librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
5p33ch3xpr/XLS-R_Finetuned	5p33ch3xpr	2022-12-01T20:55:03Z	107	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-29T16:49:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: XLS-R_Finetuned 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. --> # XLS-R_Finetuned This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2280 - Wer: 0.1725 ## 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: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 6.0094 \| 0.32 \| 500 \| 3.5637 \| 1.0 \| \| 3.3935 \| 0.64 \| 1000 \| 2.6589 \| 1.0 \| \| 1.5455 \| 0.95 \| 1500 \| 0.7979 \| 0.8225 \| \| 0.9065 \| 1.27 \| 2000 \| 0.5392 \| 0.6244 \| \| 0.7891 \| 1.59 \| 2500 \| 0.3554 \| 0.4551 \| \| 0.7118 \| 1.91 \| 3000 \| 0.3682 \| 0.4608 \| \| 0.6061 \| 2.23 \| 3500 \| 0.3384 \| 0.4416 \| \| 0.5536 \| 2.54 \| 4000 \| 0.2987 \| 0.4042 \| \| 0.547 \| 2.86 \| 4500 \| 0.2892 \| 0.3892 \| \| 0.4841 \| 3.18 \| 5000 \| 0.2890 \| 0.3690 \| \| 0.4434 \| 3.5 \| 5500 \| 0.2605 \| 0.3636 \| \| 0.4542 \| 3.81 \| 6000 \| 0.2932 \| 0.3773 \| \| 0.4171 \| 4.13 \| 6500 \| 0.2768 \| 0.3550 \| \| 0.3697 \| 4.45 \| 7000 \| 0.2443 \| 0.3382 \| \| 0.3776 \| 4.77 \| 7500 \| 0.2572 \| 0.3366 \| \| 0.3448 \| 5.09 \| 8000 \| 0.2267 \| 0.3006 \| \| 0.3285 \| 5.4 \| 8500 \| 0.2377 \| 0.3023 \| \| 0.3165 \| 5.72 \| 9000 \| 0.2344 \| 0.2888 \| \| 0.3194 \| 6.04 \| 9500 \| 0.2228 \| 0.2699 \| \| 0.2737 \| 6.36 \| 10000 \| 0.2201 \| 0.2754 \| \| 0.2986 \| 6.68 \| 10500 \| 0.2413 \| 0.2850 \| \| 0.2836 \| 6.99 \| 11000 \| 0.2117 \| 0.2629 \| \| 0.2467 \| 7.31 \| 11500 \| 0.2408 \| 0.2877 \| \| 0.2577 \| 7.63 \| 12000 \| 0.2134 \| 0.2448 \| \| 0.2503 \| 7.95 \| 12500 \| 0.2260 \| 0.2600 \| \| 0.2371 \| 8.26 \| 13000 \| 0.2081 \| 0.2379 \| \| 0.2303 \| 8.58 \| 13500 \| 0.2322 \| 0.2668 \| \| 0.213 \| 8.9 \| 14000 \| 0.2339 \| 0.2586 \| \| 0.2029 \| 9.22 \| 14500 \| 0.2300 \| 0.2704 \| \| 0.2146 \| 9.54 \| 15000 \| 0.2321 \| 0.2533 \| \| 0.2044 \| 9.85 \| 15500 \| 0.2393 \| 0.2685 \| \| 0.2008 \| 10.17 \| 16000 \| 0.2193 \| 0.2467 \| \| 0.182 \| 10.49 \| 16500 \| 0.2323 \| 0.2611 \| \| 0.2 \| 10.81 \| 17000 \| 0.2188 \| 0.2537 \| \| 0.1855 \| 11.13 \| 17500 \| 0.2436 \| 0.2523 \| \| 0.1745 \| 11.44 \| 18000 \| 0.2351 \| 0.2473 \| \| 0.1705 \| 11.76 \| 18500 \| 0.2556 \| 0.2663 \| \| 0.1745 \| 12.08 \| 19000 \| 0.2189 \| 0.2229 \| \| 0.1641 \| 12.4 \| 19500 \| 0.2192 \| 0.2342 \| \| 0.1546 \| 12.71 \| 20000 \| 0.2432 \| 0.2228 \| \| 0.1661 \| 13.03 \| 20500 \| 0.2323 \| 0.2242 \| \| 0.1436 \| 13.35 \| 21000 \| 0.2554 \| 0.2496 \| \| 0.1443 \| 13.67 \| 21500 \| 0.2195 \| 0.2026 \| \| 0.151 \| 13.99 \| 22000 \| 0.2400 \| 0.2201 \| \| 0.1333 \| 14.3 \| 22500 \| 0.2181 \| 0.2235 \| \| 0.137 \| 14.62 \| 23000 \| 0.2400 \| 0.2254 \| \| 0.1303 \| 14.94 \| 23500 \| 0.2265 \| 0.2088 \| \| 0.1386 \| 15.26 \| 24000 \| 0.2330 \| 0.2152 \| \| 0.1325 \| 15.58 \| 24500 \| 0.2328 \| 0.2127 \| \| 0.1227 \| 15.89 \| 25000 \| 0.2375 \| 0.2077 \| \| 0.1196 \| 16.21 \| 25500 \| 0.2394 \| 0.2144 \| \| 0.1197 \| 16.53 \| 26000 \| 0.2591 \| 0.2171 \| \| 0.1122 \| 16.85 \| 26500 \| 0.2383 \| 0.2066 \| \| 0.1093 \| 17.16 \| 27000 \| 0.2254 \| 0.2042 \| \| 0.105 \| 17.48 \| 27500 \| 0.2330 \| 0.2008 \| \| 0.0982 \| 17.8 \| 28000 \| 0.2317 \| 0.1902 \| \| 0.1072 \| 18.12 \| 28500 \| 0.2332 \| 0.1971 \| \| 0.1033 \| 18.44 \| 29000 \| 0.2313 \| 0.1923 \| \| 0.0982 \| 18.75 \| 29500 \| 0.2344 \| 0.1934 \| \| 0.103 \| 19.07 \| 30000 \| 0.2295 \| 0.1902 \| \| 0.0945 \| 19.39 \| 30500 \| 0.2352 \| 0.1976 \| \| 0.0892 \| 19.71 \| 31000 \| 0.2414 \| 0.1920 \| \| 0.1003 \| 20.03 \| 31500 \| 0.2300 \| 0.1879 \| \| 0.0861 \| 20.34 \| 32000 \| 0.2215 \| 0.1778 \| \| 0.0845 \| 20.66 \| 32500 \| 0.2321 \| 0.1866 \| \| 0.0858 \| 20.98 \| 33000 \| 0.2311 \| 0.1850 \| \| 0.0785 \| 21.3 \| 33500 \| 0.2341 \| 0.1874 \| \| 0.0786 \| 21.61 \| 34000 \| 0.2322 \| 0.1916 \| \| 0.0793 \| 21.93 \| 34500 \| 0.2358 \| 0.1846 \| \| 0.0772 \| 22.25 \| 35000 \| 0.2234 \| 0.1770 \| \| 0.0786 \| 22.57 \| 35500 \| 0.2180 \| 0.1758 \| \| 0.0747 \| 22.89 \| 36000 \| 0.2269 \| 0.1830 \| \| 0.0734 \| 23.2 \| 36500 \| 0.2320 \| 0.1860 \| \| 0.067 \| 23.52 \| 37000 \| 0.2324 \| 0.1797 \| \| 0.0733 \| 23.84 \| 37500 \| 0.2324 \| 0.1772 \| \| 0.0701 \| 24.16 \| 38000 \| 0.2293 \| 0.1737 \| \| 0.0691 \| 24.48 \| 38500 \| 0.2303 \| 0.1750 \| \| 0.0613 \| 24.79 \| 39000 \| 0.2280 \| 0.1725 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
unstructuredio/donut-base-sroie	unstructuredio	2022-12-01T20:45:49Z	131	1	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2022-12-01T15:48:28Z	--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie-long 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. --> # donut-base-sroie-long This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.7.0 - Tokenizers 0.11.0
fathyshalab/all-roberta-large-v1-auto_and_commute-4-16-5	fathyshalab	2022-12-01T20:40:16Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:55:42Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-auto_and_commute-4-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-auto_and_commute-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2614 - Accuracy: 0.4289 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7929 \| 1.0 \| 1 \| 2.5690 \| 0.2667 \| \| 2.267 \| 2.0 \| 2 \| 2.4558 \| 0.3533 \| \| 1.8495 \| 3.0 \| 3 \| 2.3630 \| 0.3911 \| \| 1.4397 \| 4.0 \| 4 \| 2.2956 \| 0.4133 \| \| 1.2985 \| 5.0 \| 5 \| 2.2614 \| 0.4289 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
facebook/esm2_t36_3B_UR50D	facebook	2022-12-01T20:22:22Z	3,892,233	18	transformers	[ "transformers", "pytorch", "tf", "esm", "fill-mask", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-13T12:38:30Z	--- license: mit widget: - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG" --- ## ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train: \| Checkpoint name \| Num layers \| Num parameters \| \|------------------------------\|----\|----------\| \| [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) \| 48 \| 15B \| \| [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) \| 36 \| 3B \| \| [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) \| 33 \| 650M \| \| [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) \| 30 \| 150M \| \| [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) \| 12 \| 35M \| \| [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) \| 6 \| 8M \|
RomeroRZ/style-eternos	RomeroRZ	2022-12-01T20:16:55Z	0	11	null	[ "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-30T18:55:09Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- ![The San Juan Mountains are beautiful!](https://i.imgur.com/FqZXZBa.jpg "Eternos, surrealism stable diffusion model") ![The San Juan Mountains are beautiful!](https://i.imgur.com/PV30R4X.jpg "Eternos, surrealism stable diffusion model") #### Eternos - A surrealist / Minimalist model (2.0 work in progress yay!) With a base instances images generated from multiples surrealism art, some Dali touches and roman / greek architecture influences the 704 version is more abstract and less style transferrable due to the higher resolution towards all regular styles. Tips : use init image (even stretched) for non-standart resolution, it can help SD a lot to guide it :) instance prompt : romerorzeternos (optional) You can find cool prompts with associated outputs on my website : [romerorz.art](https://www.romerorz.art/)
fathyshalab/all-roberta-large-v1-auto_and_commute-2-16-5	fathyshalab	2022-12-01T19:52:06Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:51:39Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-auto_and_commute-2-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-auto_and_commute-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2614 - Accuracy: 0.4289 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7929 \| 1.0 \| 1 \| 2.5690 \| 0.2667 \| \| 2.267 \| 2.0 \| 2 \| 2.4558 \| 0.3533 \| \| 1.8495 \| 3.0 \| 3 \| 2.3630 \| 0.3911 \| \| 1.4397 \| 4.0 \| 4 \| 2.2956 \| 0.4133 \| \| 1.2985 \| 5.0 \| 5 \| 2.2614 \| 0.4289 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
ameerTelbani/ameeeer	ameerTelbani	2022-12-01T19:49:18Z	186	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-01T19:49:03Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: ameeeer results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8656716346740723 --- # ameeeer Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
futuredatascience/from-classifier-v2	futuredatascience	2022-12-01T19:42:12Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-01T19:42:02Z	--- 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 53 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": 20, "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": 1060, "warmup_steps": 106, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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 -->
nvidia/nemo-megatron-mt5-3B	nvidia	2022-12-01T19:34:02Z	30	12	nemo	[ "nemo", "pytorch", "seq2seq", "masked language modeling", "multilingual", "ja", "en", "it", "lv", "ru", "hu", "zh", "pl", "el", "de", "cs", "ko", "hi", "no", "da", "sk", "fr", "pt", "lt", "es", "nl", "sv", "ro", "fi", "dataset:mc4", "arxiv:2010.11934", "arxiv:1910.10683", "arxiv:1809.05053", "arxiv:1909.08053", "license:cc-by-4.0", "region:us" ]	null	2022-09-22T19:46:28Z	--- language: - ja - en - it - lv - ru - hu - zh - pl - el - de - cs - ko - hi - no - da - sk - fr - pt - lt - es - nl - sv - ro - fi library_name: nemo datasets: - mc4 tags: - pytorch - seq2seq - masked language modeling - multilingual license: cc-by-4.0 --- # NeMo Megatron-mT5 3B <style> img { display: inline; } </style> \|[![Model architecture](https://img.shields.io/badge/Arch-Encoder--Decoder-green)](#model-architecture)\|[![Model size](https://img.shields.io/badge/Params-3B-green)](#model-architecture)\|[![Language](https://img.shields.io/badge/Language-Multilingual-green)](#datasets) ## Model Description NeMo Megatron-mT5 3B is a multilingual transformer-based masked language model. [mT5](https://arxiv.org/abs/2010.11934) [1] is a class of encoder-decoder models trained with a span-based masked language modeling objective on a dataset comprising documents from many different languages. We follow the [T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1) approach of pre-training using only the masked language modeling objective. It has Tensor Parallelism (TP) of 2, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU for inference and 2 A100 80G GPUs for finetuning. This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html). NOTE: Weights are distributed in bfloat16. ## List of Languages We pre-trained our mT5 model on the following languages from the [mC4](https://github.com/allenai/allennlp/discussions/5265) dataset. 1. Japanese 2. English 3. Italian 4. Latvian 5. Russian 6. Hungarian 7. Chinese 8. Polish 9. Greek 10. German 11. Czech 12. Korean 13. Hindi 14. Norwegian 15. Danish 16. Slovak 17. French 18. Portuguese 19. Lithuanian 20. Spanish 21. Dutch 22. Swedish 23. Romanian 24. Finnish NOTE: The English data used to train our model is the smaller "clean" version (C4) used in the [T5 paper](https://arxiv.org/abs/1910.10683) and not the larger one distributed as part of mC4. ## Getting started ### Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./ ``` ``` pip install nemo_toolkit['nlp']==1.12.0 ``` Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed - [https://developer.nvidia.com/nemo-megatron-open-beta?nvid=nv-int-tblg-249896](https://developer.nvidia.com/nemo-megatron-open-beta) ### Step 2: Run inference Note. The model has been trained with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1, but it should be possible to run inference with tensor parallel size 1 on most NVIDIA GPUs ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git checkout r1.12.0 python megatron_t5_eval.py \ --model_file nemo_megatron_mt5_3b_bf16_tp2.nemo \ --prompt "La capitale de la France est <mask>" \ --tensor_model_parallel_size 2 ``` The script will automatically replace all \<mask\> tokens with the appropriate sentinel tokens used while pre-training and attempt to fill them in autoregressively with greedy decoding. Expected Response: ``` { 'prompt': 'La capitale de la France est <mask>', 'completion': { 'text': 'Paris', 'tokens': [(4586, '▁Paris', 0.0)]}, 'masked_input': '▁La ▁capital e ▁de ▁la ▁France ▁est ▁<extra_id_0>' } ``` - prompt: The provided raw prompt as input - completion: - text: The final generated text from the model along with special/sentinel tokens besides \</s\> - tokens: Each individual subword that is generated along with its log-probability. - masked_input: The original raw prompt with <mask> replaced with appropriate sentinel tokens. ## Training Data The model was trained on the [mC4](https://github.com/allenai/allennlp/discussions/5265) dataset made available by AI2 and hosted on Huggingface. ## Evaluation results Zero-shot language transformer performance on the [XNLI](https://arxiv.org/abs/1809.05053) dataset for a model fine-tuned on MNLI. \| English \| Spanish \| German \| French \| Chinese\| \|---\|---\| ---\|---\|---\| \|89.4\|86.4\|84.5\|85.8\|79.9\| ## Limitations The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. ## References [1] [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [4] [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053) ## Licence License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
alanila/autotrain-training-2307973005	alanila	2022-12-01T19:32:39Z	101	0	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:alanila/autotrain-data-training", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T19:29:41Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - alanila/autotrain-data-training co2_eq_emissions: emissions: 3.7679548759427006 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2307973005 - CO2 Emissions (in grams): 3.7680 ## Validation Metrics - Loss: 1.098 - Accuracy: 0.508 - Macro F1: 0.559 - Micro F1: 0.508 - Weighted F1: 0.452 - Macro Precision: 0.610 - Micro Precision: 0.508 - Weighted Precision: 0.537 - Macro Recall: 0.581 - Micro Recall: 0.508 - Weighted Recall: 0.508 ## 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/alanila/autotrain-training-2307973005 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alanila/autotrain-training-2307973005", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alanila/autotrain-training-2307973005", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
juancopi81/sd-class-cryptopunks-64	juancopi81	2022-12-01T19:24:22Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T19:21:45Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cryptopunks. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(juancopi81/sd-class-cryptopunks-64) image = pipeline().images[0] image ```
drewski/distilbert-base-uncased-finetuned-cola	drewski	2022-12-01T19:09:26Z	105	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	2022-12-01T18:58:28Z	--- 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: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5258252097729852 --- <!-- 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: 0.5561 - Matthews Correlation: 0.5258 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5245 \| 1.0 \| 535 \| 0.5269 \| 0.4122 \| \| 0.3513 \| 2.0 \| 1070 \| 0.4976 \| 0.4999 \| \| 0.2411 \| 3.0 \| 1605 \| 0.5561 \| 0.5258 \| \| 0.1907 \| 4.0 \| 2140 \| 0.7641 \| 0.5174 \| \| 0.1409 \| 5.0 \| 2675 \| 0.8216 \| 0.5189 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
shahukareem/sd-class-butterflies-64	shahukareem	2022-12-01T19:08:22Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T19:07:58Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('shahukareem/sd-class-butterflies-64') image = pipeline().images[0] image ```
fathyshalab/all-roberta-large-v1-home-9-16-5	fathyshalab	2022-12-01T19:02:24Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:47:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-9-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-home-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7614 \| 1.0 \| 1 \| 2.6146 \| 0.1889 \| \| 2.2082 \| 2.0 \| 2 \| 2.5232 \| 0.2667 \| \| 1.8344 \| 3.0 \| 3 \| 2.4516 \| 0.2933 \| \| 1.4601 \| 4.0 \| 4 \| 2.4033 \| 0.3267 \| \| 1.2748 \| 5.0 \| 5 \| 2.3789 \| 0.3356 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
shrinivasbjoshi/r2-w266-setfit-mbti-multiclass-hypsearch-mpnet-nov30	shrinivasbjoshi	2022-12-01T18:17:06Z	6	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-01T18:16:51Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 2560 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": 4.2848872506915845e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2560, "warmup_steps": 256, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
fathyshalab/all-roberta-large-v1-home-6-16-5	fathyshalab	2022-12-01T17:37:54Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:41:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-6-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-home-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7614 \| 1.0 \| 1 \| 2.6146 \| 0.1889 \| \| 2.2082 \| 2.0 \| 2 \| 2.5232 \| 0.2667 \| \| 1.8344 \| 3.0 \| 3 \| 2.4516 \| 0.2933 \| \| 1.4601 \| 4.0 \| 4 \| 2.4033 \| 0.3267 \| \| 1.2748 \| 5.0 \| 5 \| 2.3789 \| 0.3356 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
VlakoResker/sd-class-butterflies-32	VlakoResker	2022-12-01T17:28:55Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T17:28:38Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('VlakoResker/sd-class-butterflies-32') image = pipeline().images[0] image ```
fathyshalab/all-roberta-large-v1-home-5-16-5	fathyshalab	2022-12-01T17:10:56Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:39:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-5-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-home-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7614 \| 1.0 \| 1 \| 2.6146 \| 0.1889 \| \| 2.2082 \| 2.0 \| 2 \| 2.5232 \| 0.2667 \| \| 1.8344 \| 3.0 \| 3 \| 2.4516 \| 0.2933 \| \| 1.4601 \| 4.0 \| 4 \| 2.4033 \| 0.3267 \| \| 1.2748 \| 5.0 \| 5 \| 2.3789 \| 0.3356 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
bowwwave/sd-class-butterflies-32	bowwwave	2022-12-01T16:52:39Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T16:52:24Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('bowwwave/sd-class-butterflies-32') image = pipeline().images[0] image ```
varunsappa/finetuning-sentiment-model-3000-samples	varunsappa	2022-12-01T16:23:25Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T16:09:20Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8833333333333333 - name: F1 type: f1 value: 0.8844884488448845 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3132 - Accuracy: 0.8833 - F1: 0.8845 ## 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 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fanpu/model_output_original_subreddit-AskScienceFiction_1	fanpu	2022-12-01T15:13:34Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-01T06:12:05Z	--- license: mit tags: - generated_from_trainer model-index: - name: model_output_original_subreddit-AskScienceFiction_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_output_original_subreddit-AskScienceFiction_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.9231 \| 0.3 \| 500 \| 3.8087 \| \| 3.8459 \| 0.6 \| 1000 \| 3.7766 \| \| 3.8217 \| 0.9 \| 1500 \| 3.7372 \| \| 3.6939 \| 1.21 \| 2000 \| 3.7237 \| \| 3.6745 \| 1.51 \| 2500 \| 3.7030 \| \| 3.6757 \| 1.81 \| 3000 \| 3.6811 \| \| 3.5099 \| 2.11 \| 3500 \| 3.6839 \| \| 3.505 \| 2.41 \| 4000 \| 3.6709 \| \| 3.5232 \| 2.71 \| 4500 \| 3.6515 \| \| 3.3416 \| 3.01 \| 5000 \| 3.6563 \| \| 3.3725 \| 3.32 \| 5500 \| 3.6496 \| \| 3.3672 \| 3.62 \| 6000 \| 3.6373 \| \| 3.3495 \| 3.92 \| 6500 \| 3.6280 \| \| 3.2464 \| 4.22 \| 7000 \| 3.6439 \| \| 3.2467 \| 4.52 \| 7500 \| 3.6415 \| \| 3.2473 \| 4.82 \| 8000 \| 3.6407 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
YeaHi/diffusion	YeaHi	2022-12-01T15:11:02Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-12-01T15:11:02Z	--- license: bigscience-openrail-m ---
arrafmousa/xlnet-base-cased-finetuned-squad	arrafmousa	2022-12-01T15:02:55Z	88	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlnet", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-12-01T13:27:48Z	--- license: mit tags: - generated_from_trainer model-index: - name: xlnet-base-cased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet-base-cased-finetuned-squad This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1093 ## 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 \| 203 \| 0.2186 \| \| No log \| 2.0 \| 406 \| 0.1985 \| \| 0.4204 \| 3.0 \| 609 \| 0.1093 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
