0.0222 | 20 | | 9.3676 | 0.0229 | 9.2753 | 0.0220 | 21 | | 9.2980 | 0.0229 | 9.2170 | 0.0219 | 22 | | 9.2361 | 0.0233 | 9.1518 | 0.0219 | 23 | | 9.1515 | 0.0236 | 9.0827 | 0.0223 | 24 | | 9.1171 | 0.0228 | 9.0406 | 0.0218 | 25 | | 9.0447 | 0.0234 | 8.9867 | 0.0218 | 26 | | 9.0119 | 0.0229 | 8.9307 | 0.0221 | 27 | | 8.9625 | 0.0229 | 8.8969 | 0.0221 | 28 | | 8.9098 | 0.0230 | 8.8341 | 0.0223 | 29 | | 8.8726 | 0.0227 | 8.8118 | 0.0220 | 30 | | 8.8574 | 0.0223 | 8.7910 | 0.0219 | 31 | | 8.7798 | 0.0231 | 8.7506 | 0.0221 | 32 | | 8.7535 | 0.0231 | 8.7055 | 0.0222 | 33 | | 8.7333 | 0.0228 | 8.6801 | 0.0223 | 34 | | 8.6985 | 0.0231 | 8.6837 | 0.0220 | 35 | | 8.6816 | 0.0229 | 8.6243 | 0.0223 | 36 | | 8.6356 | 0.0228 | 8.6323 | 0.0217 | 37 | | 8.6392 | 0.0225 | 8.5603 | 0.0225 | 38 | | 8.5802 | 0.0233 | 8.5722 | 0.0219 | 39 | | 8.5825 | 0.0228 | 8.5548 | 0.0220 | 40 | | 8.5625 | 0.0228 | 8.5272 | 0.0220 | 41 | | 8.5415 | 0.0228 | 8.5200 | 0.0222 | 42 | | 8.5124 | 0.0230 | 8.4787 | 0.0222 | 43 | | 8.4999 | 0.0229 | 8.4819 | 0.0218 | 44 | | 8.4561 | 0.0235 | 8.4453 | 0.0221 | 45 | | 8.4854 | 0.0223 | 8.4378 | 0.0220 | 46 | | 8.4367 | 0.0229 | 8.4212 | 0.0222 | 47 | | 8.4096 | 0.0232 | 8.4033 | 0.0221 | 48 | | 8.4162 | 0.0228 | 8.3869 | 0.0221 | 49 | | 8.4005 | 0.0229 | 8.3768 | 0.0218 | 50 | | 8.3583 | 0.0235 | 8.3470 | 0.0224 | 51 | | 8.3428 | 0.0235 | 8.3540 | 0.0221 | 52 | | 8.3491 | 0.0231 | 8.3201 | 0.0225 | 53 | | 8.3551 | 0.0231 | 8.3382 | 0.0221 | 54 | | 8.3186 | 0.0231 | 8.3136 | 0.0219 | 55 | | 8.3139 | 0.0226 | 8.2844 | 0.0222 | 56 | | 8.3170 | 0.0229 | 8.2740 | 0.0221 | 57 | | 8.2886 | 0.0231 | 8.2485 | 0.0223 | 58 | | 8.2648 | 0.0233 | 8.2336 | 0.0223 | 59 | | 8.2714 | 0.0225 | 8.2321 | 0.0221 | 60 | | 8.2446 | 0.0233 | 8.2135 | 0.0223 | 61 | | 8.2303 | 0.0230 | 8.1980 | 0.0223 | 62 | | 8.2022 | 0.0237 | 8.1996 | 0.0222 | 63 | | 8.2222 | 0.0227 | 8.1822 | 0.0222 | 64 | | 8.1690 | 0.0236 | 8.2005 | 0.0220 | 65 | | 8.1741 | 0.0233 | 8.1446 | 0.0226 | 66 | | 8.1990 | 0.0224 | 8.1586 | 0.0219 | 67 | | 8.1395 | 0.0236 | 8.1243 | 0.0225 | 68 | | 8.1675 | 0.0229 | 8.1275 | 0.0222 | 69 | | 8.1432 | 0.0229 | 8.1374 | 0.0217 | 70 | | 8.1197 | 0.0234 | 8.1078 | 0.0221 | 71 | | 8.1046 | 0.0232 | 8.0991 | 0.0221 | 72 | | 8.1013 | 0.0231 | 8.0794 | 0.0222 | 73 | | 8.0887 | 0.0228 | 8.0720 | 0.0221 | 74 | | 8.0661 | 0.0233 | 8.0573 | 0.0222 | 75 | | 8.0548 | 0.0231 | 8.0313 | 0.0226 | 76 | | 8.0307 | 0.0235 | 8.0278 | 0.0222 | 77 | | 8.0626 | 0.0226 | 8.0084 | 0.0224 | 78 | | 8.0276 | 0.0229 | 8.0099 | 0.0221 | 79 | | 8.0213 | 0.0231 | 7.9930 | 0.0222 | 80 | | 7.9798 | 0.0237 | 7.9742 | 0.0224 | 81 | | 8.0135 | 0.0226 | 7.9857 | 0.0218 | 82 | | 7.9500 | 0.0235 | 7.9505 | 0.0223 | 83 | | 7.9519 | 0.0234 | 7.9711 | 0.0217 | 84 | | 7.9616 | 0.0228 | 7.9288 | 0.0223 | 85 | | 7.9803 | 0.0225 | 7.8997 | 0.0226 | 86 | | 7.9369 | 0.0227 | 7.9015 | 0.0225 | 87 | | 7.9309 | 0.0229 | 7.9010 | 0.0224 | 88 | | 7.9367 | 0.0226 | 7.8988 | 0.0220 | 89 | | 7.8840 | 0.0230 | 7.8774 | 0.0216 | 90 | | 7.8785 | 0.0233 | 7.8527 | 0.0225 | 91 | | 7.8998 | 0.0226 | 7.8509 | 0.0219 | 92 | | 7.8451 | 0.0232 | 7.8488 | 0.0221 | 93 | | 7.8596 | 0.0231 | 7.8310 | 0.0222 | 94 | | 7.8434 | 0.0231 | 7.8168 | 0.0229 | 95 | | 7.7929 | 0.0238 | 7.7815 | 0.0233 | 96 | | 7.8174 | 0.0236 | 7.7857 | 0.0232 | 97 | | 7.7770 | 0.0241 | 7.7589 | 0.0230 | 98 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Tokenizers 0.13.3