mousaazari/t5-text2sql_v1	mousaazari	2022-12-01T13:46:33Z	15	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	2022-08-15T12:11:22Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-text2sql_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-text2sql_v1 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: 0.0772 - Rouge2 Precision: 0.8835 - Rouge2 Recall: 0.39 - Rouge2 Fmeasure: 0.5088 ## 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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| No log \| 1.0 \| 11 \| 1.9420 \| 0.0755 \| 0.022 \| 0.0323 \| \| No log \| 2.0 \| 22 \| 1.2731 \| 0.0912 \| 0.0263 \| 0.039 \| \| No log \| 3.0 \| 33 \| 0.8717 \| 0.0993 \| 0.0284 \| 0.0424 \| \| No log \| 4.0 \| 44 \| 0.5705 \| 0.1014 \| 0.032 \| 0.0464 \| \| No log \| 5.0 \| 55 \| 0.3929 \| 0.4151 \| 0.1528 \| 0.2149 \| \| No log \| 6.0 \| 66 \| 0.2911 \| 0.7778 \| 0.351 \| 0.4594 \| \| No log \| 7.0 \| 77 \| 0.2290 \| 0.781 \| 0.3305 \| 0.4395 \| \| No log \| 8.0 \| 88 \| 0.1995 \| 0.7381 \| 0.2992 \| 0.4018 \| \| No log \| 9.0 \| 99 \| 0.1768 \| 0.752 \| 0.3147 \| 0.4202 \| \| No log \| 10.0 \| 110 \| 0.1554 \| 0.7242 \| 0.3136 \| 0.412 \| \| No log \| 11.0 \| 121 \| 0.1446 \| 0.8128 \| 0.3583 \| 0.4694 \| \| No log \| 12.0 \| 132 \| 0.1337 \| 0.8194 \| 0.3653 \| 0.478 \| \| No log \| 13.0 \| 143 \| 0.1264 \| 0.8088 \| 0.3564 \| 0.4675 \| \| No log \| 14.0 \| 154 \| 0.1170 \| 0.8036 \| 0.3502 \| 0.462 \| \| No log \| 15.0 \| 165 \| 0.1078 \| 0.8851 \| 0.3981 \| 0.5188 \| \| No log \| 16.0 \| 176 \| 0.1046 \| 0.8716 \| 0.3864 \| 0.5054 \| \| No log \| 17.0 \| 187 \| 0.1007 \| 0.8753 \| 0.3851 \| 0.5042 \| \| No log \| 18.0 \| 198 \| 0.0951 \| 0.8756 \| 0.3941 \| 0.5126 \| \| No log \| 19.0 \| 209 \| 0.0928 \| 0.8414 \| 0.3565 \| 0.4708 \| \| No log \| 20.0 \| 220 \| 0.0894 \| 0.854 \| 0.3642 \| 0.4808 \| \| No log \| 21.0 \| 231 \| 0.0863 \| 0.8954 \| 0.3954 \| 0.5168 \| \| No log \| 22.0 \| 242 \| 0.0832 \| 0.888 \| 0.3931 \| 0.5122 \| \| No log \| 23.0 \| 253 \| 0.0828 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 24.0 \| 264 \| 0.0820 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 25.0 \| 275 \| 0.0803 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 26.0 \| 286 \| 0.0792 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 27.0 \| 297 \| 0.0784 \| 0.8761 \| 0.3886 \| 0.5066 \| \| No log \| 28.0 \| 308 \| 0.0775 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 29.0 \| 319 \| 0.0772 \| 0.8835 \| 0.39 \| 0.5088 \| \| No log \| 30.0 \| 330 \| 0.0772 \| 0.8835 \| 0.39 \| 0.5088 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
MGanesh29/distilbert-base-uncased-finetuned-cola-v5	MGanesh29	2022-12-01T13:40:01Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T10:54:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: distilbert-base-uncased-finetuned-cola-v5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola-v5 This model is a fine-tuned version of [MGanesh29/distilbert-base-uncased-finetuned-cola-v5](https://huggingface.co/MGanesh29/distilbert-base-uncased-finetuned-cola-v5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9310 - Precision: 0.9310 - Recall: 0.9310 - F1: 0.9310 ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 6.25 \| 50 \| 0.2638 \| 0.9310 \| 0.9310 \| 0.9310 \| 0.9310 \| \| No log \| 12.5 \| 100 \| 0.2607 \| 0.9310 \| 0.9310 \| 0.9310 \| 0.9310 \| \| No log \| 18.75 \| 150 \| 0.2643 \| 0.9310 \| 0.9310 \| 0.9310 \| 0.9310 \| \| No log \| 25.0 \| 200 \| 0.2563 \| 0.9310 \| 0.9310 \| 0.9310 \| 0.9310 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-home-2-16-5	fathyshalab	2022-12-01T13:02:46Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:33:51Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-2-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-home-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7614 \| 1.0 \| 1 \| 2.6146 \| 0.1889 \| \| 2.2082 \| 2.0 \| 2 \| 2.5232 \| 0.2667 \| \| 1.8344 \| 3.0 \| 3 \| 2.4516 \| 0.2933 \| \| 1.4601 \| 4.0 \| 4 \| 2.4033 \| 0.3267 \| \| 1.2748 \| 5.0 \| 5 \| 2.3789 \| 0.3356 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
manirai91/enlm-roberta-conll2003-final	manirai91	2022-12-01T12:28:17Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-01T11:02:56Z	--- tags: - generated_from_trainer datasets: - conll2003 model-index: - name: enlm-roberta-conll2003-final 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. --> # enlm-roberta-conll2003-final This model is a fine-tuned version of [manirai91/enlm-roberta-final](https://huggingface.co/manirai91/enlm-roberta-final) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-kitchen_and_dining-9-16-5	fathyshalab	2022-12-01T12:11:49Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:30:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-9-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-kitchen_and_dining-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7421 \| 1.0 \| 1 \| 2.5878 \| 0.2012 \| \| 2.1065 \| 2.0 \| 2 \| 2.4975 \| 0.2012 \| \| 1.5994 \| 3.0 \| 3 \| 2.4274 \| 0.2249 \| \| 1.1739 \| 4.0 \| 4 \| 2.3808 \| 0.2456 \| \| 1.083 \| 5.0 \| 5 \| 2.3560 \| 0.2692 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
ViktorDo/DistilBERT-POWO_MGH_Life_Form_Finetuned	ViktorDo	2022-12-01T11:55:19Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T11:45:50Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: DistilBERT-POWO_MGH_Life_Form_Finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DistilBERT-POWO_MGH_Life_Form_Finetuned 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: 0.3845 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5891 \| 1.0 \| 914 \| 0.4130 \| \| 0.4207 \| 2.0 \| 1828 \| 0.3868 \| \| 0.3722 \| 3.0 \| 2742 \| 0.3845 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
hizak/sd-class-butterflies-64	hizak	2022-12-01T11:52:54Z	33	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T11:52:01Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(hizak/sd-class-butterflies-64) image = pipeline().images[0] image ```
fathyshalab/all-roberta-large-v1-kitchen_and_dining-8-16-5	fathyshalab	2022-12-01T11:45:56Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:28:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-8-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-kitchen_and_dining-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7421 \| 1.0 \| 1 \| 2.5878 \| 0.2012 \| \| 2.1065 \| 2.0 \| 2 \| 2.4975 \| 0.2012 \| \| 1.5994 \| 3.0 \| 3 \| 2.4274 \| 0.2249 \| \| 1.1739 \| 4.0 \| 4 \| 2.3808 \| 0.2456 \| \| 1.083 \| 5.0 \| 5 \| 2.3560 \| 0.2692 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/all-roberta-large-v1-kitchen_and_dining-7-16-5	fathyshalab	2022-12-01T11:20:14Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:26:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-7-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-kitchen_and_dining-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7421 \| 1.0 \| 1 \| 2.5878 \| 0.2012 \| \| 2.1065 \| 2.0 \| 2 \| 2.4975 \| 0.2012 \| \| 1.5994 \| 3.0 \| 3 \| 2.4274 \| 0.2249 \| \| 1.1739 \| 4.0 \| 4 \| 2.3808 \| 0.2456 \| \| 1.083 \| 5.0 \| 5 \| 2.3560 \| 0.2692 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
rls-telefonica/word_sense_mchoice_w_d_c	rls-telefonica	2022-12-01T11:13:31Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2022-12-01T10:46:55Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: word_sense_mchoice_w_d_c 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. --> # word_sense_mchoice_w_d_c This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8885 - Accuracy: 0.8210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6904 \| 1.0 \| 531 \| 0.5099 \| 0.7913 \| \| 0.2393 \| 2.0 \| 1062 \| 0.6351 \| 0.8202 \| \| 0.0842 \| 3.0 \| 1593 \| 0.8885 \| 0.8210 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Earrr/Disco	Earrr	2022-12-01T11:10:20Z	0	0	null	[ "region:us" ]	null	2022-12-01T11:03:56Z	I don't own this model I uploaded it for personal use please contact me to delete if you are the auther [email protected]
hizak/sd-class-butterflies-32	hizak	2022-12-01T11:09:16Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-01T10:12:45Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(hizak/sd-class-butterflies-32) image = pipeline().images[0] image ```
AlekseyKorshuk/6.7b-ri-reproduce-combined-4-gpu-0-val	AlekseyKorshuk	2022-12-01T10:15:15Z	9	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:ChaiML/dalio_combined_v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-30T10:39:53Z	--- license: other tags: - generated_from_trainer datasets: - ChaiML/dalio_combined_v1 model-index: - name: 6.7b-ri-reproduce-combined-4-gpu-0-val 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. --> # 6.7b-ri-reproduce-combined-4-gpu-0-val This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the ChaiML/dalio_combined_v1 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: 9e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 100 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ConvLab/lava-policy-multiwoz20	ConvLab	2022-12-01T09:59:09Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "license:apache-2.0", "region:us" ]	null	2022-11-29T15:40:46Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog --- # lava-policy-multiwoz This is the best performing LAVA_kl model from the [LAVA paper](https://aclanthology.org/2020.coling-main.41/) which can be used as a word-level policy module in ConvLab3 pipeline. Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure The model was trained on MultiWOZ 2.0 data using the [LAVA codebase](https://gitlab.cs.uni-duesseldorf.de/general/dsml/lava-public). The model started with VAE pre-training and fine-tuning with informative prior KL loss, followed by corpus-based RL with REINFORCE. ### Training hyperparameters The following hyperparameters were used during SL training: - y_size: 10 - k_size: 20 - beta: 0.1 - simple_posterior: true - contextual_posterior: false - learning_rate: 1e-03 - max_vocab_size: 1000 - max_utt_len: 50 - max_dec_len: 30 - backward_size: 2 - train_batch_size: 128 - seed: 58 - optimizer: Adam - num_epoch: 100 with early stopping based on validation set The following hyperparameters were used during RL training: - tune_pi_only: false - max_words: 100 - temperature: 1.0 - episode_repeat: 1.0 - rl_lr: 0.01 - momentum: 0.0 - nesterov: false - gamma: 0.99 - rl_clip: 5.0 - random_seed: 38
AlekseyKorshuk/6.7b-ri-reproduce-combined-4-gpu-20-val	AlekseyKorshuk	2022-12-01T09:45:32Z	4	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-30T10:26:05Z	--- license: other tags: - generated_from_trainer metrics: - accuracy model-index: - name: 6.7b-ri-reproduce-combined-4-gpu-20-val 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. --> # 6.7b-ri-reproduce-combined-4-gpu-20-val This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9434 - Accuracy: 0.0329 - Perplexity: 51.5916 ## 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: 9e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 100 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Perplexity \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\| \| 2.5731 \| 1.0 \| 79 \| 2.6113 \| 0.0317 \| 13.6171 \| \| 2.206 \| 2.0 \| 158 \| 2.4805 \| 0.0328 \| 11.9469 \| \| 1.9105 \| 3.0 \| 237 \| 2.4512 \| 0.0333 \| 11.6019 \| \| 1.6301 \| 4.0 \| 316 \| 2.5078 \| 0.0345 \| 12.2780 \| \| 1.3733 \| 5.0 \| 395 \| 2.6816 \| 0.0342 \| 14.6090 \| \| 1.1337 \| 6.0 \| 474 \| 3.0078 \| 0.0330 \| 20.2431 \| \| 0.9619 \| 7.0 \| 553 \| 3.1777 \| 0.0330 \| 23.9923 \| \| 0.798 \| 8.0 \| 632 \| 3.2559 \| 0.0330 \| 25.9419 \| \| 0.6653 \| 9.0 \| 711 \| 3.4277 \| 0.0331 \| 30.8068 \| \| 0.552 \| 10.0 \| 790 \| 3.5566 \| 0.0333 \| 35.0453 \| \| 0.4568 \| 11.0 \| 869 \| 3.7324 \| 0.0324 \| 41.7802 \| \| 0.3756 \| 12.0 \| 948 \| 3.8184 \| 0.0328 \| 45.5295 \| \| 0.3119 \| 13.0 \| 1027 \| 3.8477 \| 0.0331 \| 46.8831 \| \| 0.2448 \| 14.0 \| 1106 \| 3.9062 \| 0.0329 \| 49.7122 \| \| 0.1986 \| 15.0 \| 1185 \| 3.9434 \| 0.0329 \| 51.5916 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3	gary109	2022-12-01T09:36:58Z	85	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "dataset:ai_light_dance", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-30T11:17:30Z	--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer datasets: - ai_light_dance model-index: - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3 This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2) on the GARY109/AI_LIGHT_DANCE - ONSET-DRUMS_FOLD_3 dataset. It achieves the following results on the evaluation set: - Loss: 0.4093 - Wer: 0.1250 ## 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: 4 - eval_batch_size: 4 - 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: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4557 \| 1.0 \| 70 \| 0.5794 \| 0.1197 \| \| 0.6796 \| 2.0 \| 140 \| 0.5726 \| 0.1388 \| \| 0.4511 \| 3.0 \| 210 \| 0.6290 \| 0.1242 \| \| 0.609 \| 4.0 \| 280 \| 0.7112 \| 0.1187 \| \| 0.4082 \| 5.0 \| 350 \| 0.8275 \| 0.1965 \| \| 0.4638 \| 6.0 \| 420 \| 0.4767 \| 0.1524 \| \| 0.4446 \| 7.0 \| 490 \| 0.5091 \| 0.1376 \| \| 0.4337 \| 8.0 \| 560 \| 0.6622 \| 0.1170 \| \| 0.4604 \| 9.0 \| 630 \| 0.7242 \| 0.1600 \| \| 0.4462 \| 10.0 \| 700 \| 0.7298 \| 0.1383 \| \| 0.4201 \| 11.0 \| 770 \| 0.8058 \| 0.1362 \| \| 0.4204 \| 12.0 \| 840 \| 0.6255 \| 0.1099 \| \| 0.461 \| 13.0 \| 910 \| 0.5204 \| 0.1109 \| \| 0.3779 \| 14.0 \| 980 \| 0.6911 \| 0.1125 \| \| 0.3403 \| 15.0 \| 1050 \| 0.5863 \| 0.1188 \| \| 0.6223 \| 16.0 \| 1120 \| 0.6367 \| 0.1147 \| \| 0.3827 \| 17.0 \| 1190 \| 0.6266 \| 0.1293 \| \| 0.3055 \| 18.0 \| 1260 \| 0.4866 \| 0.1095 \| \| 0.3917 \| 19.0 \| 1330 \| 0.4093 \| 0.1250 \| \| 0.3912 \| 20.0 \| 1400 \| 0.4514 \| 0.1077 \| \| 0.3861 \| 21.0 \| 1470 \| 0.5043 \| 0.1156 \| \| 0.3659 \| 22.0 \| 1540 \| 0.5680 \| 0.1091 \| \| 0.3536 \| 23.0 \| 1610 \| 0.7940 \| 0.1029 \| \| 0.3559 \| 24.0 \| 1680 \| 0.5877 \| 0.1101 \| \| 0.3274 \| 25.0 \| 1750 \| 0.4461 \| 0.1059 \| \| 0.5232 \| 26.0 \| 1820 \| 1.2051 \| 0.1068 \| \| 0.3241 \| 27.0 \| 1890 \| 0.8716 \| 0.1099 \| \| 0.3169 \| 28.0 \| 1960 \| 0.6752 \| 0.1082 \| \| 0.2938 \| 29.0 \| 2030 \| 0.6023 \| 0.1071 \| \| 0.3022 \| 30.0 \| 2100 \| 0.6122 \| 0.1146 \| \| 0.4245 \| 31.0 \| 2170 \| 0.5735 \| 0.1102 \| \| 0.3095 \| 32.0 \| 2240 \| 0.4476 \| 0.1042 \| \| 0.4062 \| 33.0 \| 2310 \| 0.6339 \| 0.1130 \| \| 0.3202 \| 34.0 \| 2380 \| 0.4101 \| 0.1077 \| \| 0.2952 \| 35.0 \| 2450 \| 0.4825 \| 0.1076 \| \| 0.2945 \| 36.0 \| 2520 \| 0.4998 \| 0.1058 \| \| 0.336 \| 37.0 \| 2590 \| 0.5490 \| 0.1061 \| \| 0.2912 \| 38.0 \| 2660 \| 0.4804 \| 0.1038 \| \| 0.282 \| 39.0 \| 2730 \| 0.4776 \| 0.1022 \| \| 0.4359 \| 40.0 \| 2800 \| 0.4376 \| 0.1044 \| \| 0.2698 \| 41.0 \| 2870 \| 0.5609 \| 0.1098 \| \| 0.3004 \| 42.0 \| 2940 \| 0.5258 \| 0.1083 \| \| 0.2873 \| 43.0 \| 3010 \| 0.4810 \| 0.1069 \| \| 0.3413 \| 44.0 \| 3080 \| 0.4961 \| 0.1080 \| \| 0.2802 \| 45.0 \| 3150 \| 0.6850 \| 0.1076 \| \| 0.2584 \| 46.0 \| 3220 \| 0.7210 \| 0.1082 \| \| 0.3282 \| 47.0 \| 3290 \| 0.6179 \| 0.1053 \| \| 0.2666 \| 48.0 \| 3360 \| 0.7673 \| 0.1075 \| \| 0.2989 \| 49.0 \| 3430 \| 0.7710 \| 0.1079 \| \| 0.2676 \| 50.0 \| 3500 \| 0.7655 \| 0.1076 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
MGanesh29/distilbert-base-uncased-finetuned-cola-v3	MGanesh29	2022-12-01T09:17:29Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T09:00:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola-v3 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: 0.9655 - Matthews Correlation: 0.7369 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 8 \| 1.9112 \| 0.1486 \| \| No log \| 2.0 \| 16 \| 1.8626 \| 0.1273 \| \| No log \| 3.0 \| 24 \| 1.7793 \| 0.1947 \| \| No log \| 4.0 \| 32 \| 1.6722 \| 0.1681 \| \| No log \| 5.0 \| 40 \| 1.5578 \| 0.3876 \| \| No log \| 6.0 \| 48 \| 1.4463 \| 0.5551 \| \| No log \| 7.0 \| 56 \| 1.3280 \| 0.5498 \| \| No log \| 8.0 \| 64 \| 1.2302 \| 0.5936 \| \| No log \| 9.0 \| 72 \| 1.1408 \| 0.6998 \| \| No log \| 10.0 \| 80 \| 1.0765 \| 0.6601 \| \| No log \| 11.0 \| 88 \| 1.0145 \| 0.6988 \| \| No log \| 12.0 \| 96 \| 0.9655 \| 0.7369 \| \| No log \| 13.0 \| 104 \| 0.9389 \| 0.6992 \| \| No log \| 14.0 \| 112 \| 0.9258 \| 0.6992 \| \| No log \| 15.0 \| 120 \| 0.9209 \| 0.6992 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289	hr-elrond	2022-12-01T08:59:48Z	100	2	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "unk", "dataset:hr-elrond/autotrain-data-consumer-nature-speech_finbert", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-11-18T15:00:49Z	--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - hr-elrond/autotrain-data-consumer-nature-speech_finbert co2_eq_emissions: emissions: 0.004371975254312265 --- # Model Trained Using AutoTrain We trained FinBERT to identify whether firms´ talk contains consumer concepts of human nature (e.g., "I believe consumers generally act rational.", "Consumers must take over responsibility for the choices they make.", "It seems consumers behave quite altruistic.") from statements that do not (e.g., "We expect buyers to double their purchases next year.", "We see a 5% growth in numbers compared to the previous year."). The training data consisted of 236 positive documents (containing concepts of consumer nature) and 1034 negative documents (not contain concepts of consumer nature) extracted from earnings call transcripts of S&P-500 companies (2015-2020). # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2147169289 - CO2 Emissions (in grams): 0.0044 ## Validation Metrics - Loss: 0.256 - Accuracy: 0.913 - Precision: 0.736 - Recall: 0.830 - AUC: 0.956 - F1: 0.780 ## 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/hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
htermotto/distilbert-base-uncased-finetuned-sngp-squad-seed-999	htermotto	2022-12-01T08:30:08Z	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-12-01T05:08:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-sngp-squad-seed-999 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sngp-squad-seed-999 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9622 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4527 \| 1.0 \| 8248 \| 2.0711 \| \| 2.1703 \| 2.0 \| 16496 \| 1.9622 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
ravinduj/finetuning-sentiment-model-3000-samples	ravinduj	2022-12-01T08:21:50Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T10:38:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8533333333333334 - name: F1 type: f1 value: 0.8543046357615894 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3489 - Accuracy: 0.8533 - F1: 0.8543 ## 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 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-kitchen_and_dining-3-16-5	fathyshalab	2022-12-01T08:14:12Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:19:00Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-3-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-kitchen_and_dining-3-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7421 \| 1.0 \| 1 \| 2.5878 \| 0.2012 \| \| 2.1065 \| 2.0 \| 2 \| 2.4975 \| 0.2012 \| \| 1.5994 \| 3.0 \| 3 \| 2.4274 \| 0.2249 \| \| 1.1739 \| 4.0 \| 4 \| 2.3808 \| 0.2456 \| \| 1.083 \| 5.0 \| 5 \| 2.3560 \| 0.2692 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
pig4431/YELP_ALBERT_5E	pig4431	2022-12-01T08:07:20Z	107	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T07:33:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_ALBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9733333333333334 --- <!-- 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. --> # YELP_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - Accuracy: 0.9733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4967 \| 0.03 \| 50 \| 0.1667 \| 0.9467 \| \| 0.3268 \| 0.06 \| 100 \| 0.2106 \| 0.9133 \| \| 0.3413 \| 0.1 \| 150 \| 0.2107 \| 0.9667 \| \| 0.3172 \| 0.13 \| 200 \| 0.1906 \| 0.94 \| \| 0.2804 \| 0.16 \| 250 \| 0.2588 \| 0.9 \| \| 0.2604 \| 0.19 \| 300 \| 0.2023 \| 0.94 \| \| 0.2532 \| 0.22 \| 350 \| 0.1263 \| 0.9533 \| \| 0.2103 \| 0.26 \| 400 \| 0.1233 \| 0.96 \| \| 0.212 \| 0.29 \| 450 \| 0.2019 \| 0.9267 \| \| 0.2669 \| 0.32 \| 500 \| 0.1110 \| 0.9667 \| \| 0.2187 \| 0.35 \| 550 \| 0.1542 \| 0.96 \| \| 0.2203 \| 0.38 \| 600 \| 0.0879 \| 0.9733 \| \| 0.2699 \| 0.42 \| 650 \| 0.0971 \| 0.9667 \| \| 0.2107 \| 0.45 \| 700 \| 0.0863 \| 0.9667 \| \| 0.2443 \| 0.48 \| 750 \| 0.0823 \| 0.9733 \| \| 0.1987 \| 0.51 \| 800 \| 0.1207 \| 0.9733 \| \| 0.2326 \| 0.54 \| 850 \| 0.1368 \| 0.9667 \| \| 0.1787 \| 0.58 \| 900 \| 0.1027 \| 0.9667 \| \| 0.2159 \| 0.61 \| 950 \| 0.2443 \| 0.9333 \| \| 0.1316 \| 0.64 \| 1000 \| 0.2035 \| 0.9467 \| \| 0.2416 \| 0.67 \| 1050 \| 0.0882 \| 0.9733 \| \| 0.2008 \| 0.7 \| 1100 \| 0.1709 \| 0.9533 \| \| 0.2065 \| 0.74 \| 1150 \| 0.1098 \| 0.9667 \| \| 0.2391 \| 0.77 \| 1200 \| 0.1055 \| 0.9667 \| \| 0.1533 \| 0.8 \| 1250 \| 0.1997 \| 0.94 \| \| 0.2016 \| 0.83 \| 1300 \| 0.0899 \| 0.96 \| \| 0.2016 \| 0.86 \| 1350 \| 0.0957 \| 0.9733 \| \| 0.2316 \| 0.9 \| 1400 \| 0.0784 \| 0.98 \| \| 0.1839 \| 0.93 \| 1450 \| 0.0784 \| 0.9733 \| \| 0.2121 \| 0.96 \| 1500 \| 0.1150 \| 0.9733 \| \| 0.1307 \| 0.99 \| 1550 \| 0.0969 \| 0.9733 \| \| 0.1271 \| 1.02 \| 1600 \| 0.2326 \| 0.9467 \| \| 0.1736 \| 1.06 \| 1650 \| 0.0979 \| 0.9667 \| \| 0.1357 \| 1.09 \| 1700 \| 0.0862 \| 0.98 \| \| 0.1871 \| 1.12 \| 1750 \| 0.1419 \| 0.9667 \| \| 0.1411 \| 1.15 \| 1800 \| 0.1301 \| 0.96 \| \| 0.1317 \| 1.18 \| 1850 \| 0.1602 \| 0.9533 \| \| 0.1432 \| 1.22 \| 1900 \| 0.1885 \| 0.9533 \| \| 0.1793 \| 1.25 \| 1950 \| 0.0776 \| 0.9667 \| \| 0.1322 \| 1.28 \| 2000 \| 0.0822 \| 0.9733 \| \| 0.1416 \| 1.31 \| 2050 \| 0.0920 \| 0.9733 \| \| 0.1524 \| 1.34 \| 2100 \| 0.0673 \| 0.98 \| \| 0.1338 \| 1.38 \| 2150 \| 0.0602 \| 0.98 \| \| 0.152 \| 1.41 \| 2200 \| 0.0916 \| 0.98 \| \| 0.1192 \| 1.44 \| 2250 \| 0.0559 \| 0.98 \| \| 0.1471 \| 1.47 \| 2300 \| 0.1096 \| 0.9667 \| \| 0.1267 \| 1.5 \| 2350 \| 0.0695 \| 0.9733 \| \| 0.1776 \| 1.54 \| 2400 \| 0.1363 \| 0.96 \| \| 0.1495 \| 1.57 \| 2450 \| 0.0818 \| 0.98 \| \| 0.1158 \| 1.6 \| 2500 \| 0.1282 \| 0.9667 \| \| 0.1772 \| 1.63 \| 2550 \| 0.0682 \| 0.9733 \| \| 0.1187 \| 1.66 \| 2600 \| 0.1032 \| 0.9733 \| \| 0.136 \| 1.7 \| 2650 \| 0.1071 \| 0.9667 \| \| 0.1829 \| 1.73 \| 2700 \| 0.0753 \| 0.9667 \| \| 0.1147 \| 1.76 \| 2750 \| 0.1071 \| 0.9733 \| \| 0.1174 \| 1.79 \| 2800 \| 0.1441 \| 0.9667 \| \| 0.0707 \| 1.82 \| 2850 \| 0.1362 \| 0.9667 \| \| 0.1372 \| 1.86 \| 2900 \| 0.1861 \| 0.9533 \| \| 0.2108 \| 1.89 \| 2950 \| 0.0770 \| 0.9733 \| \| 0.2014 \| 1.92 \| 3000 \| 0.1114 \| 0.9667 \| \| 0.1373 \| 1.95 \| 3050 \| 0.1244 \| 0.9667 \| \| 0.1242 \| 1.98 \| 3100 \| 0.1220 \| 0.96 \| \| 0.1267 \| 2.02 \| 3150 \| 0.1139 \| 0.9733 \| \| 0.1021 \| 2.05 \| 3200 \| 0.2013 \| 0.9533 \| \| 0.1091 \| 2.08 \| 3250 \| 0.1027 \| 0.9733 \| \| 0.0648 \| 2.11 \| 3300 \| 0.1464 \| 0.9733 \| \| 0.1207 \| 2.14 \| 3350 \| 0.1255 \| 0.9733 \| \| 0.0833 \| 2.18 \| 3400 \| 0.0708 \| 0.98 \| \| 0.0796 \| 2.21 \| 3450 \| 0.1608 \| 0.96 \| \| 0.0624 \| 2.24 \| 3500 \| 0.0827 \| 0.98 \| \| 0.0518 \| 2.27 \| 3550 \| 0.0602 \| 0.98 \| \| 0.1242 \| 2.3 \| 3600 \| 0.0752 \| 0.9733 \| \| 0.0422 \| 2.34 \| 3650 \| 0.1000 \| 0.9733 \| \| 0.0748 \| 2.37 \| 3700 \| 0.1171 \| 0.9667 \| \| 0.0839 \| 2.4 \| 3750 \| 0.1341 \| 0.9667 \| \| 0.1033 \| 2.43 \| 3800 \| 0.0744 \| 0.98 \| \| 0.0567 \| 2.46 \| 3850 \| 0.0869 \| 0.98 \| \| 0.0756 \| 2.5 \| 3900 \| 0.0745 \| 0.98 \| \| 0.0768 \| 2.53 \| 3950 \| 0.0895 \| 0.9733 \| \| 0.0878 \| 2.56 \| 4000 \| 0.0703 \| 0.98 \| \| 0.1023 \| 2.59 \| 4050 \| 0.0806 \| 0.98 \| \| 0.0807 \| 2.62 \| 4100 \| 0.0338 \| 0.9867 \| \| 0.0868 \| 2.66 \| 4150 \| 0.0892 \| 0.9667 \| \| 0.0648 \| 2.69 \| 4200 \| 0.1637 \| 0.9533 \| \| 0.0535 \| 2.72 \| 4250 \| 0.1622 \| 0.9667 \| \| 0.0675 \| 2.75 \| 4300 \| 0.1354 \| 0.9733 \| \| 0.1121 \| 2.78 \| 4350 \| 0.1440 \| 0.9533 \| \| 0.0714 \| 2.82 \| 4400 \| 0.1022 \| 0.9467 \| \| 0.0786 \| 2.85 \| 4450 \| 0.1110 \| 0.9733 \| \| 0.0822 \| 2.88 \| 4500 \| 0.1218 \| 0.9733 \| \| 0.1075 \| 2.91 \| 4550 \| 0.1041 \| 0.9733 \| \| 0.0783 \| 2.94 \| 4600 \| 0.0992 \| 0.9733 \| \| 0.1059 \| 2.98 \| 4650 \| 0.1187 \| 0.9733 \| \| 0.067 \| 3.01 \| 4700 \| 0.0931 \| 0.9733 \| \| 0.0425 \| 3.04 \| 4750 \| 0.1252 \| 0.9733 \| \| 0.0539 \| 3.07 \| 4800 \| 0.1152 \| 0.9733 \| \| 0.0419 \| 3.1 \| 4850 \| 0.1534 \| 0.9667 \| \| 0.0462 \| 3.13 \| 4900 \| 0.1398 \| 0.9733 \| \| 0.0435 \| 3.17 \| 4950 \| 0.1168 \| 0.98 \| \| 0.0144 \| 3.2 \| 5000 \| 0.1489 \| 0.9667 \| \| 0.0367 \| 3.23 \| 5050 \| 0.1293 \| 0.9733 \| \| 0.0336 \| 3.26 \| 5100 \| 0.1353 \| 0.9733 \| \| 0.0246 \| 3.29 \| 5150 \| 0.0958 \| 0.98 \| \| 0.0181 \| 3.33 \| 5200 \| 0.1294 \| 0.9733 \| \| 0.0357 \| 3.36 \| 5250 \| 0.1209 \| 0.9733 \| \| 0.0683 \| 3.39 \| 5300 \| 0.1748 \| 0.96 \| \| 0.0353 \| 3.42 \| 5350 \| 0.2159 \| 0.9533 \| \| 0.0415 \| 3.45 \| 5400 \| 0.1723 \| 0.96 \| \| 0.0336 \| 3.49 \| 5450 \| 0.1031 \| 0.98 \| \| 0.0475 \| 3.52 \| 5500 \| 0.0959 \| 0.98 \| \| 0.0393 \| 3.55 \| 5550 \| 0.2163 \| 0.96 \| \| 0.0337 \| 3.58 \| 5600 \| 0.1097 \| 0.9733 \| \| 0.0415 \| 3.61 \| 5650 \| 0.1365 \| 0.98 \| \| 0.035 \| 3.65 \| 5700 \| 0.1175 \| 0.98 \| \| 0.0448 \| 3.68 \| 5750 \| 0.1543 \| 0.9667 \| \| 0.0445 \| 3.71 \| 5800 \| 0.2005 \| 0.96 \| \| 0.0211 \| 3.74 \| 5850 \| 0.1179 \| 0.98 \| \| 0.0198 \| 3.77 \| 5900 \| 0.1298 \| 0.9733 \| \| 0.026 \| 3.81 \| 5950 \| 0.2167 \| 0.9667 \| \| 0.0412 \| 3.84 \| 6000 \| 0.1224 \| 0.98 \| \| 0.0446 \| 3.87 \| 6050 \| 0.0798 \| 0.98 \| \| 0.0174 \| 3.9 \| 6100 \| 0.0577 \| 0.9933 \| \| 0.0535 \| 3.93 \| 6150 \| 0.1482 \| 0.9667 \| \| 0.0495 \| 3.97 \| 6200 \| 0.0862 \| 0.98 \| \| 0.0267 \| 4.0 \| 6250 \| 0.1190 \| 0.98 \| \| 0.0087 \| 4.03 \| 6300 \| 0.0747 \| 0.98 \| \| 0.0102 \| 4.06 \| 6350 \| 0.0753 \| 0.9867 \| \| 0.0178 \| 4.09 \| 6400 \| 0.1812 \| 0.9667 \| \| 0.0088 \| 4.13 \| 6450 \| 0.0817 \| 0.98 \| \| 0.0144 \| 4.16 \| 6500 \| 0.0805 \| 0.98 \| \| 0.014 \| 4.19 \| 6550 \| 0.0862 \| 0.9867 \| \| 0.0002 \| 4.22 \| 6600 \| 0.0894 \| 0.98 \| \| 0.0112 \| 4.25 \| 6650 \| 0.1004 \| 0.9733 \| \| 0.0054 \| 4.29 \| 6700 \| 0.0832 \| 0.9867 \| \| 0.0001 \| 4.32 \| 6750 \| 0.0812 \| 0.9867 \| \| 0.0202 \| 4.35 \| 6800 \| 0.1828 \| 0.9667 \| \| 0.009 \| 4.38 \| 6850 \| 0.1114 \| 0.98 \| \| 0.0001 \| 4.41 \| 6900 \| 0.1295 \| 0.98 \| \| 0.0077 \| 4.45 \| 6950 \| 0.1610 \| 0.9733 \| \| 0.0082 \| 4.48 \| 7000 \| 0.1787 \| 0.9667 \| \| 0.0198 \| 4.51 \| 7050 \| 0.1485 \| 0.9733 \| \| 0.0017 \| 4.54 \| 7100 \| 0.1774 \| 0.9733 \| \| 0.0115 \| 4.57 \| 7150 \| 0.1567 \| 0.9733 \| \| 0.0001 \| 4.61 \| 7200 \| 0.1534 \| 0.9733 \| \| 0.0247 \| 4.64 \| 7250 \| 0.2020 \| 0.9667 \| \| 0.0059 \| 4.67 \| 7300 \| 0.1918 \| 0.9667 \| \| 0.0052 \| 4.7 \| 7350 \| 0.1315 \| 0.98 \| \| 0.0076 \| 4.73 \| 7400 \| 0.1289 \| 0.98 \| \| 0.0218 \| 4.77 \| 7450 \| 0.1610 \| 0.9733 \| \| 0.0077 \| 4.8 \| 7500 \| 0.1355 \| 0.98 \| \| 0.0096 \| 4.83 \| 7550 \| 0.1378 \| 0.9733 \| \| 0.008 \| 4.86 \| 7600 \| 0.1568 \| 0.9733 \| \| 0.0103 \| 4.89 \| 7650 \| 0.1388 \| 0.9733 \| \| 0.0009 \| 4.93 \| 7700 \| 0.1221 \| 0.98 \| \| 0.0287 \| 4.96 \| 7750 \| 0.1448 \| 0.9733 \| \| 0.01 \| 4.99 \| 7800 \| 0.1394 \| 0.9733 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
srnsrn120/whisper-small-hi	srnsrn120	2022-12-01T07:24:42Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-01T05:57:41Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - srnsrn120 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 40.772877338525355 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - srnsrn120 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Wer: 40.7729 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2442 \| 0.98 \| 400 \| 0.3428 \| 40.7729 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
minhhoque/segformer-b0-scene-parse-150	minhhoque	2022-12-01T06:31:02Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "license:other", "endpoints_compatible", "region:us" ]	null	2022-12-01T05:42:03Z	--- license: other tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 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. --> # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Evelyn18/roberta-base-spanish-squades-becasv3	Evelyn18	2022-12-01T06:27:03Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:becasv2", "endpoints_compatible", "region:us" ]	question-answering	2022-07-19T13:20:41Z	--- tags: - generated_from_trainer datasets: - becasv2 model-index: - name: roberta-base-spanish-squades-becasv3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-spanish-squades-modelo-robertav3 This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 1.6939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 11 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 1.7032 \| \| No log \| 2.0 \| 10 \| 1.6939 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
fanpu/model_output_original_subreddit-cmu_1	fanpu	2022-12-01T05:40:32Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-01T05:04:04Z	--- license: mit tags: - generated_from_trainer model-index: - name: model_output_original_subreddit-cmu_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_output_original_subreddit-cmu_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
dicquiloan/q-FrozenLake-v1-4x4-noSlippery	dicquiloan	2022-12-01T05:11:21Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-11-25T23:37:21Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="dicquiloan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
minhhoque/distilbert-base-uncased_imdb_reviews	minhhoque	2022-12-01T04:56:58Z	118	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T02:21:56Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased_imdb_reviews results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_imdb_reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.385 \| 0.4 \| 500 \| 0.3796 \| \| 0.2803 \| 0.8 \| 1000 \| 0.2549 \| \| 0.208 \| 1.2 \| 1500 \| 0.3218 \| \| 0.1655 \| 1.6 \| 2000 \| 0.2577 \| \| 0.153 \| 2.0 \| 2500 \| 0.2718 \| \| 0.0552 \| 2.4 \| 3000 \| 0.3514 \| \| 0.0667 \| 2.8 \| 3500 \| 0.3427 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-kitchen_and_dining-1-16-5	fathyshalab	2022-12-01T04:45:46Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:15:17Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-1-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-kitchen_and_dining-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7421 \| 1.0 \| 1 \| 2.5878 \| 0.2012 \| \| 2.1065 \| 2.0 \| 2 \| 2.4975 \| 0.2012 \| \| 1.5994 \| 3.0 \| 3 \| 2.4274 \| 0.2249 \| \| 1.1739 \| 4.0 \| 4 \| 2.3808 \| 0.2456 \| \| 1.083 \| 5.0 \| 5 \| 2.3560 \| 0.2692 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/all-roberta-large-v1-credit_cards-8-16-5	fathyshalab	2022-12-01T03:58:24Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:12:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-8-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-credit_cards-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.75 \| 1.0 \| 1 \| 2.5769 \| 0.2389 \| \| 2.178 \| 2.0 \| 2 \| 2.4879 \| 0.2389 \| \| 1.769 \| 3.0 \| 3 \| 2.4180 \| 0.2566 \| \| 1.4703 \| 4.0 \| 4 \| 2.3657 \| 0.3097 \| \| 1.2711 \| 5.0 \| 5 \| 2.3376 \| 0.3186 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
DLL888/roberta-base-squad	DLL888	2022-12-01T03:55:20Z	63	0	transformers	[ "transformers", "tf", "tensorboard", "roberta", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-12-01T03:24:46Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: DLL888/roberta-base-squad 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. --> # DLL888/roberta-base-squad 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: 0.7054 - Train End Logits Accuracy: 0.8022 - Train Start Logits Accuracy: 0.7586 - Validation Loss: 0.8224 - Validation End Logits Accuracy: 0.7692 - Validation Start Logits Accuracy: 0.7402 - 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', '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': 10570, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 500, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.1613 \| 0.7038 \| 0.6632 \| 0.8676 \| 0.7626 \| 0.7342 \| 0 \| \| 0.7054 \| 0.8022 \| 0.7586 \| 0.8224 \| 0.7692 \| 0.7402 \| 1 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
Roman1998/tesorflowTest	Roman1998	2022-12-01T03:48:43Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T03:47:33Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tesorflowTest 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. --> # tesorflowTest 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: - Train Loss: 0.1220 - 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', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 0.2863 \| 0 \| \| 0.1671 \| 1 \| \| 0.1220 \| 2 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01	Zengwei	2022-12-01T03:29:09Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-12-01T02:01:38Z	This repo contains pre-trained models, checkpoints, training logs and decoding results of the following pull-request: https://github.com/k2-fsa/icefall/pull/683
huggingtweets/prezoh	huggingtweets	2022-12-01T03:28:19Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/prezoh/1669865295720/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590487732387733505/JiMBIJrZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">prezoh</div> <div style="text-align: center; font-size: 14px;">@prezoh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from prezoh. \| Data \| prezoh \| \| --- \| --- \| \| Tweets downloaded \| 3158 \| \| Retweets \| 30 \| \| Short tweets \| 905 \| \| Tweets kept \| 2223 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/278h7rp5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @prezoh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3e7ukxmi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3e7ukxmi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/prezoh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Rastadayon/wav2vec2-large-xls-r-300m-dutch-colab	Rastadayon	2022-12-01T03:20:45Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-30T20:59:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-dutch-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-dutch-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5834 - eval_wer: 0.3471 - eval_cer: 0.1181 - eval_runtime: 338.6313 - eval_samples_per_second: 14.582 - eval_steps_per_second: 1.825 - epoch: 14.87 - step: 4000 ## 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: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
itisphilippe/StackOverflowNER	itisphilippe	2022-12-01T02:53:38Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-11-30T07:01:36Z	--- license: mit --- Models and other data for https://github.com/jeniyat/StackOverflowNER. Use `git lfs fetch --all` to download all files. Please note that folders are stored decompressed due to HuggingFace file size limitations. The individual files in ./data_ctc/ are compressed using `gzip`, and can be decompressed using `gunzip -d .gz`. Intermediate model checkpoints have not been uploaded due to bandwidth limitations. BibTeX entry and citation info* ```bibtex @inproceedings{Tabassum20acl, title = {Code and Named Entity Recognition in StackOverflow}, author = "Tabassum, Jeniya and Maddela, Mounica and Xu, Wei and Ritter, Alan", booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2020} } ```
fathyshalab/all-roberta-large-v1-credit_cards-5-16-5	fathyshalab	2022-12-01T02:47:27Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:07:32Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-5-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-credit_cards-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.75 \| 1.0 \| 1 \| 2.5769 \| 0.2389 \| \| 2.178 \| 2.0 \| 2 \| 2.4879 \| 0.2389 \| \| 1.769 \| 3.0 \| 3 \| 2.4180 \| 0.2566 \| \| 1.4703 \| 4.0 \| 4 \| 2.3657 \| 0.3097 \| \| 1.2711 \| 5.0 \| 5 \| 2.3376 \| 0.3186 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
Yanjie24/t5-samsung	Yanjie24	2022-12-01T02:31:14Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-12-01T02:09:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-samsung results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 42.2345 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-samsung This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.8153 - Rouge1: 42.2345 - Rouge2: 18.983 - Rougel: 33.0073 - Rougelsum: 38.8755 - Gen Len: 36.4242 ## 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: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.0028 \| 1.0 \| 1841 \| 1.8153 \| 42.2345 \| 18.983 \| 33.0073 \| 38.8755 \| 36.4242 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
SathEdu/distilbert-base-uncased-finetuned-emotion	SathEdu	2022-12-01T02:15:54Z	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	2022-11-19T07:30:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9256889016417648 --- <!-- 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: 0.2222 - Accuracy: 0.9255 - F1: 0.9257 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7962 \| 1.0 \| 250 \| 0.3167 \| 0.903 \| 0.8984 \| \| 0.2475 \| 2.0 \| 500 \| 0.2222 \| 0.9255 \| 0.9257 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual	cardiffnlp	2022-12-01T02:11:30Z	114	4	transformers	[ "transformers", "pytorch", "bert", "text-classification", "dataset:cardiffnlp/tweet_sentiment_multilingual", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-01T02:07:08Z	--- datasets: - cardiffnlp/tweet_sentiment_multilingual metrics: - f1 - accuracy model-index: - name: cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual results: - task: type: text-classification name: Text Classification dataset: name: cardiffnlp/tweet_sentiment_multilingual type: all split: test metrics: - name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6169540229885058 - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6168385894019698 - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all) type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6169540229885058 pipeline_tag: text-classification widget: - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}} example_title: "topic_classification 1" - text: Yes, including Medicare and social security saving👍 example_title: "sentiment 1" - text: All two of them taste like ass. example_title: "offensive 1" - text: If you wanna look like a badass, have drama on social media example_title: "irony 1" - text: Whoever just unfollowed me you a bitch example_title: "hate 1" - text: I love swimming for the same reason I love meditating...the feeling of weightlessness. example_title: "emotion 1" - text: Beautiful sunset last night from the pontoon @TupperLakeNY example_title: "emoji 1" --- # cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [`cardiffnlp/tweet_sentiment_multilingual (all)`](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp). Training split is `train` and parameters have been tuned on the validation split `validation`. Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual/raw/main/metric.json)). - F1 (micro): 0.6169540229885058 - F1 (macro): 0.6168385894019698 - Accuracy: 0.6169540229885058 ### Usage Install tweetnlp via pip. ```shell pip install tweetnlp ``` Load the model in python. ```python import tweetnlp model = tweetnlp.Classifier("cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual", max_length=128) model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}') ``` ### Reference ``` @inproceedings{dimosthenis-etal-2022-twitter, title = "{T}witter {T}opic {C}lassification", author = "Antypas, Dimosthenis and Ushio, Asahi and Camacho-Collados, Jose and Neves, Leonardo and Silva, Vitor and Barbieri, Francesco", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics" } ```
fathyshalab/all-roberta-large-v1-credit_cards-3-16-5	fathyshalab	2022-12-01T01:59:23Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T18:04:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-3-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-credit_cards-3-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.75 \| 1.0 \| 1 \| 2.5769 \| 0.2389 \| \| 2.178 \| 2.0 \| 2 \| 2.4879 \| 0.2389 \| \| 1.769 \| 3.0 \| 3 \| 2.4180 \| 0.2566 \| \| 1.4703 \| 4.0 \| 4 \| 2.3657 \| 0.3097 \| \| 1.2711 \| 5.0 \| 5 \| 2.3376 \| 0.3186 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
DiogoSabec/BOT	DiogoSabec	2022-12-01T01:33:17Z	119	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-01T00:40:43Z	--- tags: - conversational ---
sd-dreambooth-library/crisimsestelle	sd-dreambooth-library	2022-12-01T01:20:13Z	52	0	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-29T16:50:18Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### Contain Real Ingredients on Stable Diffusion 2 via Dreambooth #### model by estelleflores ![image 20](https://i.ibb.co/6b0ZBtr/1.png) This is a Stable Diffusion 2 model fine-tuned to the CRIsimsEstelle concept taught to Stable Diffusion with Dreambooth. ![image 21](https://i.ibb.co/7tL6Vs4/59.png) It can be used by modifying the `instance_prompt`: 3d render in \<cri-sims> style or just using the initializer \'\<cri-sims> style' somewhere in your prompt will work. ![image 22](https://i.ibb.co/H21FX8Q/37.png) Images used for training this concept come from the [project Contain Real Ingredients](https://teia.art/estelle), an art practice inside the game The Sims 4 by artist Estelle Flores: ![image 0](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/13.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/7.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/11.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/10.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/2.jpeg) ![image 6](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/8.jpeg) ![image 7](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/3.jpeg) ![image 8](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/12.jpeg) ![image 9](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/4.jpeg) ![image 10](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/5.jpeg) ![image 11](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/1.jpeg) ![image 12](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/14.jpeg) ![image 13](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/9.jpeg) ![image 14](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/17.jpeg) ![image 15](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/16.jpeg) ![image 16](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/18.jpeg) ![image 17](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/6.jpeg) ![image 18](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/15.jpeg) ![image 19](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/19.jpeg) You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
wmFrank/sample-factory-2-megaverse	wmFrank	2022-12-01T00:50:17Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-01T00:49:58Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TowerBuilding type: TowerBuilding metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the TowerBuilding environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
fathyshalab/all-roberta-large-v1-banking-9-16-5	fathyshalab	2022-12-01T00:47:58Z	108	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T18:53:06Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-9-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7211 \| 1.0 \| 1 \| 2.5748 \| 0.2301 \| \| 2.2722 \| 2.0 \| 2 \| 2.4566 \| 0.3009 \| \| 1.9185 \| 3.0 \| 3 \| 2.3596 \| 0.3805 \| \| 1.667 \| 4.0 \| 4 \| 2.2920 \| 0.3982 \| \| 1.4704 \| 5.0 \| 5 \| 2.2565 \| 0.3982 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/all-roberta-large-v1-banking-8-16-5	fathyshalab	2022-12-01T00:21:13Z	109	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T18:30:16Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-8-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7211 \| 1.0 \| 1 \| 2.5748 \| 0.2301 \| \| 2.2722 \| 2.0 \| 2 \| 2.4566 \| 0.3009 \| \| 1.9185 \| 3.0 \| 3 \| 2.3596 \| 0.3805 \| \| 1.667 \| 4.0 \| 4 \| 2.2920 \| 0.3982 \| \| 1.4704 \| 5.0 \| 5 \| 2.2565 \| 0.3982 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