kejolong/Route246

kejolong

2023-06-13T05:15:12Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-13T05:13:09Z

--- license: creativeml-openrail-m ---

AustinCarthy/MixGPT2_Suffix_100KP_BFall_fromP_90K_topP_0.75_ratio5

AustinCarthy

2023-06-13T04:58:54Z

0

null

[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]

null

2023-06-10T02:19:23Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_Domain_100KP_BFall_fromP_90K_topP_0.75_ratio5 results: [] ---  # MixGPT2_Domain_100KP_BFall_fromP_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2_using_benigh_95K_top_p_0.75suffix dataset. It achieves the following results on the evaluation set: - Loss: 0.0218 - Accuracy: 0.9976 - F1: 0.9743 - Precision: 0.9987 - Recall: 0.951 - Roc Auc Score: 0.9755 - Tpr At Fpr 0.01: 0.9608 ## 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: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.003 | 1.0 | 35625 | 0.0165 | 0.9972 | 0.9705 | 0.9921 | 0.9498 | 0.9747 | 0.888 | | 0.0023 | 2.0 | 71250 | 0.0171 | 0.9976 | 0.9739 | 0.9985 | 0.9504 | 0.9752 | 0.9526 | | 0.003 | 3.0 | 106875 | 0.0151 | 0.9978 | 0.9765 | 0.9983 | 0.9556 | 0.9778 | 0.9584 | | 0.0017 | 4.0 | 142500 | 0.0170 | 0.9977 | 0.9753 | 0.9971 | 0.9544 | 0.9771 | 0.9512 | | 0.0012 | 5.0 | 178125 | 0.0218 | 0.9976 | 0.9743 | 0.9987 | 0.951 | 0.9755 | 0.9608 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

djifg/grow_classification_kobert-lm

djifg

2023-06-13T04:44:25Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T04:36:12Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification_kobert-lm results: [] ---  # grow_classification_kobert-lm This model is a fine-tuned version of [monologg/kobert-lm](https://huggingface.co/monologg/kobert-lm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0751 - Accuracy: 0.6946 - F1: 0.6970 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8394 | 1.0 | 329 | 0.8209 | 0.6664 | 0.6719 | | 0.5789 | 2.0 | 658 | 0.8653 | 0.6962 | 0.7018 | | 0.5125 | 3.0 | 987 | 0.9554 | 0.6942 | 0.6919 | | 0.4852 | 4.0 | 1316 | 0.9838 | 0.7057 | 0.7120 | | 0.464 | 5.0 | 1645 | 0.9969 | 0.6910 | 0.6934 | | 0.4519 | 6.0 | 1974 | 1.0354 | 0.6918 | 0.6973 | | 0.4437 | 7.0 | 2303 | 1.0301 | 0.6871 | 0.6940 | | 0.4342 | 8.0 | 2632 | 1.0676 | 0.6950 | 0.6972 | | 0.4299 | 9.0 | 2961 | 1.0691 | 0.6962 | 0.6984 | | 0.4276 | 10.0 | 3290 | 1.0751 | 0.6946 | 0.6970 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