Taqwa/whisper-small-hi	Taqwa	2022-12-01T00:05:15Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-26T20:53:48Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 35.74028612545501 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [Taqwa/whisper-small-hiTaqwa](https://huggingface.co/Taqwa/whisper-small-hiTaqwa) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3353 - Wer: 35.7403 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0762 \| 0.31 \| 125 \| 0.2818 \| 33.3573 \| \| 0.0653 \| 0.61 \| 250 \| 0.2930 \| 33.9584 \| \| 0.062 \| 0.92 \| 375 \| 0.3060 \| 34.7456 \| \| 0.0518 \| 1.22 \| 500 \| 0.3353 \| 35.7403 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-banking-6-16-5	fathyshalab	2022-11-30T23:26:46Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T17:44:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-6-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7211 \| 1.0 \| 1 \| 2.5748 \| 0.2301 \| \| 2.2722 \| 2.0 \| 2 \| 2.4566 \| 0.3009 \| \| 1.9185 \| 3.0 \| 3 \| 2.3596 \| 0.3805 \| \| 1.667 \| 4.0 \| 4 \| 2.2920 \| 0.3982 \| \| 1.4704 \| 5.0 \| 5 \| 2.2565 \| 0.3982 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
CarperAI/randomwalks	CarperAI	2022-11-30T22:22:26Z	164	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-28T17:23:14Z	--- license: mit --- This is a pretrained model used in [PPO toy example](https://github.com/CarperAI/trlx/tree/main/examples/randomwalks) from [CarperAI/trlX](https://github.com/CarperAI/trlx/tree/main/examples/randomwalks)
deblagoj/distilbert-base-uncased-finetuned-emotion	deblagoj	2022-11-30T22:05:20Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-07T18:26:07Z	--- 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: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.919 - name: F1 type: f1 value: 0.9190903538852266 --- <!-- 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: 0.2225 - Accuracy: 0.919 - F1: 0.9191 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.814 \| 1.0 \| 250 \| 0.3153 \| 0.904 \| 0.9016 \| \| 0.2515 \| 2.0 \| 500 \| 0.2225 \| 0.919 \| 0.9191 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1
huggingtweets/poisonjr	huggingtweets	2022-11-30T21:50:40Z	119	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-30T21:49:04Z	--- language: en thumbnail: http://www.huggingtweets.com/poisonjr/1669845035713/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1582446449228382209/8JRLlVu__400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">gale na</div> <div style="text-align: center; font-size: 14px;">@poisonjr</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from gale na. \| Data \| gale na \| \| --- \| --- \| \| Tweets downloaded \| 3204 \| \| Retweets \| 731 \| \| Short tweets \| 782 \| \| Tweets kept \| 1691 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33t9oiqy/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @poisonjr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3c5vn57r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3c5vn57r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/poisonjr') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
manirai91/enlm-roberta-final	manirai91	2022-11-30T21:40:33Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-28T03:41:11Z	--- tags: - generated_from_trainer model-index: - name: enlm-roberta-final 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. --> # enlm-roberta-final This model is a fine-tuned version of [manirai91/enlm-roberta](https://huggingface.co/manirai91/enlm-roberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4187 ## 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: 6e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 8192 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5245 \| 0.34 \| 160 \| 1.4187 \| \| 1.5245 \| 0.69 \| 320 \| 1.4183 \| \| 1.5259 \| 1.03 \| 480 \| 1.4177 \| \| 1.5265 \| 1.37 \| 640 \| 1.4185 \| \| 1.5245 \| 1.72 \| 800 \| 1.4190 \| \| 1.5241 \| 2.06 \| 960 \| 1.4172 \| \| 1.5227 \| 2.4 \| 1120 \| 1.4165 \| \| 1.5226 \| 2.75 \| 1280 \| 1.4152 \| \| 1.522 \| 3.09 \| 1440 \| 1.4190 \| \| 1.5243 \| 3.43 \| 1600 \| 1.4177 \| \| 1.5213 \| 3.78 \| 1760 \| 1.4134 \| \| 1.524 \| 4.12 \| 1920 \| 1.4140 \| \| 1.5223 \| 4.46 \| 2080 \| 1.4173 \| \| 1.5236 \| 4.81 \| 2240 \| 1.4121 \| \| 1.5239 \| 5.15 \| 2400 \| 1.4186 \| \| 1.5203 \| 5.49 \| 2560 \| 1.4154 \| \| 1.522 \| 5.84 \| 2720 \| 1.4162 \| \| 1.5209 \| 6.18 \| 2880 \| 1.4154 \| \| 1.5196 \| 6.52 \| 3040 \| 1.4153 \| \| 1.5209 \| 6.87 \| 3200 \| 1.4122 \| \| 1.5202 \| 7.21 \| 3360 \| 1.4146 \| \| 1.5192 \| 7.55 \| 3520 \| 1.4141 \| \| 1.5215 \| 7.9 \| 3680 \| 1.4123 \| \| 1.5228 \| 8.24 \| 3840 \| 1.4147 \| \| 1.5222 \| 8.58 \| 4000 \| 1.4144 \| \| 1.5201 \| 8.93 \| 4160 \| 1.4173 \| \| 1.523 \| 9.27 \| 4320 \| 1.4171 \| \| 1.5212 \| 9.61 \| 4480 \| 1.4149 \| \| 1.522 \| 9.96 \| 4640 \| 1.4187 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
huggingtweets/blewglass	huggingtweets	2022-11-30T21:38:03Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-30T21:36:41Z	--- language: en thumbnail: http://www.huggingtweets.com/blewglass/1669844278462/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1589805873366724610/ifGVL-6g_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">come back clammy</div> <div style="text-align: center; font-size: 14px;">@blewglass</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from come back clammy. \| Data \| come back clammy \| \| --- \| --- \| \| Tweets downloaded \| 3174 \| \| Retweets \| 582 \| \| Short tweets \| 317 \| \| Tweets kept \| 2275 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cybl684/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @blewglass's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zifv54gk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zifv54gk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/blewglass') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
danielsaggau/scotus_py	danielsaggau	2022-11-30T21:12:28Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "longformer", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-30T21:12:16Z	--- 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 512 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 970 with parameters: ``` {'batch_size': 4, '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": 970, "warmup_steps": 97, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LongformerModel (1): Pooling({'word_embedding_dimension': 512, '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 -->
fathyshalab/all-roberta-large-v1-banking-1-16-5	fathyshalab	2022-11-30T21:09:17Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T15:45:09Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4479 - Accuracy: 0.2301 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.716 \| 1.0 \| 1 \| 2.6641 \| 0.1327 \| \| 2.1674 \| 2.0 \| 2 \| 2.5852 \| 0.1858 \| \| 1.7169 \| 3.0 \| 3 \| 2.5202 \| 0.2035 \| \| 1.3976 \| 4.0 \| 4 \| 2.4729 \| 0.2124 \| \| 1.2503 \| 5.0 \| 5 \| 2.4479 \| 0.2301 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
gavin124/gpt2-finetuned-cnn-summarization-v1	gavin124	2022-11-30T20:40:22Z	80	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "summarization", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-11-30T15:33:05Z	--- license: mit tags: - summarization - generated_from_trainer model-index: - name: gpt2-finetuned-cnn-summarization-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-finetuned-cnn-summarization-v1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1709 ## 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 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.2025 \| 1.0 \| 5742 \| 2.1636 \| \| 2.0428 \| 2.0 \| 11484 \| 2.1659 \| \| 1.9681 \| 3.0 \| 17226 \| 2.1709 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pere/whisper-medium-NST-uf-linlr	pere	2022-11-30T19:24:35Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "NbAiLab/NST", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-28T07:44:59Z	--- license: apache-2.0 tags: - hf-asr-leaderboard - automatic-speech-recognition - NbAiLab/NST - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-NST-uf-linlr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-NST-uf-linlr This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set: - Loss: 0.3007 - Wer: 9.1220 ## 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: 72 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.2046 \| 0.05 \| 1000 \| 0.3426 \| 15.2794 \| \| 0.148 \| 0.1 \| 2000 \| 0.3284 \| 10.8324 \| \| 0.121 \| 0.15 \| 3000 \| 0.3092 \| 12.8848 \| \| 0.1089 \| 0.2 \| 4000 \| 0.2808 \| 10.4903 \| \| 0.0976 \| 0.25 \| 5000 \| 0.2617 \| 9.9202 \| \| 0.0901 \| 0.3 \| 6000 \| 0.2604 \| 21.8928 \| \| 0.0834 \| 0.35 \| 7000 \| 0.2877 \| 9.3501 \| \| 0.0825 \| 0.4 \| 8000 \| 0.2794 \| 9.3501 \| \| 0.0553 \| 1.05 \| 9000 \| 0.2845 \| 9.5781 \| \| 0.0472 \| 1.1 \| 10000 \| 0.2814 \| 24.1733 \| \| 0.0409 \| 1.15 \| 11000 \| 0.3084 \| 8.0958 \| \| 0.041 \| 1.2 \| 12000 \| 0.2865 \| 9.2360 \| \| 0.0353 \| 1.25 \| 13000 \| 0.2828 \| 6.4994 \| \| 0.0348 \| 1.3 \| 14000 \| 0.2708 \| 7.5257 \| \| 0.0349 \| 1.35 \| 15000 \| 0.2842 \| 23.0331 \| \| 0.0361 \| 1.4 \| 16000 \| 0.2769 \| 10.1482 \| \| 0.0249 \| 2.04 \| 17000 \| 0.2935 \| 8.8940 \| \| 0.0204 \| 2.09 \| 18000 \| 0.2874 \| 12.4287 \| \| 0.0175 \| 2.14 \| 19000 \| 0.2882 \| 12.9989 \| \| 0.0197 \| 2.19 \| 20000 \| 0.3007 \| 9.1220 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1
ShadoWxShinigamI/vray-render	ShadoWxShinigamI	2022-11-30T19:05:09Z	0	54	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-11-30T18:55:42Z	--- license: creativeml-openrail-m --- ##Textual Inversion Embedding For SD 2.0 (768) by ShadoWxShinigamI 44 Images, 768x768, Batch Size 4, Gradient Accumulation 11, Vectors - 6, Steps 500 I love the V-Ray Render style, and wanted to try making an embed for a highly varied style. This is my attempt. It is definitely not perfect. It gives slightly soft outputs, I will revisit this embed once i get the hang of training efficiently. In case of any errors when using this embedd with Auto1111, try out the png embed instead. Examples:- ![cabin vray.png](https://s3.amazonaws.com/moonup/production/uploads/1669834885077-633a520aecbd8b19357b4806.png) ![car.png](https://s3.amazonaws.com/moonup/production/uploads/1669834906577-633a520aecbd8b19357b4806.png) ![lion v-ray.png](https://s3.amazonaws.com/moonup/production/uploads/1669834916379-633a520aecbd8b19357b4806.png) ![ship.png](https://s3.amazonaws.com/moonup/production/uploads/1669834936057-633a520aecbd8b19357b4806.png) ![00019-3721583948.png](https://s3.amazonaws.com/moonup/production/uploads/1669834960130-633a520aecbd8b19357b4806.png) ![human.png](https://s3.amazonaws.com/moonup/production/uploads/1669835006537-633a520aecbd8b19357b4806.png)
abdalrahmanshahrour/ShahrourDamageLenses	abdalrahmanshahrour	2022-11-30T19:01:07Z	0	0	null	[ "region:us" ]	null	2022-11-30T18:37:14Z	# Damage-detection ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/damage-prj.png?raw=true) prj files: ## step 1: download all files 1. clone my repo ```python git clone https://github.com/AbdelrahmanShahrour/Damage-detection.git ``` 2. get data and models files from [here](https://drive.google.com/drive/folders/1vXaD8z2J_kbh8oDU4rNcyuPoXgjOSRKs?usp=sharing) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-34-48.png?raw=true) ## step 2: creat venv and install all lib ```python python3 -m venv env ``` ```python source env/bin/activate ``` ```python pip3 install -r requirements.txt ``` ## step 3: open jupyter notebook ```python jupyter notebook ``` ## step 4: open `output.ipynb` and run all Cells ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-19.png?raw=true) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-28.png?raw=true) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-37.png?raw=true) # step 5: enjoy and develop this project and share his with me 😁👍🏻
andrewzhang505/isaacgym_humanoid	andrewzhang505	2022-11-30T19:00:40Z	9	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-30T01:40:53Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid type: Humanoid metrics: - type: mean_reward value: 8418.38 +/- 1855.54 name: mean_reward verified: false --- A(n) APPO model trained on the Humanoid environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r andrewzhang505/isaacgym_humanoid ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.isaacgym_examples.enjoy_isaacgym --algo=APPO --env=Humanoid --train_dir=./train_dir --experiment=isaacgym_humanoid ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.isaacgym_examples.train_isaacgym --algo=APPO --env=Humanoid --train_dir=./train_dir --experiment=isaacgym_humanoid --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Crushtoe/GODEL-v1_1-base-seq2seq-vangluss	Crushtoe	2022-11-30T18:59:59Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-30T17:39:03Z	--- tags: - conversational --- # Vangluss: Bot Edition Trying (and failing) to use GODEL in place of DialoGPT.
htermotto/distilbert-base-uncased-finetuned-sngp-squad-seed-42	htermotto	2022-11-30T18:58:48Z	33	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-11-30T10:31:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-sngp-squad-seed-42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sngp-squad-seed-42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9074 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4521 \| 1.0 \| 8248 \| 2.0439 \| \| 2.1298 \| 2.0 \| 16496 \| 1.9074 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
jmunoz/finetuning-sentiment-model-3000-samples	jmunoz	2022-11-30T18:41:53Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T22:47:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cpu - Datasets 1.2.1 - Tokenizers 0.12.1
ximboleta/Glebbo	ximboleta	2022-11-30T18:39:44Z	0	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2022-11-30T18:39:44Z	--- license: cc-by-nc-nd-4.0 ---
edgertej/poebert-checkpoint-finetuned-poetry-foundation-2	edgertej	2022-11-30T17:14:10Z	78	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-30T16:14:34Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: edgertej/poebert-checkpoint-finetuned-poetry-foundation-2 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. --> # edgertej/poebert-checkpoint-finetuned-poetry-foundation-2 This model is a fine-tuned version of [edgertej/poebert-checkpoint-finetuned-poetry-foundation](https://huggingface.co/edgertej/poebert-checkpoint-finetuned-poetry-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8653 - Validation Loss: 3.5986 - 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', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.9003 \| 3.6587 \| 0 \| \| 3.8970 \| 3.6169 \| 1 \| \| 3.8653 \| 3.5986 \| 2 \| ### Framework versions - Transformers 4.19.2 - TensorFlow 2.9.1 - Datasets 2.4.0 - Tokenizers 0.12.1
alexrofail/sd-class-butterflies-32	alexrofail	2022-11-30T16:31:22Z	33	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-30T16:29:47Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. In this run I just ran each cell of the NB to understand what is going on. Experimentation to follow 🙏 ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(alexrofail/sd-class-butterflies-32) image = pipeline().images[0] image ```
fathyshalab/all-roberta-large-v1-banking-17-16-5	fathyshalab	2022-11-30T15:28:05Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:57:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-17-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-17-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8182 \| 1.0 \| 1 \| 2.7709 \| 0.0356 \| \| 2.6751 \| 2.0 \| 2 \| 2.7579 \| 0.0578 \| \| 2.5239 \| 3.0 \| 3 \| 2.7509 \| 0.0622 \| \| 2.4346 \| 4.0 \| 4 \| 2.7470 \| 0.0756 \| \| 2.4099 \| 5.0 \| 5 \| 2.7452 \| 0.0756 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/all-roberta-large-v1-banking-16-16-5	fathyshalab	2022-11-30T15:24:44Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:34:27Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-16-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-16-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8182 \| 1.0 \| 1 \| 2.7709 \| 0.0356 \| \| 2.6751 \| 2.0 \| 2 \| 2.7579 \| 0.0578 \| \| 2.5239 \| 3.0 \| 3 \| 2.7509 \| 0.0622 \| \| 2.4346 \| 4.0 \| 4 \| 2.7470 \| 0.0756 \| \| 2.4099 \| 5.0 \| 5 \| 2.7452 \| 0.0756 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/all-roberta-large-v1-banking-14-16-5	fathyshalab	2022-11-30T15:17:52Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T20:48:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-14-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-14-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8182 \| 1.0 \| 1 \| 2.7709 \| 0.0356 \| \| 2.6751 \| 2.0 \| 2 \| 2.7579 \| 0.0578 \| \| 2.5239 \| 3.0 \| 3 \| 2.7509 \| 0.0622 \| \| 2.4346 \| 4.0 \| 4 \| 2.7470 \| 0.0756 \| \| 2.4099 \| 5.0 \| 5 \| 2.7452 \| 0.0756 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
gd1m3y/sentiment_bert	gd1m3y	2022-11-30T15:04:50Z	159	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-30T14:20:13Z	--- tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: sentiment_bert results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank config: sentences_66agree split: train args: sentences_66agree metrics: - name: Accuracy type: accuracy value: 0.9360189573459715 --- <!-- 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_bert This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.3754 - Accuracy: 0.9360 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
tomekkorbak/compassionate_hypatia	tomekkorbak	2022-11-30T14:23:57Z	103	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-29T19:22:43Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: compassionate_hypatia 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. --> # compassionate_hypatia This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'compassionate_hypatia', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3kybxs99
yorko/sd-class-butterflies-32	yorko	2022-11-30T13:41:32Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-30T13:30:35Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained("yorko/sd-class-butterflies-32") image = pipeline().images[0] image ```
nixmaverick1997/app-setfit-classifier	nixmaverick1997	2022-11-30T13:32:26Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-classifier", "transformers", "sentiment-classifier", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-31T16:11:57Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-classifier - transformers - sentiment-classifier --- # SetFit Sentiment Classifier This is a variant of the [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 --> Uses Siamese and triplet network structures to generate semantically meaningful sentence embeddings ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install setfit ``` Then you can use the model like this: ```python from setfit import SetFitModel sentences = ["This is an example sentence", "Each sentence is converted"] model = SetFitModel.from_pretrained("nixmaverick1997/app-setfit-classifier") 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("nixmaverick1997/app-setfit-classifier") model = AutoModel.from_pretrained("nixmaverick1997/app-setfit-classifier") # 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 --> Loss class = CosineSimilarityLoss ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 640 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": 640, "warmup_steps": 64, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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 -->
Watwat100/256data	Watwat100	2022-11-30T13:00:52Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-30T13:00:38Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 1576 with parameters: ``` {'batch_size': 13, '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": 3, "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": 4728, "warmup_steps": 473, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
kejian/immaculate-filtering	kejian	2022-11-30T12:11:34Z	104	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-29T15:12:15Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: immaculate-filtering 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. --> # immaculate-filtering This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'immaculate-filtering', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3jjalm0n
GujjetiNagaraju/xlm-roberta-base-finetuned-Telugu_NLP	GujjetiNagaraju	2022-11-30T12:10:20Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-30T11:05:48Z	--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-Telugu_NLP 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-Telugu_NLP 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: 1.9986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4192 \| 1.0 \| 1250 \| 2.1557 \| \| 2.2859 \| 2.0 \| 2500 \| 2.0632 \| \| 2.2311 \| 3.0 \| 3750 \| 2.0083 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
roscazo/DisTEMIST_fine_tuned_sentence	roscazo	2022-11-30T11:30:15Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-23T09:51:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: DisTEMIST_fine_tuned_sentence 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. --> # DisTEMIST_fine_tuned_sentence This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2164 - Precision: 0.6069 - Recall: 0.6401 - F1: 0.6231 ## 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: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=2.6e-09 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 73 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.1166 \| 1.0 \| 1099 \| 0.1152 \| 0.5214 \| 0.6433 \| 0.5760 \| \| 0.0718 \| 2.0 \| 2198 \| 0.1096 \| 0.6015 \| 0.6297 \| 0.6153 \| \| 0.0438 \| 3.0 \| 3297 \| 0.1517 \| 0.6573 \| 0.5895 \| 0.6215 \| \| 0.0293 \| 4.0 \| 4396 \| 0.1496 \| 0.6212 \| 0.6198 \| 0.6205 \| \| 0.0179 \| 5.0 \| 5495 \| 0.1665 \| 0.5670 \| 0.6505 \| 0.6059 \| \| 0.0119 \| 6.0 \| 6594 \| 0.1602 \| 0.6035 \| 0.6379 \| 0.6202 \| \| 0.0078 \| 7.0 \| 7693 \| 0.1844 \| 0.6008 \| 0.6347 \| 0.6173 \| \| 0.0041 \| 8.0 \| 8792 \| 0.2019 \| 0.6006 \| 0.6288 \| 0.6144 \| \| 0.0026 \| 9.0 \| 9891 \| 0.2075 \| 0.6015 \| 0.6270 \| 0.6140 \| \| 0.0014 \| 10.0 \| 10990 \| 0.2164 \| 0.6069 \| 0.6401 \| 0.6231 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