ettsai/sentiment_analysis-DistilBERT-emo

ettsai

2023-06-13T04:38:25Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emo", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-12T13:51:39Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emo metrics: - accuracy model-index: - name: sentiment_analysis-DistilBERT-emo results: - task: name: Text Classification type: text-classification dataset: name: emo type: emo config: emo2019 split: test args: emo2019 metrics: - name: Accuracy type: accuracy value: 0.882 ---  # sentiment_analysis-DistilBERT-emo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emo dataset. It achieves the following results on the evaluation set: - Loss: 0.3929 - Accuracy: 0.882 - F1 Score: 0.8928 ## 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 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Trickshotblaster/leetcoder-qa

Trickshotblaster

2023-06-13T04:37:14Z

63

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-13T04:36:42Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: leetcoder-qa results: [] ---  # leetcoder-qa This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1922 - Validation Loss: 3.8900 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 6.3985 | 4.7493 | 0 | | 4.8760 | 4.4969 | 1 | | 4.7016 | 4.3654 | 2 | | 4.5848 | 4.2650 | 3 | | 4.5086 | 4.1827 | 4 | | 4.4307 | 4.1106 | 5 | | 4.3581 | 4.0471 | 6 | | 4.2923 | 3.9898 | 7 | | 4.2397 | 3.9375 | 8 | | 4.1922 | 3.8900 | 9 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

danielthomas45a/pure-frankincense-essential-oils

danielthomas45a

2023-06-13T04:20:50Z

0

null

[ "license:openrail", "region:us" ]

null

2023-06-13T04:04:06Z

--- license: openrail --- Experience the captivating essence of nature with our <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a>. ✨ Uncover the secrets of this ancient treasure, known for its remarkable therapeutic properties and heavenly aroma. Elevate your senses and embark on a journey of serenity and rejuvenation. <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a> <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a> <a href="https://www.amazon.com/Pure-Frankincense-Essential-Oil-for-Pain-Skin/dp/B076P3XYGX">pure frankincense essential oils</a>

irfanamal/distilroberta-base-finetuned-wikitext2

irfanamal

2023-06-13T04:18:14Z

164

0

transformers

[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-12T11:00:37Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] ---  # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1005 | 1.0 | 1203 | 1.9467 | | 2.034 | 2.0 | 2406 | 1.8616 | | 1.9683 | 3.0 | 3609 | 1.8253 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

mjlaali/ppo-LunarLander-v2

mjlaali

2023-06-13T04:14:35Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T04:14:21Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.65 +/- 25.35 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 ... ```

Dans-Archive/Dans-PersonalityEngine-13b

Dans-Archive

2023-06-13T04:14:23Z

53

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-11T23:42:20Z

--- language: - en --- ### Description: This is a multipurpose chat / chat instruct hybrid model in the same vein as the Pygmalion team's Metharme. It uses a curated pile of training data that has been normalized into a consistent training format. It has been trained on a wide array of one shot instructions, multi round instructions, and role playing scenarios. ### Prompt format: Metharme The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired. ``` <|system|>system message here<|user|>user message here<|model|> ``` ``` <|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|> ``` ``` <|system|>system message here<|model|> ``` ``` <|system|>system message here<|model|>model message<|user|>user message here<|model|> ``` Some example prompts: ``` <|system|>The following is a transcript between a helpful assistant and a user.<|user|>Why is the sky blue?<|model|> ``` ``` <|system|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<|user|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|model|> ``` ``` <|system|>You are a professional editor with decades of experience, help the user with any task they have for you.<|user|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<|model|> ``` More will be added at a later date. ### Perplexity Benchmarks: - TBA ### Training information: [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl) - GPTQ 4 bit LoRA - 7 Epochs - 64 / 32 R / A - 2048 Cutoff - 18 hours on 4x RTX 4090s ### Data used in training: - TBA ### Models used: For training: https://huggingface.co/PocketDoc/llama-13b-gptq-4bit-128g For merging: https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA and https://huggingface.co/huggyllama/llama-13b ### Disclaimer: It has not been aligned and no warranty is given for the quality or safety of its outputs.

silpakanneganti/biobert-finetuned-squad-insurance

silpakanneganti

2023-06-13T03:47:55Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-06-12T10:07:55Z