5p33ch3xpr/XLS-R_Finetuned

5p33ch3xpr

2022-12-01T20:55:03Z

107

1

transformers

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

automatic-speech-recognition

2022-10-29T16:49:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: XLS-R_Finetuned results: [] ---  # XLS-R_Finetuned This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2280 - Wer: 0.1725 ## 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: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.0094 | 0.32 | 500 | 3.5637 | 1.0 | | 3.3935 | 0.64 | 1000 | 2.6589 | 1.0 | | 1.5455 | 0.95 | 1500 | 0.7979 | 0.8225 | | 0.9065 | 1.27 | 2000 | 0.5392 | 0.6244 | | 0.7891 | 1.59 | 2500 | 0.3554 | 0.4551 | | 0.7118 | 1.91 | 3000 | 0.3682 | 0.4608 | | 0.6061 | 2.23 | 3500 | 0.3384 | 0.4416 | | 0.5536 | 2.54 | 4000 | 0.2987 | 0.4042 | | 0.547 | 2.86 | 4500 | 0.2892 | 0.3892 | | 0.4841 | 3.18 | 5000 | 0.2890 | 0.3690 | | 0.4434 | 3.5 | 5500 | 0.2605 | 0.3636 | | 0.4542 | 3.81 | 6000 | 0.2932 | 0.3773 | | 0.4171 | 4.13 | 6500 | 0.2768 | 0.3550 | | 0.3697 | 4.45 | 7000 | 0.2443 | 0.3382 | | 0.3776 | 4.77 | 7500 | 0.2572 | 0.3366 | | 0.3448 | 5.09 | 8000 | 0.2267 | 0.3006 | | 0.3285 | 5.4 | 8500 | 0.2377 | 0.3023 | | 0.3165 | 5.72 | 9000 | 0.2344 | 0.2888 | | 0.3194 | 6.04 | 9500 | 0.2228 | 0.2699 | | 0.2737 | 6.36 | 10000 | 0.2201 | 0.2754 | | 0.2986 | 6.68 | 10500 | 0.2413 | 0.2850 | | 0.2836 | 6.99 | 11000 | 0.2117 | 0.2629 | | 0.2467 | 7.31 | 11500 | 0.2408 | 0.2877 | | 0.2577 | 7.63 | 12000 | 0.2134 | 0.2448 | | 0.2503 | 7.95 | 12500 | 0.2260 | 0.2600 | | 0.2371 | 8.26 | 13000 | 0.2081 | 0.2379 | | 0.2303 | 8.58 | 13500 | 0.2322 | 0.2668 | | 0.213 | 8.9 | 14000 | 0.2339 | 0.2586 | | 0.2029 | 9.22 | 14500 | 0.2300 | 0.2704 | | 0.2146 | 9.54 | 15000 | 0.2321 | 0.2533 | | 0.2044 | 9.85 | 15500 | 0.2393 | 0.2685 | | 0.2008 | 10.17 | 16000 | 0.2193 | 0.2467 | | 0.182 | 10.49 | 16500 | 0.2323 | 0.2611 | | 0.2 | 10.81 | 17000 | 0.2188 | 0.2537 | | 0.1855 | 11.13 | 17500 | 0.2436 | 0.2523 | | 0.1745 | 11.44 | 18000 | 0.2351 | 0.2473 | | 0.1705 | 11.76 | 18500 | 0.2556 | 0.2663 | | 0.1745 | 12.08 | 19000 | 0.2189 | 0.2229 | | 0.1641 | 12.4 | 19500 | 0.2192 | 0.2342 | | 0.1546 | 12.71 | 20000 | 0.2432 | 0.2228 | | 0.1661 | 13.03 | 20500 | 0.2323 | 0.2242 | | 0.1436 | 13.35 | 21000 | 0.2554 | 0.2496 | | 0.1443 | 13.67 | 21500 | 0.2195 | 0.2026 | | 0.151 | 13.99 | 22000 | 0.2400 | 0.2201 | | 0.1333 | 14.3 | 22500 | 0.2181 | 0.2235 | | 0.137 | 14.62 | 23000 | 0.2400 | 0.2254 | | 0.1303 | 14.94 | 23500 | 0.2265 | 0.2088 | | 0.1386 | 15.26 | 24000 | 0.2330 | 0.2152 | | 0.1325 | 15.58 | 24500 | 0.2328 | 0.2127 | | 0.1227 | 15.89 | 25000 | 0.2375 | 0.2077 | | 0.1196 | 16.21 | 25500 | 0.2394 | 0.2144 | | 0.1197 | 16.53 | 26000 | 0.2591 | 0.2171 | | 0.1122 | 16.85 | 26500 | 0.2383 | 0.2066 | | 0.1093 | 17.16 | 27000 | 0.2254 | 0.2042 | | 0.105 | 17.48 | 27500 | 0.2330 | 0.2008 | | 0.0982 | 17.8 | 28000 | 0.2317 | 0.1902 | | 0.1072 | 18.12 | 28500 | 0.2332 | 0.1971 | | 0.1033 | 18.44 | 29000 | 0.2313 | 0.1923 | | 0.0982 | 18.75 | 29500 | 0.2344 | 0.1934 | | 0.103 | 19.07 | 30000 | 0.2295 | 0.1902 | | 0.0945 | 19.39 | 30500 | 0.2352 | 0.1976 | | 0.0892 | 19.71 | 31000 | 0.2414 | 0.1920 | | 0.1003 | 20.03 | 31500 | 0.2300 | 0.1879 | | 0.0861 | 20.34 | 32000 | 0.2215 | 0.1778 | | 0.0845 | 20.66 | 32500 | 0.2321 | 0.1866 | | 0.0858 | 20.98 | 33000 | 0.2311 | 0.1850 | | 0.0785 | 21.3 | 33500 | 0.2341 | 0.1874 | | 0.0786 | 21.61 | 34000 | 0.2322 | 0.1916 | | 0.0793 | 21.93 | 34500 | 0.2358 | 0.1846 | | 0.0772 | 22.25 | 35000 | 0.2234 | 0.1770 | | 0.0786 | 22.57 | 35500 | 0.2180 | 0.1758 | | 0.0747 | 22.89 | 36000 | 0.2269 | 0.1830 | | 0.0734 | 23.2 | 36500 | 0.2320 | 0.1860 | | 0.067 | 23.52 | 37000 | 0.2324 | 0.1797 | | 0.0733 | 23.84 | 37500 | 0.2324 | 0.1772 | | 0.0701 | 24.16 | 38000 | 0.2293 | 0.1737 | | 0.0691 | 24.48 | 38500 | 0.2303 | 0.1750 | | 0.0613 | 24.79 | 39000 | 0.2280 | 0.1725 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

unstructuredio/donut-base-sroie

unstructuredio

2022-12-01T20:45:49Z

131

1

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2022-12-01T15:48:28Z

--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie-long results: [] ---  # donut-base-sroie-long This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.7.0 - Tokenizers 0.11.0

fathyshalab/all-roberta-large-v1-auto_and_commute-4-16-5

fathyshalab

2022-12-01T20:40:16Z

105

0

transformers

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

text-classification

2022-11-30T18:55:42Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-auto_and_commute-4-16-5 results: [] ---  # all-roberta-large-v1-auto_and_commute-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2614 - Accuracy: 0.4289 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 | | 2.267 | 2.0 | 2 | 2.4558 | 0.3533 | | 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 | | 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 | | 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

facebook/esm2_t36_3B_UR50D

facebook

2022-12-01T20:22:22Z

3,892,233

18

transformers

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

fill-mask

2022-10-13T12:38:30Z

--- license: mit widget: - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG" --- ## ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train: | Checkpoint name | Num layers | Num parameters | |------------------------------|----|----------| | [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B | | [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B | | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M | | [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M | | [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M | | [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M |

RomeroRZ/style-eternos

RomeroRZ

2022-12-01T20:16:55Z

0

11

null

[ "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-30T18:55:09Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- ![The San Juan Mountains are beautiful!](https://i.imgur.com/FqZXZBa.jpg "Eternos, surrealism stable diffusion model") ![The San Juan Mountains are beautiful!](https://i.imgur.com/PV30R4X.jpg "Eternos, surrealism stable diffusion model") #### Eternos - A surrealist / Minimalist model (2.0 work in progress yay!) With a base instances images generated from multiples surrealism art, some Dali touches and roman / greek architecture influences the 704 version is more abstract and less style transferrable due to the higher resolution towards all regular styles. Tips : use init image (even stretched) for non-standart resolution, it can help SD a lot to guide it :) instance prompt : **romerorzeternos** (optional) You can find cool prompts with associated outputs on my website : **[romerorz.art](https://www.romerorz.art/)**

fathyshalab/all-roberta-large-v1-auto_and_commute-2-16-5

fathyshalab

2022-12-01T19:52:06Z

105

0

transformers

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

text-classification

2022-11-30T18:51:39Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-auto_and_commute-2-16-5 results: [] ---  # all-roberta-large-v1-auto_and_commute-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2614 - Accuracy: 0.4289 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 | | 2.267 | 2.0 | 2 | 2.4558 | 0.3533 | | 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 | | 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 | | 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

ameerTelbani/ameeeer

ameerTelbani

2022-12-01T19:49:18Z

186

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-12-01T19:49:03Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: ameeeer results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8656716346740723 --- # ameeeer Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)

futuredatascience/from-classifier-v2

futuredatascience

2022-12-01T19:42:12Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-01T19:42:02Z

--- 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 53 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": 20, "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": 1060, "warmup_steps": 106, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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

nvidia/nemo-megatron-mt5-3B

nvidia

2022-12-01T19:34:02Z

30

12

nemo

[ "nemo", "pytorch", "seq2seq", "masked language modeling", "multilingual", "ja", "en", "it", "lv", "ru", "hu", "zh", "pl", "el", "de", "cs", "ko", "hi", "no", "da", "sk", "fr", "pt", "lt", "es", "nl", "sv", "ro", "fi", "dataset:mc4", "arxiv:2010.11934", "arxiv:1910.10683", "arxiv:1809.05053", "arxiv:1909.08053", "license:cc-by-4.0", "region:us" ]

null

2022-09-22T19:46:28Z

--- language: - ja - en - it - lv - ru - hu - zh - pl - el - de - cs - ko - hi - no - da - sk - fr - pt - lt - es - nl - sv - ro - fi library_name: nemo datasets: - mc4 tags: - pytorch - seq2seq - masked language modeling - multilingual license: cc-by-4.0 --- # NeMo Megatron-mT5 3B <style> img { display: inline; } </style> |[![Model architecture](https://img.shields.io/badge/Arch-Encoder--Decoder-green)](#model-architecture)|[![Model size](https://img.shields.io/badge/Params-3B-green)](#model-architecture)|[![Language](https://img.shields.io/badge/Language-Multilingual-green)](#datasets) ## Model Description NeMo Megatron-mT5 3B is a *multilingual* transformer-based masked language model. [mT5](https://arxiv.org/abs/2010.11934) [1] is a class of encoder-decoder models trained with a span-based masked language modeling objective on a dataset comprising documents from many different languages. We follow the [T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1) approach of pre-training using only the masked language modeling objective. It has Tensor Parallelism (TP) of 2, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU for inference and 2 A100 80G GPUs for finetuning. This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html). **NOTE**: Weights are distributed in bfloat16. ## List of Languages We pre-trained our mT5 model on the following languages from the [mC4](https://github.com/allenai/allennlp/discussions/5265) dataset. 1. Japanese 2. English 3. Italian 4. Latvian 5. Russian 6. Hungarian 7. Chinese 8. Polish 9. Greek 10. German 11. Czech 12. Korean 13. Hindi 14. Norwegian 15. Danish 16. Slovak 17. French 18. Portuguese 19. Lithuanian 20. Spanish 21. Dutch 22. Swedish 23. Romanian 24. Finnish *NOTE*: The English data used to train our model is the smaller "clean" version (C4) used in the [T5 paper](https://arxiv.org/abs/1910.10683) and not the larger one distributed as part of mC4. ## Getting started ### Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./ ``` ``` pip install nemo_toolkit['nlp']==1.12.0 ``` Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed - [https://developer.nvidia.com/nemo-megatron-open-beta?nvid=nv-int-tblg-249896](https://developer.nvidia.com/nemo-megatron-open-beta) ### Step 2: Run inference **Note.** The model has been trained with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1, but it should be possible to run inference with tensor parallel size 1 on most NVIDIA GPUs ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git checkout r1.12.0 python megatron_t5_eval.py \ --model_file nemo_megatron_mt5_3b_bf16_tp2.nemo \ --prompt "La capitale de la France est <mask>" \ --tensor_model_parallel_size 2 ``` The script will automatically replace all \<mask\> tokens with the appropriate sentinel tokens used while pre-training and attempt to fill them in autoregressively with greedy decoding. *Expected Response*: ``` { 'prompt': 'La capitale de la France est <mask>', 'completion': { 'text': 'Paris', 'tokens': [(4586, '▁Paris', 0.0)]}, 'masked_input': '▁La ▁capital e ▁de ▁la ▁France ▁est ▁<extra_id_0>' } ``` - prompt: The provided raw prompt as input - completion: - text: The final generated text from the model along with special/sentinel tokens besides \</s\> - tokens: Each individual subword that is generated along with its log-probability. - masked_input: The original raw prompt with <mask> replaced with appropriate sentinel tokens. ## Training Data The model was trained on the [mC4](https://github.com/allenai/allennlp/discussions/5265) dataset made available by AI2 and hosted on Huggingface. ## Evaluation results Zero-shot language transformer performance on the [XNLI](https://arxiv.org/abs/1809.05053) dataset for a model fine-tuned on MNLI. | English | Spanish | German | French | Chinese| |---|---| ---|---|---| |89.4|86.4|84.5|85.8|79.9| ## Limitations The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. ## References [1] [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [4] [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053) ## Licence License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.

alanila/autotrain-training-2307973005

alanila

2022-12-01T19:32:39Z

101

0

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:alanila/autotrain-data-training", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-12-01T19:29:41Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - alanila/autotrain-data-training co2_eq_emissions: emissions: 3.7679548759427006 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2307973005 - CO2 Emissions (in grams): 3.7680 ## Validation Metrics - Loss: 1.098 - Accuracy: 0.508 - Macro F1: 0.559 - Micro F1: 0.508 - Weighted F1: 0.452 - Macro Precision: 0.610 - Micro Precision: 0.508 - Weighted Precision: 0.537 - Macro Recall: 0.581 - Micro Recall: 0.508 - Weighted Recall: 0.508 ## 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/alanila/autotrain-training-2307973005 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alanila/autotrain-training-2307973005", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alanila/autotrain-training-2307973005", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

juancopi81/sd-class-cryptopunks-64

juancopi81

2022-12-01T19:24:22Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T19:21:45Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cryptopunks. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(juancopi81/sd-class-cryptopunks-64) image = pipeline().images[0] image ```

drewski/distilbert-base-uncased-finetuned-cola

drewski

2022-12-01T19:09:26Z

105

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

2022-12-01T18:58:28Z

--- 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: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5258252097729852 ---  # 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: 0.5561 - Matthews Correlation: 0.5258 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5245 | 1.0 | 535 | 0.5269 | 0.4122 | | 0.3513 | 2.0 | 1070 | 0.4976 | 0.4999 | | 0.2411 | 3.0 | 1605 | 0.5561 | 0.5258 | | 0.1907 | 4.0 | 2140 | 0.7641 | 0.5174 | | 0.1409 | 5.0 | 2675 | 0.8216 | 0.5189 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

shahukareem/sd-class-butterflies-64

shahukareem

2022-12-01T19:08:22Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T19:07:58Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('shahukareem/sd-class-butterflies-64') image = pipeline().images[0] image ```

fathyshalab/all-roberta-large-v1-home-9-16-5

fathyshalab

2022-12-01T19:02:24Z

105

0

transformers

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

text-classification

2022-11-30T18:47:41Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-9-16-5 results: [] ---  # all-roberta-large-v1-home-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7614 | 1.0 | 1 | 2.6146 | 0.1889 | | 2.2082 | 2.0 | 2 | 2.5232 | 0.2667 | | 1.8344 | 3.0 | 3 | 2.4516 | 0.2933 | | 1.4601 | 4.0 | 4 | 2.4033 | 0.3267 | | 1.2748 | 5.0 | 5 | 2.3789 | 0.3356 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

shrinivasbjoshi/r2-w266-setfit-mbti-multiclass-hypsearch-mpnet-nov30

shrinivasbjoshi

2022-12-01T18:17:06Z

6

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-01T18:16:51Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 2560 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": 4.2848872506915845e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2560, "warmup_steps": 256, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors

fathyshalab/all-roberta-large-v1-home-6-16-5

fathyshalab

2022-12-01T17:37:54Z

105

0

transformers

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

text-classification

2022-11-30T18:41:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-6-16-5 results: [] ---  # all-roberta-large-v1-home-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7614 | 1.0 | 1 | 2.6146 | 0.1889 | | 2.2082 | 2.0 | 2 | 2.5232 | 0.2667 | | 1.8344 | 3.0 | 3 | 2.4516 | 0.2933 | | 1.4601 | 4.0 | 4 | 2.4033 | 0.3267 | | 1.2748 | 5.0 | 5 | 2.3789 | 0.3356 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

VlakoResker/sd-class-butterflies-32

VlakoResker

2022-12-01T17:28:55Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T17:28:38Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('VlakoResker/sd-class-butterflies-32') image = pipeline().images[0] image ```

fathyshalab/all-roberta-large-v1-home-5-16-5

fathyshalab

2022-12-01T17:10:56Z

105

0

transformers

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

text-classification

2022-11-30T18:39:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-5-16-5 results: [] ---  # all-roberta-large-v1-home-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7614 | 1.0 | 1 | 2.6146 | 0.1889 | | 2.2082 | 2.0 | 2 | 2.5232 | 0.2667 | | 1.8344 | 3.0 | 3 | 2.4516 | 0.2933 | | 1.4601 | 4.0 | 4 | 2.4033 | 0.3267 | | 1.2748 | 5.0 | 5 | 2.3789 | 0.3356 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

bowwwave/sd-class-butterflies-32

bowwwave

2022-12-01T16:52:39Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T16:52:24Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('bowwwave/sd-class-butterflies-32') image = pipeline().images[0] image ```

varunsappa/finetuning-sentiment-model-3000-samples

varunsappa

2022-12-01T16:23:25Z

107

0

transformers

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

text-classification

2022-12-01T16:09:20Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8833333333333333 - name: F1 type: f1 value: 0.8844884488448845 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3132 - Accuracy: 0.8833 - F1: 0.8845 ## 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 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fanpu/model_output_original_subreddit-AskScienceFiction_1

fanpu

2022-12-01T15:13:34Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-01T06:12:05Z