--- tags: - generated_from_trainer model-index: - name: biobert-finetuned-squad-insurance results: [] ---  # biobert-finetuned-squad-insurance This model is a fine-tuned version of [dmis-lab/biobert-large-cased-v1.1-squad](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1-squad) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

gsn-codes/a2c-AntBulletEnv-v0

gsn-codes

2023-06-13T03:43:09Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T02:20:41Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1407.58 +/- 108.66 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_120

gokuls

2023-06-13T03:39:09Z

53

0

transformers

[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-11T21:07:16Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v1_complete_training_new_wt_init_120 results: [] ---  # bert_12_layer_model_v1_complete_training_new_wt_init_120 This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_96](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_96) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1966 - Accuracy: 0.5856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 2.3673 | 0.08 | 10000 | 2.2852 | 0.5732 | | 2.356 | 0.16 | 20000 | 2.2772 | 0.5744 | | 2.3424 | 0.25 | 30000 | 2.2640 | 0.5765 | | 2.3442 | 0.33 | 40000 | 2.2525 | 0.5778 | | 2.3228 | 0.41 | 50000 | 2.2427 | 0.5793 | | 2.3179 | 0.49 | 60000 | 2.2313 | 0.5810 | | 2.2993 | 0.57 | 70000 | 2.2237 | 0.5822 | | 2.2911 | 0.66 | 80000 | 2.2128 | 0.5831 | | 2.279 | 0.74 | 90000 | 2.2008 | 0.5842 | | 2.2715 | 0.82 | 100000 | 2.1966 | 0.5856 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.12.0 - Tokenizers 0.13.3

NeoCodes-dev/Reinforce-pixel_copter_v1

NeoCodes-dev

2023-06-13T03:25:44Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-13T03:25:39Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixel_copter_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.90 +/- 52.86 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

killah-t-cell/model_out

killah-t-cell

2023-06-13T03:19:15Z

1

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-06-12T22:33:37Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-killah-t-cell/model_out These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below. prompt: Crowd of men wearing hats with trees in the background ![images_0)](./images_0.png) prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality ![images_1)](./images_1.png) prompt: Group photo of clowns, oil on canvas, bittersweet expression ![images_2)](./images_2.png)

ugiugi/inisw08-DistilBERT-mlm-sgd

ugiugi

2023-06-13T03:15:19Z

20

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-13T01:22:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: inisw08-RoBERT-mlm-sgd results: [] ---  # inisw08-RoBERT-mlm-sgd This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4290 - Accuracy: 0.3133 ## 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 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

ugiugi/inisw08-DistilBERT-mlm-adamw_torch

ugiugi

2023-06-13T03:10:38Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-13T00:47:42Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: inisw08-RoBERT-mlm-adamw_torch results: [] ---  # inisw08-RoBERT-mlm-adamw_torch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1120 - Accuracy: 0.4689 ## 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 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

djifg/grow_classification

djifg

2023-06-13T02:16:31Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-13T02:04:23Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification results: [] ---  # grow_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2151 - Accuracy: 0.7979 - F1: 0.8011 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7378 | 1.0 | 329 | 0.7427 | 0.7542 | 0.7576 | | 0.2507 | 2.0 | 658 | 0.8395 | 0.7744 | 0.7803 | | 0.1459 | 3.0 | 987 | 0.9161 | 0.7776 | 0.7818 | | 0.0897 | 4.0 | 1316 | 1.1515 | 0.7617 | 0.7695 | | 0.0625 | 5.0 | 1645 | 1.0914 | 0.7855 | 0.7925 | | 0.0505 | 6.0 | 1974 | 1.1111 | 0.7907 | 0.7941 | | 0.039 | 7.0 | 2303 | 1.3218 | 0.7673 | 0.7762 | | 0.0344 | 8.0 | 2632 | 1.2019 | 0.7943 | 0.7970 | | 0.0262 | 9.0 | 2961 | 1.2520 | 0.7907 | 0.7949 | | 0.0238 | 10.0 | 3290 | 1.2151 | 0.7979 | 0.8011 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

yyabuki/pegasus-samsum

yyabuki

2023-06-13T01:35:21Z

96

0

transformers

[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-12T16:06:37Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Framework versions - Transformers 4.28.1 - Pytorch 1.10.2 - Datasets 2.11.0 - Tokenizers 0.13.3

withxiao/alpaca-llama-7b-fp16

withxiao

2023-06-13T01:27:18Z

5

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:yahma/alpaca-cleaned", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-10T05:00:41Z