--- license: mit tags: - generated_from_trainer model-index: - name: model_output_original_subreddit-AskScienceFiction_1 results: [] ---  # model_output_original_subreddit-AskScienceFiction_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.9231 | 0.3 | 500 | 3.8087 | | 3.8459 | 0.6 | 1000 | 3.7766 | | 3.8217 | 0.9 | 1500 | 3.7372 | | 3.6939 | 1.21 | 2000 | 3.7237 | | 3.6745 | 1.51 | 2500 | 3.7030 | | 3.6757 | 1.81 | 3000 | 3.6811 | | 3.5099 | 2.11 | 3500 | 3.6839 | | 3.505 | 2.41 | 4000 | 3.6709 | | 3.5232 | 2.71 | 4500 | 3.6515 | | 3.3416 | 3.01 | 5000 | 3.6563 | | 3.3725 | 3.32 | 5500 | 3.6496 | | 3.3672 | 3.62 | 6000 | 3.6373 | | 3.3495 | 3.92 | 6500 | 3.6280 | | 3.2464 | 4.22 | 7000 | 3.6439 | | 3.2467 | 4.52 | 7500 | 3.6415 | | 3.2473 | 4.82 | 8000 | 3.6407 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

YeaHi/diffusion

YeaHi

2022-12-01T15:11:02Z

0

null

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

null

2022-12-01T15:11:02Z

--- license: bigscience-openrail-m ---

arrafmousa/xlnet-base-cased-finetuned-squad

arrafmousa

2022-12-01T15:02:55Z

88

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlnet", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-12-01T13:27:48Z

--- license: mit tags: - generated_from_trainer model-index: - name: xlnet-base-cased-finetuned-squad results: [] ---  # xlnet-base-cased-finetuned-squad This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1093 ## 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 | 203 | 0.2186 | | No log | 2.0 | 406 | 0.1985 | | 0.4204 | 3.0 | 609 | 0.1093 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

mousaazari/t5-text2sql_v1

mousaazari

2022-12-01T13:46:33Z

15

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

2022-08-15T12:11:22Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-text2sql_v1 results: [] ---  # t5-text2sql_v1 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: 0.0772 - Rouge2 Precision: 0.8835 - Rouge2 Recall: 0.39 - Rouge2 Fmeasure: 0.5088 ## 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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | No log | 1.0 | 11 | 1.9420 | 0.0755 | 0.022 | 0.0323 | | No log | 2.0 | 22 | 1.2731 | 0.0912 | 0.0263 | 0.039 | | No log | 3.0 | 33 | 0.8717 | 0.0993 | 0.0284 | 0.0424 | | No log | 4.0 | 44 | 0.5705 | 0.1014 | 0.032 | 0.0464 | | No log | 5.0 | 55 | 0.3929 | 0.4151 | 0.1528 | 0.2149 | | No log | 6.0 | 66 | 0.2911 | 0.7778 | 0.351 | 0.4594 | | No log | 7.0 | 77 | 0.2290 | 0.781 | 0.3305 | 0.4395 | | No log | 8.0 | 88 | 0.1995 | 0.7381 | 0.2992 | 0.4018 | | No log | 9.0 | 99 | 0.1768 | 0.752 | 0.3147 | 0.4202 | | No log | 10.0 | 110 | 0.1554 | 0.7242 | 0.3136 | 0.412 | | No log | 11.0 | 121 | 0.1446 | 0.8128 | 0.3583 | 0.4694 | | No log | 12.0 | 132 | 0.1337 | 0.8194 | 0.3653 | 0.478 | | No log | 13.0 | 143 | 0.1264 | 0.8088 | 0.3564 | 0.4675 | | No log | 14.0 | 154 | 0.1170 | 0.8036 | 0.3502 | 0.462 | | No log | 15.0 | 165 | 0.1078 | 0.8851 | 0.3981 | 0.5188 | | No log | 16.0 | 176 | 0.1046 | 0.8716 | 0.3864 | 0.5054 | | No log | 17.0 | 187 | 0.1007 | 0.8753 | 0.3851 | 0.5042 | | No log | 18.0 | 198 | 0.0951 | 0.8756 | 0.3941 | 0.5126 | | No log | 19.0 | 209 | 0.0928 | 0.8414 | 0.3565 | 0.4708 | | No log | 20.0 | 220 | 0.0894 | 0.854 | 0.3642 | 0.4808 | | No log | 21.0 | 231 | 0.0863 | 0.8954 | 0.3954 | 0.5168 | | No log | 22.0 | 242 | 0.0832 | 0.888 | 0.3931 | 0.5122 | | No log | 23.0 | 253 | 0.0828 | 0.8835 | 0.39 | 0.5088 | | No log | 24.0 | 264 | 0.0820 | 0.8835 | 0.39 | 0.5088 | | No log | 25.0 | 275 | 0.0803 | 0.8835 | 0.39 | 0.5088 | | No log | 26.0 | 286 | 0.0792 | 0.8835 | 0.39 | 0.5088 | | No log | 27.0 | 297 | 0.0784 | 0.8761 | 0.3886 | 0.5066 | | No log | 28.0 | 308 | 0.0775 | 0.8835 | 0.39 | 0.5088 | | No log | 29.0 | 319 | 0.0772 | 0.8835 | 0.39 | 0.5088 | | No log | 30.0 | 330 | 0.0772 | 0.8835 | 0.39 | 0.5088 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

MGanesh29/distilbert-base-uncased-finetuned-cola-v5

MGanesh29

2022-12-01T13:40:01Z

107

0

transformers

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

text-classification

2022-12-01T10:54:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: distilbert-base-uncased-finetuned-cola-v5 results: [] ---  # distilbert-base-uncased-finetuned-cola-v5 This model is a fine-tuned version of [MGanesh29/distilbert-base-uncased-finetuned-cola-v5](https://huggingface.co/MGanesh29/distilbert-base-uncased-finetuned-cola-v5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9310 - Precision: 0.9310 - Recall: 0.9310 - F1: 0.9310 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 6.25 | 50 | 0.2638 | 0.9310 | 0.9310 | 0.9310 | 0.9310 | | No log | 12.5 | 100 | 0.2607 | 0.9310 | 0.9310 | 0.9310 | 0.9310 | | No log | 18.75 | 150 | 0.2643 | 0.9310 | 0.9310 | 0.9310 | 0.9310 | | No log | 25.0 | 200 | 0.2563 | 0.9310 | 0.9310 | 0.9310 | 0.9310 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-home-2-16-5

fathyshalab

2022-12-01T13:02:46Z

106

0

transformers

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

text-classification

2022-11-30T18:33:51Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-home-2-16-5 results: [] ---  # all-roberta-large-v1-home-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7614 | 1.0 | 1 | 2.6146 | 0.1889 | | 2.2082 | 2.0 | 2 | 2.5232 | 0.2667 | | 1.8344 | 3.0 | 3 | 2.4516 | 0.2933 | | 1.4601 | 4.0 | 4 | 2.4033 | 0.3267 | | 1.2748 | 5.0 | 5 | 2.3789 | 0.3356 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

manirai91/enlm-roberta-conll2003-final

manirai91

2022-12-01T12:28:17Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-12-01T11:02:56Z

--- tags: - generated_from_trainer datasets: - conll2003 model-index: - name: enlm-roberta-conll2003-final results: [] ---  # enlm-roberta-conll2003-final This model is a fine-tuned version of [manirai91/enlm-roberta-final](https://huggingface.co/manirai91/enlm-roberta-final) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-kitchen_and_dining-9-16-5

fathyshalab

2022-12-01T12:11:49Z

105

0

transformers

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

text-classification

2022-11-30T18:30:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-9-16-5 results: [] ---  # all-roberta-large-v1-kitchen_and_dining-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

ViktorDo/DistilBERT-POWO_MGH_Life_Form_Finetuned

ViktorDo

2022-12-01T11:55:19Z

110

0

transformers

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

text-classification

2022-12-01T11:45:50Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: DistilBERT-POWO_MGH_Life_Form_Finetuned results: [] ---  # DistilBERT-POWO_MGH_Life_Form_Finetuned 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: 0.3845 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5891 | 1.0 | 914 | 0.4130 | | 0.4207 | 2.0 | 1828 | 0.3868 | | 0.3722 | 3.0 | 2742 | 0.3845 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

hizak/sd-class-butterflies-64

hizak

2022-12-01T11:52:54Z

33

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T11:52:01Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(hizak/sd-class-butterflies-64) image = pipeline().images[0] image ```

fathyshalab/all-roberta-large-v1-kitchen_and_dining-8-16-5

fathyshalab

2022-12-01T11:45:56Z

105

0

transformers

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

text-classification

2022-11-30T18:28:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-8-16-5 results: [] ---  # all-roberta-large-v1-kitchen_and_dining-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/all-roberta-large-v1-kitchen_and_dining-7-16-5

fathyshalab

2022-12-01T11:20:14Z

103

0

transformers

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

text-classification

2022-11-30T18:26:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-7-16-5 results: [] ---  # all-roberta-large-v1-kitchen_and_dining-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

rls-telefonica/word_sense_mchoice_w_d_c

rls-telefonica

2022-12-01T11:13:31Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]

multiple-choice

2022-12-01T10:46:55Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: word_sense_mchoice_w_d_c results: [] ---  # word_sense_mchoice_w_d_c This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8885 - Accuracy: 0.8210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6904 | 1.0 | 531 | 0.5099 | 0.7913 | | 0.2393 | 2.0 | 1062 | 0.6351 | 0.8202 | | 0.0842 | 3.0 | 1593 | 0.8885 | 0.8210 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Earrr/Disco

Earrr

2022-12-01T11:10:20Z

0

null

[ "region:us" ]

null

2022-12-01T11:03:56Z

I don't own this model I uploaded it for personal use please contact me to delete if you are the auther [email protected]

hizak/sd-class-butterflies-32

hizak

2022-12-01T11:09:16Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-01T10:12:45Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(hizak/sd-class-butterflies-32) image = pipeline().images[0] image ```

AlekseyKorshuk/6.7b-ri-reproduce-combined-4-gpu-0-val

AlekseyKorshuk

2022-12-01T10:15:15Z

9

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:ChaiML/dalio_combined_v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-30T10:39:53Z

--- license: other tags: - generated_from_trainer datasets: - ChaiML/dalio_combined_v1 model-index: - name: 6.7b-ri-reproduce-combined-4-gpu-0-val results: [] ---  # 6.7b-ri-reproduce-combined-4-gpu-0-val This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the ChaiML/dalio_combined_v1 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: 9e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 100 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ConvLab/lava-policy-multiwoz20

ConvLab

2022-12-01T09:59:09Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "license:apache-2.0", "region:us" ]

null

2022-11-29T15:40:46Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog --- # lava-policy-multiwoz This is the best performing LAVA_kl model from the [LAVA paper](https://aclanthology.org/2020.coling-main.41/) which can be used as a word-level policy module in ConvLab3 pipeline. Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure The model was trained on MultiWOZ 2.0 data using the [LAVA codebase](https://gitlab.cs.uni-duesseldorf.de/general/dsml/lava-public). The model started with VAE pre-training and fine-tuning with informative prior KL loss, followed by corpus-based RL with REINFORCE. ### Training hyperparameters The following hyperparameters were used during SL training: - y_size: 10 - k_size: 20 - beta: 0.1 - simple_posterior: true - contextual_posterior: false - learning_rate: 1e-03 - max_vocab_size: 1000 - max_utt_len: 50 - max_dec_len: 30 - backward_size: 2 - train_batch_size: 128 - seed: 58 - optimizer: Adam - num_epoch: 100 with early stopping based on validation set The following hyperparameters were used during RL training: - tune_pi_only: false - max_words: 100 - temperature: 1.0 - episode_repeat: 1.0 - rl_lr: 0.01 - momentum: 0.0 - nesterov: false - gamma: 0.99 - rl_clip: 5.0 - random_seed: 38

AlekseyKorshuk/6.7b-ri-reproduce-combined-4-gpu-20-val

AlekseyKorshuk

2022-12-01T09:45:32Z

4

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-30T10:26:05Z

--- license: other tags: - generated_from_trainer metrics: - accuracy model-index: - name: 6.7b-ri-reproduce-combined-4-gpu-20-val results: [] ---  # 6.7b-ri-reproduce-combined-4-gpu-20-val This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9434 - Accuracy: 0.0329 - Perplexity: 51.5916 ## 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: 9e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 100 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| | 2.5731 | 1.0 | 79 | 2.6113 | 0.0317 | 13.6171 | | 2.206 | 2.0 | 158 | 2.4805 | 0.0328 | 11.9469 | | 1.9105 | 3.0 | 237 | 2.4512 | 0.0333 | 11.6019 | | 1.6301 | 4.0 | 316 | 2.5078 | 0.0345 | 12.2780 | | 1.3733 | 5.0 | 395 | 2.6816 | 0.0342 | 14.6090 | | 1.1337 | 6.0 | 474 | 3.0078 | 0.0330 | 20.2431 | | 0.9619 | 7.0 | 553 | 3.1777 | 0.0330 | 23.9923 | | 0.798 | 8.0 | 632 | 3.2559 | 0.0330 | 25.9419 | | 0.6653 | 9.0 | 711 | 3.4277 | 0.0331 | 30.8068 | | 0.552 | 10.0 | 790 | 3.5566 | 0.0333 | 35.0453 | | 0.4568 | 11.0 | 869 | 3.7324 | 0.0324 | 41.7802 | | 0.3756 | 12.0 | 948 | 3.8184 | 0.0328 | 45.5295 | | 0.3119 | 13.0 | 1027 | 3.8477 | 0.0331 | 46.8831 | | 0.2448 | 14.0 | 1106 | 3.9062 | 0.0329 | 49.7122 | | 0.1986 | 15.0 | 1185 | 3.9434 | 0.0329 | 51.5916 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3

gary109

2022-12-01T09:36:58Z

85

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "dataset:ai_light_dance", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-30T11:17:30Z

--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer datasets: - ai_light_dance model-index: - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3 results: [] ---  # ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_3 This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2) on the GARY109/AI_LIGHT_DANCE - ONSET-DRUMS_FOLD_3 dataset. It achieves the following results on the evaluation set: - Loss: 0.4093 - Wer: 0.1250 ## 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: 4 - eval_batch_size: 4 - 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: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4557 | 1.0 | 70 | 0.5794 | 0.1197 | | 0.6796 | 2.0 | 140 | 0.5726 | 0.1388 | | 0.4511 | 3.0 | 210 | 0.6290 | 0.1242 | | 0.609 | 4.0 | 280 | 0.7112 | 0.1187 | | 0.4082 | 5.0 | 350 | 0.8275 | 0.1965 | | 0.4638 | 6.0 | 420 | 0.4767 | 0.1524 | | 0.4446 | 7.0 | 490 | 0.5091 | 0.1376 | | 0.4337 | 8.0 | 560 | 0.6622 | 0.1170 | | 0.4604 | 9.0 | 630 | 0.7242 | 0.1600 | | 0.4462 | 10.0 | 700 | 0.7298 | 0.1383 | | 0.4201 | 11.0 | 770 | 0.8058 | 0.1362 | | 0.4204 | 12.0 | 840 | 0.6255 | 0.1099 | | 0.461 | 13.0 | 910 | 0.5204 | 0.1109 | | 0.3779 | 14.0 | 980 | 0.6911 | 0.1125 | | 0.3403 | 15.0 | 1050 | 0.5863 | 0.1188 | | 0.6223 | 16.0 | 1120 | 0.6367 | 0.1147 | | 0.3827 | 17.0 | 1190 | 0.6266 | 0.1293 | | 0.3055 | 18.0 | 1260 | 0.4866 | 0.1095 | | 0.3917 | 19.0 | 1330 | 0.4093 | 0.1250 | | 0.3912 | 20.0 | 1400 | 0.4514 | 0.1077 | | 0.3861 | 21.0 | 1470 | 0.5043 | 0.1156 | | 0.3659 | 22.0 | 1540 | 0.5680 | 0.1091 | | 0.3536 | 23.0 | 1610 | 0.7940 | 0.1029 | | 0.3559 | 24.0 | 1680 | 0.5877 | 0.1101 | | 0.3274 | 25.0 | 1750 | 0.4461 | 0.1059 | | 0.5232 | 26.0 | 1820 | 1.2051 | 0.1068 | | 0.3241 | 27.0 | 1890 | 0.8716 | 0.1099 | | 0.3169 | 28.0 | 1960 | 0.6752 | 0.1082 | | 0.2938 | 29.0 | 2030 | 0.6023 | 0.1071 | | 0.3022 | 30.0 | 2100 | 0.6122 | 0.1146 | | 0.4245 | 31.0 | 2170 | 0.5735 | 0.1102 | | 0.3095 | 32.0 | 2240 | 0.4476 | 0.1042 | | 0.4062 | 33.0 | 2310 | 0.6339 | 0.1130 | | 0.3202 | 34.0 | 2380 | 0.4101 | 0.1077 | | 0.2952 | 35.0 | 2450 | 0.4825 | 0.1076 | | 0.2945 | 36.0 | 2520 | 0.4998 | 0.1058 | | 0.336 | 37.0 | 2590 | 0.5490 | 0.1061 | | 0.2912 | 38.0 | 2660 | 0.4804 | 0.1038 | | 0.282 | 39.0 | 2730 | 0.4776 | 0.1022 | | 0.4359 | 40.0 | 2800 | 0.4376 | 0.1044 | | 0.2698 | 41.0 | 2870 | 0.5609 | 0.1098 | | 0.3004 | 42.0 | 2940 | 0.5258 | 0.1083 | | 0.2873 | 43.0 | 3010 | 0.4810 | 0.1069 | | 0.3413 | 44.0 | 3080 | 0.4961 | 0.1080 | | 0.2802 | 45.0 | 3150 | 0.6850 | 0.1076 | | 0.2584 | 46.0 | 3220 | 0.7210 | 0.1082 | | 0.3282 | 47.0 | 3290 | 0.6179 | 0.1053 | | 0.2666 | 48.0 | 3360 | 0.7673 | 0.1075 | | 0.2989 | 49.0 | 3430 | 0.7710 | 0.1079 | | 0.2676 | 50.0 | 3500 | 0.7655 | 0.1076 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

MGanesh29/distilbert-base-uncased-finetuned-cola-v3

MGanesh29

2022-12-01T09:17:29Z

103

0

transformers

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

text-classification

2022-12-01T09:00:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola-v3 results: [] ---  # distilbert-base-uncased-finetuned-cola-v3 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: 0.9655 - Matthews Correlation: 0.7369 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 8 | 1.9112 | 0.1486 | | No log | 2.0 | 16 | 1.8626 | 0.1273 | | No log | 3.0 | 24 | 1.7793 | 0.1947 | | No log | 4.0 | 32 | 1.6722 | 0.1681 | | No log | 5.0 | 40 | 1.5578 | 0.3876 | | No log | 6.0 | 48 | 1.4463 | 0.5551 | | No log | 7.0 | 56 | 1.3280 | 0.5498 | | No log | 8.0 | 64 | 1.2302 | 0.5936 | | No log | 9.0 | 72 | 1.1408 | 0.6998 | | No log | 10.0 | 80 | 1.0765 | 0.6601 | | No log | 11.0 | 88 | 1.0145 | 0.6988 | | No log | 12.0 | 96 | 0.9655 | 0.7369 | | No log | 13.0 | 104 | 0.9389 | 0.6992 | | No log | 14.0 | 112 | 0.9258 | 0.6992 | | No log | 15.0 | 120 | 0.9209 | 0.6992 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289

hr-elrond

2022-12-01T08:59:48Z

100

2

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "unk", "dataset:hr-elrond/autotrain-data-consumer-nature-speech_finbert", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-11-18T15:00:49Z