--- datasets: - yahma/alpaca-cleaned pipeline_tag: text-generation ---

GoGD/xlm-roberta-base-finetuned-panx-all

GoGD

2023-06-13T01:25:21Z

101

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-13T01:20:24Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1297 - F1: 0.8803 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2887 | 1.0 | 620 | 0.1770 | 0.8207 | | 0.1426 | 2.0 | 1240 | 0.1468 | 0.8578 | | 0.091 | 3.0 | 1860 | 0.1297 | 0.8803 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3

timdettmers/qlora-longform-65b

timdettmers

2023-06-13T01:24:28Z

0

null

[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]

null

2023-05-22T20:40:37Z

# QLoRA Instruction Tuned Models | [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) | **The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes.** **Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets.** ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - **Strong performance on MMLU** following the QLoRA instruction tuning. - **Replicable and efficient instruction tuning procedure** that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - **Lightweight** checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card **Architecture**: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. **Base Model**: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. **Finetuning Data**: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. **Languages**: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters | Parameters | Dataset | Batch size | LR | Steps | Source Length | Target Length | |------------|----------|------------|------|-------|---------------|---------------| | 7B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 7B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 7B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 7B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 13B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 13B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 13B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 13B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 33B | All | 32 | 1e-4 | 5000 | 384 | 128 | | 33B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 33B | HH-RLHF | 32 | 1e-4 | 5000 | - | 768 | | 33B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | | 65B | All | 64 | 1e-4 | 2500 | 384 | 128 | | 65B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 65B | HH-RLHF | 64 | 1e-4 | 2500 | - | 768 | | 65B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset | 7B | 13B | 33B | 65B ---|---|---|---|--- LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4 Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7 Longform | 32.1 | 43.2 | 56.6 | 59.7 Chip2 | 34.5 | 41.6 | 53.6 | 59.8 HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1 Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3 OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2 Alpaca | 38.8 | 47.8 | 57.3 | 62.5 FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. | Model / Dataset | Params | Model bits | Memory | ChatGPT vs Sys | Sys vs ChatGPT | Mean | 95\% CI | |------------------|--------|------------|--------|----------------|----------------|------------------|---------| | GPT-4 | - | - | - | 119.4\% | 110.1\% | **114.5**\% | 2.6\% | | Bard | - | - | - | 93.2\% | 96.4\% | 94.8\% | 4.1\% | | Guanaco | 65B | 4-bit | 41 GB | 96.7\% | 101.9\% | **99.3**\% | 4.4\% | | Alpaca | 65B | 4-bit | 41 GB | 63.0\% | 77.9\% | 70.7\% | 4.3\% | | FLAN v2 | 65B | 4-bit | 41 GB | 37.0\% | 59.6\% | 48.4\% | 4.6\% | | Guanaco | 33B | 4-bit | 21 GB | 96.5\% | 99.2\% | **97.8**\% | 4.4\% | | Open Assistant | 33B | 16-bit | 66 GB | 73.4\% | 85.7\% | 78.1\% | 5.3\% | | Alpaca | 33B | 4-bit | 21 GB | 67.2\% | 79.7\% | 73.6\% | 4.2\% | | FLAN v2 | 33B | 4-bit | 21 GB | 26.3\% | 49.7\% | 38.0\% | 3.9\% | | Vicuna | 13B | 16-bit | 26 GB | 91.2\% | 98.7\% | **94.9**\% | 4.5\% | | Guanaco | 13B | 4-bit | 10 GB | 87.3\% | 93.4\% | 90.4\% | 5.2\% | | Alpaca | 13B | 4-bit | 10 GB | 63.8\% | 76.7\% | 69.4\% | 4.2\% | | HH-RLHF | 13B | 4-bit | 10 GB | 55.5\% | 69.1\% | 62.5\% | 4.7\% | | Unnatural Instr. | 13B | 4-bit | 10 GB | 50.6\% | 69.8\% | 60.5\% | 4.2\% | | Chip2 | 13B | 4-bit | 10 GB | 49.2\% | 69.3\% | 59.5\% | 4.7\% | | Longform | 13B | 4-bit | 10 GB | 44.9\% | 62.0\% | 53.6\% | 5.2\% | | Self-Instruct | 13B | 4-bit | 10 GB | 38.0\% | 60.5\% | 49.1\% | 4.6\% | | FLAN v2 | 13B | 4-bit | 10 GB | 32.4\% | 61.2\% | 47.0\% | 3.6\% | | Guanaco | 7B | 4-bit | 5 GB | 84.1\% | 89.8\% | **87.0**\% | 5.4\% | | Alpaca | 7B | 4-bit | 5 GB | 57.3\% | 71.2\% | 64.4\% | 5.0\% | | FLAN v2 | 7B | 4-bit | 5 GB | 33.3\% | 56.1\% | 44.8\% | 4.0\% | ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```

timdettmers/qlora-chip2-65b

timdettmers

2023-06-13T01:24:26Z

0

null

[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]

null

2023-05-22T20:31:11Z

# QLoRA Instruction Tuned Models | [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) | **The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes.** **Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets.** ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - **Strong performance on MMLU** following the QLoRA instruction tuning. - **Replicable and efficient instruction tuning procedure** that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - **Lightweight** checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card **Architecture**: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. **Base Model**: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. **Finetuning Data**: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. **Languages**: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters | Parameters | Dataset | Batch size | LR | Steps | Source Length | Target Length | |------------|----------|------------|------|-------|---------------|---------------| | 7B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 7B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 7B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 7B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 13B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 13B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 13B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 13B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 33B | All | 32 | 1e-4 | 5000 | 384 | 128 | | 33B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 33B | HH-RLHF | 32 | 1e-4 | 5000 | - | 768 | | 33B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | | 65B | All | 64 | 1e-4 | 2500 | 384 | 128 | | 65B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 65B | HH-RLHF | 64 | 1e-4 | 2500 | - | 768 | | 65B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset | 7B | 13B | 33B | 65B ---|---|---|---|--- LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4 Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7 Longform | 32.1 | 43.2 | 56.6 | 59.7 Chip2 | 34.5 | 41.6 | 53.6 | 59.8 HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1 Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3 OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2 Alpaca | 38.8 | 47.8 | 57.3 | 62.5 FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. | Model / Dataset | Params | Model bits | Memory | ChatGPT vs Sys | Sys vs ChatGPT | Mean | 95\% CI | |------------------|--------|------------|--------|----------------|----------------|------------------|---------| | GPT-4 | - | - | - | 119.4\% | 110.1\% | **114.5**\% | 2.6\% | | Bard | - | - | - | 93.2\% | 96.4\% | 94.8\% | 4.1\% | | Guanaco | 65B | 4-bit | 41 GB | 96.7\% | 101.9\% | **99.3**\% | 4.4\% | | Alpaca | 65B | 4-bit | 41 GB | 63.0\% | 77.9\% | 70.7\% | 4.3\% | | FLAN v2 | 65B | 4-bit | 41 GB | 37.0\% | 59.6\% | 48.4\% | 4.6\% | | Guanaco | 33B | 4-bit | 21 GB | 96.5\% | 99.2\% | **97.8**\% | 4.4\% | | Open Assistant | 33B | 16-bit | 66 GB | 73.4\% | 85.7\% | 78.1\% | 5.3\% | | Alpaca | 33B | 4-bit | 21 GB | 67.2\% | 79.7\% | 73.6\% | 4.2\% | | FLAN v2 | 33B | 4-bit | 21 GB | 26.3\% | 49.7\% | 38.0\% | 3.9\% | | Vicuna | 13B | 16-bit | 26 GB | 91.2\% | 98.7\% | **94.9**\% | 4.5\% | | Guanaco | 13B | 4-bit | 10 GB | 87.3\% | 93.4\% | 90.4\% | 5.2\% | | Alpaca | 13B | 4-bit | 10 GB | 63.8\% | 76.7\% | 69.4\% | 4.2\% | | HH-RLHF | 13B | 4-bit | 10 GB | 55.5\% | 69.1\% | 62.5\% | 4.7\% | | Unnatural Instr. | 13B | 4-bit | 10 GB | 50.6\% | 69.8\% | 60.5\% | 4.2\% | | Chip2 | 13B | 4-bit | 10 GB | 49.2\% | 69.3\% | 59.5\% | 4.7\% | | Longform | 13B | 4-bit | 10 GB | 44.9\% | 62.0\% | 53.6\% | 5.2\% | | Self-Instruct | 13B | 4-bit | 10 GB | 38.0\% | 60.5\% | 49.1\% | 4.6\% | | FLAN v2 | 13B | 4-bit | 10 GB | 32.4\% | 61.2\% | 47.0\% | 3.6\% | | Guanaco | 7B | 4-bit | 5 GB | 84.1\% | 89.8\% | **87.0**\% | 5.4\% | | Alpaca | 7B | 4-bit | 5 GB | 57.3\% | 71.2\% | 64.4\% | 5.0\% | | FLAN v2 | 7B | 4-bit | 5 GB | 33.3\% | 56.1\% | 44.8\% | 4.0\% | ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```

timdettmers/qlora-flan-65b

timdettmers

2023-06-13T01:24:21Z

0

1

null

[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]