--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - hr-elrond/autotrain-data-consumer-nature-speech_finbert co2_eq_emissions: emissions: 0.004371975254312265 --- # Model Trained Using AutoTrain We trained FinBERT to identify whether firms´ talk contains consumer concepts of human nature (e.g., "I believe consumers generally act rational.", "Consumers must take over responsibility for the choices they make.", "It seems consumers behave quite altruistic.") from statements that do not (e.g., "We expect buyers to double their purchases next year.", "We see a 5% growth in numbers compared to the previous year."). The training data consisted of 236 positive documents (containing concepts of consumer nature) and 1034 negative documents (not contain concepts of consumer nature) extracted from earnings call transcripts of S&P-500 companies (2015-2020). # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2147169289 - CO2 Emissions (in grams): 0.0044 ## Validation Metrics - Loss: 0.256 - Accuracy: 0.913 - Precision: 0.736 - Recall: 0.830 - AUC: 0.956 - F1: 0.780 ## 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/hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("hr-elrond/autotrain-consumer-nature-speech_finbert-2147169289", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

htermotto/distilbert-base-uncased-finetuned-sngp-squad-seed-999

htermotto

2022-12-01T08:30:08Z

31

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-12-01T05:08:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-sngp-squad-seed-999 results: [] ---  # distilbert-base-uncased-finetuned-sngp-squad-seed-999 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9622 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 2.4527 | 1.0 | 8248 | 2.0711 | | 2.1703 | 2.0 | 16496 | 1.9622 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

ravinduj/finetuning-sentiment-model-3000-samples

ravinduj

2022-12-01T08:21:50Z

105

0

transformers

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

text-classification

2022-11-30T10:38:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8533333333333334 - name: F1 type: f1 value: 0.8543046357615894 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3489 - Accuracy: 0.8533 - F1: 0.8543 ## 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 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-kitchen_and_dining-3-16-5

fathyshalab

2022-12-01T08:14:12Z

105

0

transformers

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

text-classification

2022-11-30T18:19:00Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-3-16-5 results: [] ---  # all-roberta-large-v1-kitchen_and_dining-3-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

pig4431/YELP_ALBERT_5E

pig4431

2022-12-01T08:07:20Z

107

0

transformers

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

text-classification

2022-12-01T07:33:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_ALBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9733333333333334 ---  # YELP_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - Accuracy: 0.9733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4967 | 0.03 | 50 | 0.1667 | 0.9467 | | 0.3268 | 0.06 | 100 | 0.2106 | 0.9133 | | 0.3413 | 0.1 | 150 | 0.2107 | 0.9667 | | 0.3172 | 0.13 | 200 | 0.1906 | 0.94 | | 0.2804 | 0.16 | 250 | 0.2588 | 0.9 | | 0.2604 | 0.19 | 300 | 0.2023 | 0.94 | | 0.2532 | 0.22 | 350 | 0.1263 | 0.9533 | | 0.2103 | 0.26 | 400 | 0.1233 | 0.96 | | 0.212 | 0.29 | 450 | 0.2019 | 0.9267 | | 0.2669 | 0.32 | 500 | 0.1110 | 0.9667 | | 0.2187 | 0.35 | 550 | 0.1542 | 0.96 | | 0.2203 | 0.38 | 600 | 0.0879 | 0.9733 | | 0.2699 | 0.42 | 650 | 0.0971 | 0.9667 | | 0.2107 | 0.45 | 700 | 0.0863 | 0.9667 | | 0.2443 | 0.48 | 750 | 0.0823 | 0.9733 | | 0.1987 | 0.51 | 800 | 0.1207 | 0.9733 | | 0.2326 | 0.54 | 850 | 0.1368 | 0.9667 | | 0.1787 | 0.58 | 900 | 0.1027 | 0.9667 | | 0.2159 | 0.61 | 950 | 0.2443 | 0.9333 | | 0.1316 | 0.64 | 1000 | 0.2035 | 0.9467 | | 0.2416 | 0.67 | 1050 | 0.0882 | 0.9733 | | 0.2008 | 0.7 | 1100 | 0.1709 | 0.9533 | | 0.2065 | 0.74 | 1150 | 0.1098 | 0.9667 | | 0.2391 | 0.77 | 1200 | 0.1055 | 0.9667 | | 0.1533 | 0.8 | 1250 | 0.1997 | 0.94 | | 0.2016 | 0.83 | 1300 | 0.0899 | 0.96 | | 0.2016 | 0.86 | 1350 | 0.0957 | 0.9733 | | 0.2316 | 0.9 | 1400 | 0.0784 | 0.98 | | 0.1839 | 0.93 | 1450 | 0.0784 | 0.9733 | | 0.2121 | 0.96 | 1500 | 0.1150 | 0.9733 | | 0.1307 | 0.99 | 1550 | 0.0969 | 0.9733 | | 0.1271 | 1.02 | 1600 | 0.2326 | 0.9467 | | 0.1736 | 1.06 | 1650 | 0.0979 | 0.9667 | | 0.1357 | 1.09 | 1700 | 0.0862 | 0.98 | | 0.1871 | 1.12 | 1750 | 0.1419 | 0.9667 | | 0.1411 | 1.15 | 1800 | 0.1301 | 0.96 | | 0.1317 | 1.18 | 1850 | 0.1602 | 0.9533 | | 0.1432 | 1.22 | 1900 | 0.1885 | 0.9533 | | 0.1793 | 1.25 | 1950 | 0.0776 | 0.9667 | | 0.1322 | 1.28 | 2000 | 0.0822 | 0.9733 | | 0.1416 | 1.31 | 2050 | 0.0920 | 0.9733 | | 0.1524 | 1.34 | 2100 | 0.0673 | 0.98 | | 0.1338 | 1.38 | 2150 | 0.0602 | 0.98 | | 0.152 | 1.41 | 2200 | 0.0916 | 0.98 | | 0.1192 | 1.44 | 2250 | 0.0559 | 0.98 | | 0.1471 | 1.47 | 2300 | 0.1096 | 0.9667 | | 0.1267 | 1.5 | 2350 | 0.0695 | 0.9733 | | 0.1776 | 1.54 | 2400 | 0.1363 | 0.96 | | 0.1495 | 1.57 | 2450 | 0.0818 | 0.98 | | 0.1158 | 1.6 | 2500 | 0.1282 | 0.9667 | | 0.1772 | 1.63 | 2550 | 0.0682 | 0.9733 | | 0.1187 | 1.66 | 2600 | 0.1032 | 0.9733 | | 0.136 | 1.7 | 2650 | 0.1071 | 0.9667 | | 0.1829 | 1.73 | 2700 | 0.0753 | 0.9667 | | 0.1147 | 1.76 | 2750 | 0.1071 | 0.9733 | | 0.1174 | 1.79 | 2800 | 0.1441 | 0.9667 | | 0.0707 | 1.82 | 2850 | 0.1362 | 0.9667 | | 0.1372 | 1.86 | 2900 | 0.1861 | 0.9533 | | 0.2108 | 1.89 | 2950 | 0.0770 | 0.9733 | | 0.2014 | 1.92 | 3000 | 0.1114 | 0.9667 | | 0.1373 | 1.95 | 3050 | 0.1244 | 0.9667 | | 0.1242 | 1.98 | 3100 | 0.1220 | 0.96 | | 0.1267 | 2.02 | 3150 | 0.1139 | 0.9733 | | 0.1021 | 2.05 | 3200 | 0.2013 | 0.9533 | | 0.1091 | 2.08 | 3250 | 0.1027 | 0.9733 | | 0.0648 | 2.11 | 3300 | 0.1464 | 0.9733 | | 0.1207 | 2.14 | 3350 | 0.1255 | 0.9733 | | 0.0833 | 2.18 | 3400 | 0.0708 | 0.98 | | 0.0796 | 2.21 | 3450 | 0.1608 | 0.96 | | 0.0624 | 2.24 | 3500 | 0.0827 | 0.98 | | 0.0518 | 2.27 | 3550 | 0.0602 | 0.98 | | 0.1242 | 2.3 | 3600 | 0.0752 | 0.9733 | | 0.0422 | 2.34 | 3650 | 0.1000 | 0.9733 | | 0.0748 | 2.37 | 3700 | 0.1171 | 0.9667 | | 0.0839 | 2.4 | 3750 | 0.1341 | 0.9667 | | 0.1033 | 2.43 | 3800 | 0.0744 | 0.98 | | 0.0567 | 2.46 | 3850 | 0.0869 | 0.98 | | 0.0756 | 2.5 | 3900 | 0.0745 | 0.98 | | 0.0768 | 2.53 | 3950 | 0.0895 | 0.9733 | | 0.0878 | 2.56 | 4000 | 0.0703 | 0.98 | | 0.1023 | 2.59 | 4050 | 0.0806 | 0.98 | | 0.0807 | 2.62 | 4100 | 0.0338 | 0.9867 | | 0.0868 | 2.66 | 4150 | 0.0892 | 0.9667 | | 0.0648 | 2.69 | 4200 | 0.1637 | 0.9533 | | 0.0535 | 2.72 | 4250 | 0.1622 | 0.9667 | | 0.0675 | 2.75 | 4300 | 0.1354 | 0.9733 | | 0.1121 | 2.78 | 4350 | 0.1440 | 0.9533 | | 0.0714 | 2.82 | 4400 | 0.1022 | 0.9467 | | 0.0786 | 2.85 | 4450 | 0.1110 | 0.9733 | | 0.0822 | 2.88 | 4500 | 0.1218 | 0.9733 | | 0.1075 | 2.91 | 4550 | 0.1041 | 0.9733 | | 0.0783 | 2.94 | 4600 | 0.0992 | 0.9733 | | 0.1059 | 2.98 | 4650 | 0.1187 | 0.9733 | | 0.067 | 3.01 | 4700 | 0.0931 | 0.9733 | | 0.0425 | 3.04 | 4750 | 0.1252 | 0.9733 | | 0.0539 | 3.07 | 4800 | 0.1152 | 0.9733 | | 0.0419 | 3.1 | 4850 | 0.1534 | 0.9667 | | 0.0462 | 3.13 | 4900 | 0.1398 | 0.9733 | | 0.0435 | 3.17 | 4950 | 0.1168 | 0.98 | | 0.0144 | 3.2 | 5000 | 0.1489 | 0.9667 | | 0.0367 | 3.23 | 5050 | 0.1293 | 0.9733 | | 0.0336 | 3.26 | 5100 | 0.1353 | 0.9733 | | 0.0246 | 3.29 | 5150 | 0.0958 | 0.98 | | 0.0181 | 3.33 | 5200 | 0.1294 | 0.9733 | | 0.0357 | 3.36 | 5250 | 0.1209 | 0.9733 | | 0.0683 | 3.39 | 5300 | 0.1748 | 0.96 | | 0.0353 | 3.42 | 5350 | 0.2159 | 0.9533 | | 0.0415 | 3.45 | 5400 | 0.1723 | 0.96 | | 0.0336 | 3.49 | 5450 | 0.1031 | 0.98 | | 0.0475 | 3.52 | 5500 | 0.0959 | 0.98 | | 0.0393 | 3.55 | 5550 | 0.2163 | 0.96 | | 0.0337 | 3.58 | 5600 | 0.1097 | 0.9733 | | 0.0415 | 3.61 | 5650 | 0.1365 | 0.98 | | 0.035 | 3.65 | 5700 | 0.1175 | 0.98 | | 0.0448 | 3.68 | 5750 | 0.1543 | 0.9667 | | 0.0445 | 3.71 | 5800 | 0.2005 | 0.96 | | 0.0211 | 3.74 | 5850 | 0.1179 | 0.98 | | 0.0198 | 3.77 | 5900 | 0.1298 | 0.9733 | | 0.026 | 3.81 | 5950 | 0.2167 | 0.9667 | | 0.0412 | 3.84 | 6000 | 0.1224 | 0.98 | | 0.0446 | 3.87 | 6050 | 0.0798 | 0.98 | | 0.0174 | 3.9 | 6100 | 0.0577 | 0.9933 | | 0.0535 | 3.93 | 6150 | 0.1482 | 0.9667 | | 0.0495 | 3.97 | 6200 | 0.0862 | 0.98 | | 0.0267 | 4.0 | 6250 | 0.1190 | 0.98 | | 0.0087 | 4.03 | 6300 | 0.0747 | 0.98 | | 0.0102 | 4.06 | 6350 | 0.0753 | 0.9867 | | 0.0178 | 4.09 | 6400 | 0.1812 | 0.9667 | | 0.0088 | 4.13 | 6450 | 0.0817 | 0.98 | | 0.0144 | 4.16 | 6500 | 0.0805 | 0.98 | | 0.014 | 4.19 | 6550 | 0.0862 | 0.9867 | | 0.0002 | 4.22 | 6600 | 0.0894 | 0.98 | | 0.0112 | 4.25 | 6650 | 0.1004 | 0.9733 | | 0.0054 | 4.29 | 6700 | 0.0832 | 0.9867 | | 0.0001 | 4.32 | 6750 | 0.0812 | 0.9867 | | 0.0202 | 4.35 | 6800 | 0.1828 | 0.9667 | | 0.009 | 4.38 | 6850 | 0.1114 | 0.98 | | 0.0001 | 4.41 | 6900 | 0.1295 | 0.98 | | 0.0077 | 4.45 | 6950 | 0.1610 | 0.9733 | | 0.0082 | 4.48 | 7000 | 0.1787 | 0.9667 | | 0.0198 | 4.51 | 7050 | 0.1485 | 0.9733 | | 0.0017 | 4.54 | 7100 | 0.1774 | 0.9733 | | 0.0115 | 4.57 | 7150 | 0.1567 | 0.9733 | | 0.0001 | 4.61 | 7200 | 0.1534 | 0.9733 | | 0.0247 | 4.64 | 7250 | 0.2020 | 0.9667 | | 0.0059 | 4.67 | 7300 | 0.1918 | 0.9667 | | 0.0052 | 4.7 | 7350 | 0.1315 | 0.98 | | 0.0076 | 4.73 | 7400 | 0.1289 | 0.98 | | 0.0218 | 4.77 | 7450 | 0.1610 | 0.9733 | | 0.0077 | 4.8 | 7500 | 0.1355 | 0.98 | | 0.0096 | 4.83 | 7550 | 0.1378 | 0.9733 | | 0.008 | 4.86 | 7600 | 0.1568 | 0.9733 | | 0.0103 | 4.89 | 7650 | 0.1388 | 0.9733 | | 0.0009 | 4.93 | 7700 | 0.1221 | 0.98 | | 0.0287 | 4.96 | 7750 | 0.1448 | 0.9733 | | 0.01 | 4.99 | 7800 | 0.1394 | 0.9733 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

srnsrn120/whisper-small-hi

srnsrn120

2022-12-01T07:24:42Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-01T05:57:41Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - srnsrn120 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 40.772877338525355 ---  # Whisper Small Hi - srnsrn120 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Wer: 40.7729 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2442 | 0.98 | 400 | 0.3428 | 40.7729 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

minhhoque/segformer-b0-scene-parse-150

minhhoque

2022-12-01T06:31:02Z

45

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "license:other", "endpoints_compatible", "region:us" ]

null

2022-12-01T05:42:03Z

--- license: other tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 results: [] ---  # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Evelyn18/roberta-base-spanish-squades-becasv3

Evelyn18

2022-12-01T06:27:03Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:becasv2", "endpoints_compatible", "region:us" ]

question-answering

2022-07-19T13:20:41Z

--- tags: - generated_from_trainer datasets: - becasv2 model-index: - name: roberta-base-spanish-squades-becasv3 results: [] ---  # roberta-base-spanish-squades-modelo-robertav3 This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 1.6939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 11 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 1.7032 | | No log | 2.0 | 10 | 1.6939 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

fanpu/model_output_original_subreddit-cmu_1

fanpu

2022-12-01T05:40:32Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-01T05:04:04Z

--- license: mit tags: - generated_from_trainer model-index: - name: model_output_original_subreddit-cmu_1 results: [] ---  # model_output_original_subreddit-cmu_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

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

dicquiloan

2022-12-01T05:11:21Z

0

null

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

reinforcement-learning

2022-11-25T23:37:21Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="dicquiloan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

minhhoque/distilbert-base-uncased_imdb_reviews

minhhoque

2022-12-01T04:56:58Z

118

1

transformers

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

text-classification

2022-11-28T02:21:56Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased_imdb_reviews results: [] ---  # distilbert-base-uncased_imdb_reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.385 | 0.4 | 500 | 0.3796 | | 0.2803 | 0.8 | 1000 | 0.2549 | | 0.208 | 1.2 | 1500 | 0.3218 | | 0.1655 | 1.6 | 2000 | 0.2577 | | 0.153 | 2.0 | 2500 | 0.2718 | | 0.0552 | 2.4 | 3000 | 0.3514 | | 0.0667 | 2.8 | 3500 | 0.3427 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-kitchen_and_dining-1-16-5

fathyshalab

2022-12-01T04:45:46Z

105

0

transformers

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

text-classification

2022-11-30T18:15:17Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-kitchen_and_dining-1-16-5 results: [] ---  # all-roberta-large-v1-kitchen_and_dining-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/all-roberta-large-v1-credit_cards-8-16-5

fathyshalab

2022-12-01T03:58:24Z

103

0

transformers

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

text-classification

2022-11-30T18:12:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-8-16-5 results: [] ---  # all-roberta-large-v1-credit_cards-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.75 | 1.0 | 1 | 2.5769 | 0.2389 | | 2.178 | 2.0 | 2 | 2.4879 | 0.2389 | | 1.769 | 3.0 | 3 | 2.4180 | 0.2566 | | 1.4703 | 4.0 | 4 | 2.3657 | 0.3097 | | 1.2711 | 5.0 | 5 | 2.3376 | 0.3186 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

DLL888/roberta-base-squad

DLL888

2022-12-01T03:55:20Z

63

0

transformers

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

question-answering

2022-12-01T03:24:46Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: DLL888/roberta-base-squad results: [] ---  # DLL888/roberta-base-squad 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: 0.7054 - Train End Logits Accuracy: 0.8022 - Train Start Logits Accuracy: 0.7586 - Validation Loss: 0.8224 - Validation End Logits Accuracy: 0.7692 - Validation Start Logits Accuracy: 0.7402 - 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', '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': 10570, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 500, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: mixed_float16 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.1613 | 0.7038 | 0.6632 | 0.8676 | 0.7626 | 0.7342 | 0 | | 0.7054 | 0.8022 | 0.7586 | 0.8224 | 0.7692 | 0.7402 | 1 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

Roman1998/tesorflowTest

Roman1998

2022-12-01T03:48:43Z

62

0

transformers

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

text-classification

2022-12-01T03:47:33Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tesorflowTest results: [] ---  # tesorflowTest 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: - Train Loss: 0.1220 - 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', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.2863 | 0 | | 0.1671 | 1 | | 0.1220 | 2 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01

Zengwei

2022-12-01T03:29:09Z

0

null

[ "tensorboard", "region:us" ]

null

2022-12-01T02:01:38Z

This repo contains pre-trained models, checkpoints, training logs and decoding results of the following pull-request: https://github.com/k2-fsa/icefall/pull/683

huggingtweets/prezoh

huggingtweets

2022-12-01T03:28:19Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/prezoh/1669865295720/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590487732387733505/JiMBIJrZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">prezoh</div> <div style="text-align: center; font-size: 14px;">@prezoh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from prezoh. | Data | prezoh | | --- | --- | | Tweets downloaded | 3158 | | Retweets | 30 | | Short tweets | 905 | | Tweets kept | 2223 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/278h7rp5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @prezoh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3e7ukxmi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3e7ukxmi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/prezoh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Rastadayon/wav2vec2-large-xls-r-300m-dutch-colab

Rastadayon

2022-12-01T03:20:45Z

161

0

transformers

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

automatic-speech-recognition

2022-11-30T20:59:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-dutch-colab results: [] ---  # wav2vec2-large-xls-r-300m-dutch-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5834 - eval_wer: 0.3471 - eval_cer: 0.1181 - eval_runtime: 338.6313 - eval_samples_per_second: 14.582 - eval_steps_per_second: 1.825 - epoch: 14.87 - step: 4000 ## 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: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

itisphilippe/StackOverflowNER

itisphilippe

2022-12-01T02:53:38Z

0

1

null

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

null

2022-11-30T07:01:36Z

--- license: mit --- Models and other data for https://github.com/jeniyat/StackOverflowNER. Use `git lfs fetch --all` to download all files. Please note that folders are stored decompressed due to HuggingFace file size limitations. The individual files in ./data_ctc/ are compressed using `gzip`, and can be decompressed using `gunzip -d *.gz`. Intermediate model checkpoints have not been uploaded due to bandwidth limitations. **BibTeX entry and citation info** ```bibtex @inproceedings{Tabassum20acl, title = {Code and Named Entity Recognition in StackOverflow}, author = "Tabassum, Jeniya and Maddela, Mounica and Xu, Wei and Ritter, Alan", booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2020} } ```

fathyshalab/all-roberta-large-v1-credit_cards-5-16-5

fathyshalab

2022-12-01T02:47:27Z

106

0

transformers

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

text-classification

2022-11-30T18:07:32Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-5-16-5 results: [] ---  # all-roberta-large-v1-credit_cards-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.75 | 1.0 | 1 | 2.5769 | 0.2389 | | 2.178 | 2.0 | 2 | 2.4879 | 0.2389 | | 1.769 | 3.0 | 3 | 2.4180 | 0.2566 | | 1.4703 | 4.0 | 4 | 2.3657 | 0.3097 | | 1.2711 | 5.0 | 5 | 2.3376 | 0.3186 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

Yanjie24/t5-samsung

Yanjie24

2022-12-01T02:31:14Z

107

0

transformers

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

text2text-generation

2022-12-01T02:09:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-samsung results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 42.2345 ---  # t5-samsung This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.8153 - Rouge1: 42.2345 - Rouge2: 18.983 - Rougel: 33.0073 - Rougelsum: 38.8755 - Gen Len: 36.4242 ## 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: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.0028 | 1.0 | 1841 | 1.8153 | 42.2345 | 18.983 | 33.0073 | 38.8755 | 36.4242 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

SathEdu/distilbert-base-uncased-finetuned-emotion

SathEdu

2022-12-01T02:15:54Z

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

2022-11-19T07:30:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9256889016417648 ---  # 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: 0.2222 - Accuracy: 0.9255 - F1: 0.9257 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7962 | 1.0 | 250 | 0.3167 | 0.903 | 0.8984 | | 0.2475 | 2.0 | 500 | 0.2222 | 0.9255 | 0.9257 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual

cardiffnlp

2022-12-01T02:11:30Z

114

4

transformers

[ "transformers", "pytorch", "bert", "text-classification", "dataset:cardiffnlp/tweet_sentiment_multilingual", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-01T02:07:08Z

--- datasets: - cardiffnlp/tweet_sentiment_multilingual metrics: - f1 - accuracy model-index: - name: cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual results: - task: type: text-classification name: Text Classification dataset: name: cardiffnlp/tweet_sentiment_multilingual type: all split: test metrics: - name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6169540229885058 - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6168385894019698 - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all) type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6169540229885058 pipeline_tag: text-classification widget: - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}} example_title: "topic_classification 1" - text: Yes, including Medicare and social security saving👍 example_title: "sentiment 1" - text: All two of them taste like ass. example_title: "offensive 1" - text: If you wanna look like a badass, have drama on social media example_title: "irony 1" - text: Whoever just unfollowed me you a bitch example_title: "hate 1" - text: I love swimming for the same reason I love meditating...the feeling of weightlessness. example_title: "emotion 1" - text: Beautiful sunset last night from the pontoon @TupperLakeNY example_title: "emoji 1" --- # cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [`cardiffnlp/tweet_sentiment_multilingual (all)`](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp). Training split is `train` and parameters have been tuned on the validation split `validation`. Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual/raw/main/metric.json)). - F1 (micro): 0.6169540229885058 - F1 (macro): 0.6168385894019698 - Accuracy: 0.6169540229885058 ### Usage Install tweetnlp via pip. ```shell pip install tweetnlp ``` Load the model in python. ```python import tweetnlp model = tweetnlp.Classifier("cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual", max_length=128) model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}') ``` ### Reference ``` @inproceedings{dimosthenis-etal-2022-twitter, title = "{T}witter {T}opic {C}lassification", author = "Antypas, Dimosthenis and Ushio, Asahi and Camacho-Collados, Jose and Neves, Leonardo and Silva, Vitor and Barbieri, Francesco", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics" } ```

fathyshalab/all-roberta-large-v1-credit_cards-3-16-5

fathyshalab

2022-12-01T01:59:23Z

106

0

transformers

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

text-classification

2022-11-30T18:04:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-credit_cards-3-16-5 results: [] ---  # all-roberta-large-v1-credit_cards-3-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.75 | 1.0 | 1 | 2.5769 | 0.2389 | | 2.178 | 2.0 | 2 | 2.4879 | 0.2389 | | 1.769 | 3.0 | 3 | 2.4180 | 0.2566 | | 1.4703 | 4.0 | 4 | 2.3657 | 0.3097 | | 1.2711 | 5.0 | 5 | 2.3376 | 0.3186 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

DiogoSabec/BOT

DiogoSabec

2022-12-01T01:33:17Z

119

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-01T00:40:43Z

--- tags: - conversational ---

sd-dreambooth-library/crisimsestelle

sd-dreambooth-library

2022-12-01T01:20:13Z

52

0

diffusers

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

text-to-image

2022-11-29T16:50:18Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### Contain Real Ingredients on Stable Diffusion 2 via Dreambooth #### model by estelleflores ![image 20](https://i.ibb.co/6b0ZBtr/1.png) This is a Stable Diffusion 2 model fine-tuned to the CRIsimsEstelle concept taught to Stable Diffusion with Dreambooth. ![image 21](https://i.ibb.co/7tL6Vs4/59.png) It can be used by modifying the `instance_prompt`: **3d render in \<cri-sims> style** or just using the initializer \'\<cri-sims> style' somewhere in your prompt will work. ![image 22](https://i.ibb.co/H21FX8Q/37.png) Images used for training this concept come from the [project Contain Real Ingredients](https://teia.art/estelle), an art practice inside the game The Sims 4 by artist Estelle Flores: ![image 0](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/13.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/7.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/11.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/10.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/2.jpeg) ![image 6](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/8.jpeg) ![image 7](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/3.jpeg) ![image 8](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/12.jpeg) ![image 9](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/4.jpeg) ![image 10](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/5.jpeg) ![image 11](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/1.jpeg) ![image 12](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/14.jpeg) ![image 13](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/9.jpeg) ![image 14](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/17.jpeg) ![image 15](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/16.jpeg) ![image 16](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/18.jpeg) ![image 17](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/6.jpeg) ![image 18](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/15.jpeg) ![image 19](https://huggingface.co/sd-dreambooth-library/crisimsestelle/resolve/main/concept_images/19.jpeg) You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)

wmFrank/sample-factory-2-megaverse

wmFrank

2022-12-01T00:50:17Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-01T00:49:58Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TowerBuilding type: TowerBuilding metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **TowerBuilding** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

fathyshalab/all-roberta-large-v1-banking-9-16-5

fathyshalab

2022-12-01T00:47:58Z

108

0

transformers

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

text-classification

2022-11-28T18:53:06Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-9-16-5 results: [] ---  # all-roberta-large-v1-banking-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7211 | 1.0 | 1 | 2.5748 | 0.2301 | | 2.2722 | 2.0 | 2 | 2.4566 | 0.3009 | | 1.9185 | 3.0 | 3 | 2.3596 | 0.3805 | | 1.667 | 4.0 | 4 | 2.2920 | 0.3982 | | 1.4704 | 5.0 | 5 | 2.2565 | 0.3982 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/all-roberta-large-v1-banking-8-16-5

fathyshalab

2022-12-01T00:21:13Z

109

0

transformers

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

text-classification

2022-11-28T18:30:16Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-8-16-5 results: [] ---  # all-roberta-large-v1-banking-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7211 | 1.0 | 1 | 2.5748 | 0.2301 | | 2.2722 | 2.0 | 2 | 2.4566 | 0.3009 | | 1.9185 | 3.0 | 3 | 2.3596 | 0.3805 | | 1.667 | 4.0 | 4 | 2.2920 | 0.3982 | | 1.4704 | 5.0 | 5 | 2.2565 | 0.3982 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

Taqwa/whisper-small-hi

Taqwa

2022-12-01T00:05:15Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-26T20:53:48Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 35.74028612545501 ---  # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [Taqwa/whisper-small-hiTaqwa](https://huggingface.co/Taqwa/whisper-small-hiTaqwa) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3353 - Wer: 35.7403 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0762 | 0.31 | 125 | 0.2818 | 33.3573 | | 0.0653 | 0.61 | 250 | 0.2930 | 33.9584 | | 0.062 | 0.92 | 375 | 0.3060 | 34.7456 | | 0.0518 | 1.22 | 500 | 0.3353 | 35.7403 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-banking-6-16-5

fathyshalab

2022-11-30T23:26:46Z

3

0

transformers

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

text-classification

2022-11-28T17:44:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-6-16-5 results: [] ---  # all-roberta-large-v1-banking-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7211 | 1.0 | 1 | 2.5748 | 0.2301 | | 2.2722 | 2.0 | 2 | 2.4566 | 0.3009 | | 1.9185 | 3.0 | 3 | 2.3596 | 0.3805 | | 1.667 | 4.0 | 4 | 2.2920 | 0.3982 | | 1.4704 | 5.0 | 5 | 2.2565 | 0.3982 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

CarperAI/randomwalks

CarperAI

2022-11-30T22:22:26Z

164

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-28T17:23:14Z

--- license: mit --- This is a pretrained model used in [PPO toy example](https://github.com/CarperAI/trlx/tree/main/examples/randomwalks) from [CarperAI/trlX](https://github.com/CarperAI/trlx/tree/main/examples/randomwalks)

deblagoj/distilbert-base-uncased-finetuned-emotion

deblagoj

2022-11-30T22:05:20Z

103

0

transformers

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

text-classification

2022-04-07T18:26:07Z

--- 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: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.919 - name: F1 type: f1 value: 0.9190903538852266 ---  # 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: 0.2225 - Accuracy: 0.919 - F1: 0.9191 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.814 | 1.0 | 250 | 0.3153 | 0.904 | 0.9016 | | 0.2515 | 2.0 | 500 | 0.2225 | 0.919 | 0.9191 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1

huggingtweets/poisonjr

huggingtweets

2022-11-30T21:50:40Z

119

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-30T21:49:04Z

--- language: en thumbnail: http://www.huggingtweets.com/poisonjr/1669845035713/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1582446449228382209/8JRLlVu__400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">gale na</div> <div style="text-align: center; font-size: 14px;">@poisonjr</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from gale na. | Data | gale na | | --- | --- | | Tweets downloaded | 3204 | | Retweets | 731 | | Short tweets | 782 | | Tweets kept | 1691 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33t9oiqy/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @poisonjr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3c5vn57r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3c5vn57r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/poisonjr') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

manirai91/enlm-roberta-final

manirai91

2022-11-30T21:40:33Z

108

0

transformers

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

fill-mask

2022-11-28T03:41:11Z

--- tags: - generated_from_trainer model-index: - name: enlm-roberta-final results: [] ---  # enlm-roberta-final This model is a fine-tuned version of [manirai91/enlm-roberta](https://huggingface.co/manirai91/enlm-roberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4187 ## 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: 6e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 8192 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5245 | 0.34 | 160 | 1.4187 | | 1.5245 | 0.69 | 320 | 1.4183 | | 1.5259 | 1.03 | 480 | 1.4177 | | 1.5265 | 1.37 | 640 | 1.4185 | | 1.5245 | 1.72 | 800 | 1.4190 | | 1.5241 | 2.06 | 960 | 1.4172 | | 1.5227 | 2.4 | 1120 | 1.4165 | | 1.5226 | 2.75 | 1280 | 1.4152 | | 1.522 | 3.09 | 1440 | 1.4190 | | 1.5243 | 3.43 | 1600 | 1.4177 | | 1.5213 | 3.78 | 1760 | 1.4134 | | 1.524 | 4.12 | 1920 | 1.4140 | | 1.5223 | 4.46 | 2080 | 1.4173 | | 1.5236 | 4.81 | 2240 | 1.4121 | | 1.5239 | 5.15 | 2400 | 1.4186 | | 1.5203 | 5.49 | 2560 | 1.4154 | | 1.522 | 5.84 | 2720 | 1.4162 | | 1.5209 | 6.18 | 2880 | 1.4154 | | 1.5196 | 6.52 | 3040 | 1.4153 | | 1.5209 | 6.87 | 3200 | 1.4122 | | 1.5202 | 7.21 | 3360 | 1.4146 | | 1.5192 | 7.55 | 3520 | 1.4141 | | 1.5215 | 7.9 | 3680 | 1.4123 | | 1.5228 | 8.24 | 3840 | 1.4147 | | 1.5222 | 8.58 | 4000 | 1.4144 | | 1.5201 | 8.93 | 4160 | 1.4173 | | 1.523 | 9.27 | 4320 | 1.4171 | | 1.5212 | 9.61 | 4480 | 1.4149 | | 1.522 | 9.96 | 4640 | 1.4187 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

huggingtweets/blewglass

huggingtweets

2022-11-30T21:38:03Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-30T21:36:41Z

--- language: en thumbnail: http://www.huggingtweets.com/blewglass/1669844278462/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1589805873366724610/ifGVL-6g_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">come back clammy</div> <div style="text-align: center; font-size: 14px;">@blewglass</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from come back clammy. | Data | come back clammy | | --- | --- | | Tweets downloaded | 3174 | | Retweets | 582 | | Short tweets | 317 | | Tweets kept | 2275 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cybl684/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @blewglass's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zifv54gk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zifv54gk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/blewglass') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

danielsaggau/scotus_py

danielsaggau

2022-11-30T21:12:28Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "longformer", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-30T21:12:16Z

--- 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 512 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 970 with parameters: ``` {'batch_size': 4, '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": 970, "warmup_steps": 97, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LongformerModel (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

fathyshalab/all-roberta-large-v1-banking-1-16-5

fathyshalab

2022-11-30T21:09:17Z

5

0

transformers

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

text-classification

2022-11-28T15:45:09Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-16-5 results: [] ---  # all-roberta-large-v1-banking-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4479 - Accuracy: 0.2301 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.716 | 1.0 | 1 | 2.6641 | 0.1327 | | 2.1674 | 2.0 | 2 | 2.5852 | 0.1858 | | 1.7169 | 3.0 | 3 | 2.5202 | 0.2035 | | 1.3976 | 4.0 | 4 | 2.4729 | 0.2124 | | 1.2503 | 5.0 | 5 | 2.4479 | 0.2301 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

gavin124/gpt2-finetuned-cnn-summarization-v1

gavin124

2022-11-30T20:40:22Z

80

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "summarization", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-11-30T15:33:05Z

--- license: mit tags: - summarization - generated_from_trainer model-index: - name: gpt2-finetuned-cnn-summarization-v1 results: [] ---  # gpt2-finetuned-cnn-summarization-v1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1709 ## 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 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 2.2025 | 1.0 | 5742 | 2.1636 | | 2.0428 | 2.0 | 11484 | 2.1659 | | 1.9681 | 3.0 | 17226 | 2.1709 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pere/whisper-medium-NST-uf-linlr

pere

2022-11-30T19:24:35Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "NbAiLab/NST", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-28T07:44:59Z

--- license: apache-2.0 tags: - hf-asr-leaderboard - automatic-speech-recognition - NbAiLab/NST - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-NST-uf-linlr results: [] ---  # whisper-medium-NST-uf-linlr This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set: - Loss: 0.3007 - Wer: 9.1220 ## 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: 72 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.2046 | 0.05 | 1000 | 0.3426 | 15.2794 | | 0.148 | 0.1 | 2000 | 0.3284 | 10.8324 | | 0.121 | 0.15 | 3000 | 0.3092 | 12.8848 | | 0.1089 | 0.2 | 4000 | 0.2808 | 10.4903 | | 0.0976 | 0.25 | 5000 | 0.2617 | 9.9202 | | 0.0901 | 0.3 | 6000 | 0.2604 | 21.8928 | | 0.0834 | 0.35 | 7000 | 0.2877 | 9.3501 | | 0.0825 | 0.4 | 8000 | 0.2794 | 9.3501 | | 0.0553 | 1.05 | 9000 | 0.2845 | 9.5781 | | 0.0472 | 1.1 | 10000 | 0.2814 | 24.1733 | | 0.0409 | 1.15 | 11000 | 0.3084 | 8.0958 | | 0.041 | 1.2 | 12000 | 0.2865 | 9.2360 | | 0.0353 | 1.25 | 13000 | 0.2828 | 6.4994 | | 0.0348 | 1.3 | 14000 | 0.2708 | 7.5257 | | 0.0349 | 1.35 | 15000 | 0.2842 | 23.0331 | | 0.0361 | 1.4 | 16000 | 0.2769 | 10.1482 | | 0.0249 | 2.04 | 17000 | 0.2935 | 8.8940 | | 0.0204 | 2.09 | 18000 | 0.2874 | 12.4287 | | 0.0175 | 2.14 | 19000 | 0.2882 | 12.9989 | | 0.0197 | 2.19 | 20000 | 0.3007 | 9.1220 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

ShadoWxShinigamI/vray-render

ShadoWxShinigamI

2022-11-30T19:05:09Z

0

54

null

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

null

2022-11-30T18:55:42Z

--- license: creativeml-openrail-m --- ##Textual Inversion Embedding For SD 2.0 (768) by ShadoWxShinigamI 44 Images, 768x768, Batch Size 4, Gradient Accumulation 11, Vectors - 6, Steps 500 I love the V-Ray Render style, and wanted to try making an embed for a highly varied style. This is my attempt. It is definitely not perfect. It gives slightly soft outputs, I will revisit this embed once i get the hang of training efficiently. In case of any errors when using this embedd with Auto1111, try out the png embed instead. Examples:- ![cabin vray.png](https://s3.amazonaws.com/moonup/production/uploads/1669834885077-633a520aecbd8b19357b4806.png) ![car.png](https://s3.amazonaws.com/moonup/production/uploads/1669834906577-633a520aecbd8b19357b4806.png) ![lion v-ray.png](https://s3.amazonaws.com/moonup/production/uploads/1669834916379-633a520aecbd8b19357b4806.png) ![ship.png](https://s3.amazonaws.com/moonup/production/uploads/1669834936057-633a520aecbd8b19357b4806.png) ![00019-3721583948.png](https://s3.amazonaws.com/moonup/production/uploads/1669834960130-633a520aecbd8b19357b4806.png) ![human.png](https://s3.amazonaws.com/moonup/production/uploads/1669835006537-633a520aecbd8b19357b4806.png)

abdalrahmanshahrour/ShahrourDamageLenses

abdalrahmanshahrour

2022-11-30T19:01:07Z

0

null

[ "region:us" ]

null

2022-11-30T18:37:14Z

# Damage-detection ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/damage-prj.png?raw=true) prj files: ## step 1: download all files 1. clone my repo ```python git clone https://github.com/AbdelrahmanShahrour/Damage-detection.git ``` 2. get data and models files from [here](https://drive.google.com/drive/folders/1vXaD8z2J_kbh8oDU4rNcyuPoXgjOSRKs?usp=sharing) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-34-48.png?raw=true) ## step 2: creat venv and install all lib ```python python3 -m venv env ``` ```python source env/bin/activate ``` ```python pip3 install -r requirements.txt ``` ## step 3: open jupyter notebook ```python jupyter notebook ``` ## step 4: open `output.ipynb` and run all Cells ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-19.png?raw=true) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-28.png?raw=true) ![image](https://github.com/AbdelrahmanShahrour/Damage-detection/blob/main/output/Screenshot%20from%202022-11-27%2014-33-37.png?raw=true) # step 5: enjoy and develop this project and share his with me 😁👍🏻

andrewzhang505/isaacgym_humanoid

andrewzhang505

2022-11-30T19:00:40Z

9

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-30T01:40:53Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid type: Humanoid metrics: - type: mean_reward value: 8418.38 +/- 1855.54 name: mean_reward verified: false --- A(n) **APPO** model trained on the **Humanoid** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r andrewzhang505/isaacgym_humanoid ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.isaacgym_examples.enjoy_isaacgym --algo=APPO --env=Humanoid --train_dir=./train_dir --experiment=isaacgym_humanoid ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.isaacgym_examples.train_isaacgym --algo=APPO --env=Humanoid --train_dir=./train_dir --experiment=isaacgym_humanoid --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Crushtoe/GODEL-v1_1-base-seq2seq-vangluss

Crushtoe

2022-11-30T18:59:59Z

103

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-30T17:39:03Z

--- tags: - conversational --- # Vangluss: Bot Edition Trying (and failing) to use GODEL in place of DialoGPT.

htermotto/distilbert-base-uncased-finetuned-sngp-squad-seed-42

htermotto

2022-11-30T18:58:48Z

33

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-11-30T10:31:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-sngp-squad-seed-42 results: [] ---  # distilbert-base-uncased-finetuned-sngp-squad-seed-42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9074 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 2.4521 | 1.0 | 8248 | 2.0439 | | 2.1298 | 2.0 | 16496 | 1.9074 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

jmunoz/finetuning-sentiment-model-3000-samples

jmunoz

2022-11-30T18:41:53Z

106

0

transformers

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

text-classification

2022-11-29T22:47:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cpu - Datasets 1.2.1 - Tokenizers 0.12.1

ximboleta/Glebbo

ximboleta

2022-11-30T18:39:44Z

0

null

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

null

2022-11-30T18:39:44Z

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

edgertej/poebert-checkpoint-finetuned-poetry-foundation-2

edgertej

2022-11-30T17:14:10Z

78

0

transformers

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

fill-mask

2022-11-30T16:14:34Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: edgertej/poebert-checkpoint-finetuned-poetry-foundation-2 results: [] ---  # edgertej/poebert-checkpoint-finetuned-poetry-foundation-2 This model is a fine-tuned version of [edgertej/poebert-checkpoint-finetuned-poetry-foundation](https://huggingface.co/edgertej/poebert-checkpoint-finetuned-poetry-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8653 - Validation Loss: 3.5986 - 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', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.9003 | 3.6587 | 0 | | 3.8970 | 3.6169 | 1 | | 3.8653 | 3.5986 | 2 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.9.1 - Datasets 2.4.0 - Tokenizers 0.12.1

alexrofail/sd-class-butterflies-32

alexrofail

2022-11-30T16:31:22Z

33

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-30T16:29:47Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. In this run I just ran each cell of the NB to understand what is going on. Experimentation to follow 🙏 ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(alexrofail/sd-class-butterflies-32) image = pipeline().images[0] image ```

fathyshalab/all-roberta-large-v1-banking-17-16-5

fathyshalab

2022-11-30T15:28:05Z

105

0

transformers

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

text-classification

2022-11-28T21:57:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-17-16-5 results: [] ---  # all-roberta-large-v1-banking-17-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/all-roberta-large-v1-banking-16-16-5

fathyshalab

2022-11-30T15:24:44Z

105

0

transformers

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

text-classification

2022-11-28T21:34:27Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-16-16-5 results: [] ---  # all-roberta-large-v1-banking-16-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/all-roberta-large-v1-banking-14-16-5

fathyshalab

2022-11-30T15:17:52Z

106

0

transformers

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

text-classification

2022-11-28T20:48:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-14-16-5 results: [] ---  # all-roberta-large-v1-banking-14-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

gd1m3y/sentiment_bert

gd1m3y

2022-11-30T15:04:50Z

159

0

transformers

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

text-classification

2022-11-30T14:20:13Z

--- tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: sentiment_bert results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank config: sentences_66agree split: train args: sentences_66agree metrics: - name: Accuracy type: accuracy value: 0.9360189573459715 ---  # sentiment_bert This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.3754 - Accuracy: 0.9360 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

tomekkorbak/compassionate_hypatia

tomekkorbak

2022-11-30T14:23:57Z

103

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-29T19:22:43Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: compassionate_hypatia results: [] ---  # compassionate_hypatia This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'compassionate_hypatia', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3kybxs99

yorko/sd-class-butterflies-32

yorko

2022-11-30T13:41:32Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-30T13:30:35Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained("yorko/sd-class-butterflies-32") image = pipeline().images[0] image ```

nixmaverick1997/app-setfit-classifier

nixmaverick1997

2022-11-30T13:32:26Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-classifier", "transformers", "sentiment-classifier", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-31T16:11:57Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-classifier - transformers - sentiment-classifier --- # SetFit Sentiment Classifier This is a variant of the [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.  Uses Siamese and triplet network structures to generate semantically meaningful sentence embeddings ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install setfit ``` Then you can use the model like this: ```python from setfit import SetFitModel sentences = ["This is an example sentence", "Each sentence is converted"] model = SetFitModel.from_pretrained("nixmaverick1997/app-setfit-classifier") 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("nixmaverick1997/app-setfit-classifier") model = AutoModel.from_pretrained("nixmaverick1997/app-setfit-classifier") # 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  Loss class = CosineSimilarityLoss ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 640 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": 640, "warmup_steps": 64, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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

Watwat100/256data

Watwat100

2022-11-30T13:00:52Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-30T13:00:38Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 1576 with parameters: ``` {'batch_size': 13, '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": 3, "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": 4728, "warmup_steps": 473, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors

kejian/immaculate-filtering

kejian

2022-11-30T12:11:34Z

104

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-29T15:12:15Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: immaculate-filtering results: [] ---  # immaculate-filtering This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'immaculate-filtering', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3jjalm0n

GujjetiNagaraju/xlm-roberta-base-finetuned-Telugu_NLP

GujjetiNagaraju

2022-11-30T12:10:20Z

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-30T11:05:48Z

--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-Telugu_NLP results: [] ---  # xlm-roberta-base-finetuned-Telugu_NLP 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: 1.9986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4192 | 1.0 | 1250 | 2.1557 | | 2.2859 | 2.0 | 2500 | 2.0632 | | 2.2311 | 3.0 | 3750 | 2.0083 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

roscazo/DisTEMIST_fine_tuned_sentence

roscazo

2022-11-30T11:30:15Z

108

0

transformers

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

token-classification

2022-11-23T09:51:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: DisTEMIST_fine_tuned_sentence results: [] ---  # DisTEMIST_fine_tuned_sentence This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2164 - Precision: 0.6069 - Recall: 0.6401 - F1: 0.6231 ## 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: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=2.6e-09 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 73 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:| | 0.1166 | 1.0 | 1099 | 0.1152 | 0.5214 | 0.6433 | 0.5760 | | 0.0718 | 2.0 | 2198 | 0.1096 | 0.6015 | 0.6297 | 0.6153 | | 0.0438 | 3.0 | 3297 | 0.1517 | 0.6573 | 0.5895 | 0.6215 | | 0.0293 | 4.0 | 4396 | 0.1496 | 0.6212 | 0.6198 | 0.6205 | | 0.0179 | 5.0 | 5495 | 0.1665 | 0.5670 | 0.6505 | 0.6059 | | 0.0119 | 6.0 | 6594 | 0.1602 | 0.6035 | 0.6379 | 0.6202 | | 0.0078 | 7.0 | 7693 | 0.1844 | 0.6008 | 0.6347 | 0.6173 | | 0.0041 | 8.0 | 8792 | 0.2019 | 0.6006 | 0.6288 | 0.6144 | | 0.0026 | 9.0 | 9891 | 0.2075 | 0.6015 | 0.6270 | 0.6140 | | 0.0014 | 10.0 | 10990 | 0.2164 | 0.6069 | 0.6401 | 0.6231 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2