null

2023-05-22T19:14:00Z

# QLoRA Instruction Tuned Models | [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) | **The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes.** **Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets.** ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - **Strong performance on MMLU** following the QLoRA instruction tuning. - **Replicable and efficient instruction tuning procedure** that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - **Lightweight** checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card **Architecture**: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. **Base Model**: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. **Finetuning Data**: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. **Languages**: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters | Parameters | Dataset | Batch size | LR | Steps | Source Length | Target Length | |------------|----------|------------|------|-------|---------------|---------------| | 7B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 7B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 7B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 7B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 13B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 13B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 13B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 13B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 33B | All | 32 | 1e-4 | 5000 | 384 | 128 | | 33B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 33B | HH-RLHF | 32 | 1e-4 | 5000 | - | 768 | | 33B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | | 65B | All | 64 | 1e-4 | 2500 | 384 | 128 | | 65B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 65B | HH-RLHF | 64 | 1e-4 | 2500 | - | 768 | | 65B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset | 7B | 13B | 33B | 65B ---|---|---|---|--- LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4 Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7 Longform | 32.1 | 43.2 | 56.6 | 59.7 Chip2 | 34.5 | 41.6 | 53.6 | 59.8 HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1 Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3 OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2 Alpaca | 38.8 | 47.8 | 57.3 | 62.5 FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. | Model / Dataset | Params | Model bits | Memory | ChatGPT vs Sys | Sys vs ChatGPT | Mean | 95\% CI | |------------------|--------|------------|--------|----------------|----------------|------------------|---------| | GPT-4 | - | - | - | 119.4\% | 110.1\% | **114.5**\% | 2.6\% | | Bard | - | - | - | 93.2\% | 96.4\% | 94.8\% | 4.1\% | | Guanaco | 65B | 4-bit | 41 GB | 96.7\% | 101.9\% | **99.3**\% | 4.4\% | | Alpaca | 65B | 4-bit | 41 GB | 63.0\% | 77.9\% | 70.7\% | 4.3\% | | FLAN v2 | 65B | 4-bit | 41 GB | 37.0\% | 59.6\% | 48.4\% | 4.6\% | | Guanaco | 33B | 4-bit | 21 GB | 96.5\% | 99.2\% | **97.8**\% | 4.4\% | | Open Assistant | 33B | 16-bit | 66 GB | 73.4\% | 85.7\% | 78.1\% | 5.3\% | | Alpaca | 33B | 4-bit | 21 GB | 67.2\% | 79.7\% | 73.6\% | 4.2\% | | FLAN v2 | 33B | 4-bit | 21 GB | 26.3\% | 49.7\% | 38.0\% | 3.9\% | | Vicuna | 13B | 16-bit | 26 GB | 91.2\% | 98.7\% | **94.9**\% | 4.5\% | | Guanaco | 13B | 4-bit | 10 GB | 87.3\% | 93.4\% | 90.4\% | 5.2\% | | Alpaca | 13B | 4-bit | 10 GB | 63.8\% | 76.7\% | 69.4\% | 4.2\% | | HH-RLHF | 13B | 4-bit | 10 GB | 55.5\% | 69.1\% | 62.5\% | 4.7\% | | Unnatural Instr. | 13B | 4-bit | 10 GB | 50.6\% | 69.8\% | 60.5\% | 4.2\% | | Chip2 | 13B | 4-bit | 10 GB | 49.2\% | 69.3\% | 59.5\% | 4.7\% | | Longform | 13B | 4-bit | 10 GB | 44.9\% | 62.0\% | 53.6\% | 5.2\% | | Self-Instruct | 13B | 4-bit | 10 GB | 38.0\% | 60.5\% | 49.1\% | 4.6\% | | FLAN v2 | 13B | 4-bit | 10 GB | 32.4\% | 61.2\% | 47.0\% | 3.6\% | | Guanaco | 7B | 4-bit | 5 GB | 84.1\% | 89.8\% | **87.0**\% | 5.4\% | | Alpaca | 7B | 4-bit | 5 GB | 57.3\% | 71.2\% | 64.4\% | 5.0\% | | FLAN v2 | 7B | 4-bit | 5 GB | 33.3\% | 56.1\% | 44.8\% | 4.0\% | ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```

timdettmers/qlora-hh-rlhf-33b

timdettmers

2023-06-13T01:24:14Z

0

1

null

[ "arxiv:2305.14314", "arxiv:2302.13971", "region:us" ]

null

2023-05-22T21:33:20Z

# QLoRA Instruction Tuned Models | [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) | **The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes.** **Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets.** ⚠️ These models are purely intended for research purposes and could produce problematic outputs. ## What are QLoRA Instruction Tuned Models and why use them? - **Strong performance on MMLU** following the QLoRA instruction tuning. - **Replicable and efficient instruction tuning procedure** that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora). - **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning. - **Lightweight** checkpoints which only contain adapter weights. ## License and Intended Use QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license. ## Usage Here is an example of how you would load Flan v2 7B in 4-bits: ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, load_in_4bit=True, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Inference can then be performed as usual with HF models as follows: ```python prompt = "Introduce yourself" formatted_prompt = ( f"A chat between a curious human and an artificial intelligence assistant." f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" f"### Human: {prompt} ### Assistant:" ) inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Expected output similar to the following: ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have. ``` ## Current Inference Limitations Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels. Below is how you would load the model in 16 bits: ```python model_name = "huggyllama/llama-7b" adapters_name = 'timdettmers/qlora-flan-7b' model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())}, ) model = PeftModel.from_pretrained(model, adapters_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Model Card **Architecture**: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$. **Base Model**: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model. **Finetuning Data**: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2. **Languages**: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details. Next, we describe Training and Evaluation details. ### Training QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets. All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models. For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer. ### Training hyperparameters | Parameters | Dataset | Batch size | LR | Steps | Source Length | Target Length | |------------|----------|------------|------|-------|---------------|---------------| | 7B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 7B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 7B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 7B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 13B | All | 16 | 2e-4 | 10000 | 384 | 128 | | 13B | OASST1 | 16 | 2e-4 | 1875 | - | 512 | | 13B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 | | 13B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 | | 33B | All | 32 | 1e-4 | 5000 | 384 | 128 | | 33B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 33B | HH-RLHF | 32 | 1e-4 | 5000 | - | 768 | | 33B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | | 65B | All | 64 | 1e-4 | 2500 | 384 | 128 | | 65B | OASST1 | 16 | 1e-4 | 1875 | - | 512 | | 65B | HH-RLHF | 64 | 1e-4 | 2500 | - | 768 | | 65B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 | ### Evaluation We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy. Dataset | 7B | 13B | 33B | 65B ---|---|---|---|--- LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4 Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7 Longform | 32.1 | 43.2 | 56.6 | 59.7 Chip2 | 34.5 | 41.6 | 53.6 | 59.8 HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1 Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3 OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2 Alpaca | 38.8 | 47.8 | 57.3 | 62.5 FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9 We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes. | Model / Dataset | Params | Model bits | Memory | ChatGPT vs Sys | Sys vs ChatGPT | Mean | 95\% CI | |------------------|--------|------------|--------|----------------|----------------|------------------|---------| | GPT-4 | - | - | - | 119.4\% | 110.1\% | **114.5**\% | 2.6\% | | Bard | - | - | - | 93.2\% | 96.4\% | 94.8\% | 4.1\% | | Guanaco | 65B | 4-bit | 41 GB | 96.7\% | 101.9\% | **99.3**\% | 4.4\% | | Alpaca | 65B | 4-bit | 41 GB | 63.0\% | 77.9\% | 70.7\% | 4.3\% | | FLAN v2 | 65B | 4-bit | 41 GB | 37.0\% | 59.6\% | 48.4\% | 4.6\% | | Guanaco | 33B | 4-bit | 21 GB | 96.5\% | 99.2\% | **97.8**\% | 4.4\% | | Open Assistant | 33B | 16-bit | 66 GB | 73.4\% | 85.7\% | 78.1\% | 5.3\% | | Alpaca | 33B | 4-bit | 21 GB | 67.2\% | 79.7\% | 73.6\% | 4.2\% | | FLAN v2 | 33B | 4-bit | 21 GB | 26.3\% | 49.7\% | 38.0\% | 3.9\% | | Vicuna | 13B | 16-bit | 26 GB | 91.2\% | 98.7\% | **94.9**\% | 4.5\% | | Guanaco | 13B | 4-bit | 10 GB | 87.3\% | 93.4\% | 90.4\% | 5.2\% | | Alpaca | 13B | 4-bit | 10 GB | 63.8\% | 76.7\% | 69.4\% | 4.2\% | | HH-RLHF | 13B | 4-bit | 10 GB | 55.5\% | 69.1\% | 62.5\% | 4.7\% | | Unnatural Instr. | 13B | 4-bit | 10 GB | 50.6\% | 69.8\% | 60.5\% | 4.2\% | | Chip2 | 13B | 4-bit | 10 GB | 49.2\% | 69.3\% | 59.5\% | 4.7\% | | Longform | 13B | 4-bit | 10 GB | 44.9\% | 62.0\% | 53.6\% | 5.2\% | | Self-Instruct | 13B | 4-bit | 10 GB | 38.0\% | 60.5\% | 49.1\% | 4.6\% | | FLAN v2 | 13B | 4-bit | 10 GB | 32.4\% | 61.2\% | 47.0\% | 3.6\% | | Guanaco | 7B | 4-bit | 5 GB | 84.1\% | 89.8\% | **87.0**\% | 5.4\% | | Alpaca | 7B | 4-bit | 5 GB | 57.3\% | 71.2\% | 64.4\% | 5.0\% | | FLAN v2 | 7B | 4-bit | 5 GB | 33.3\% | 56.1\% | 44.8\% | 4.0\% | ## Citation ```bibtex @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ```