librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
chainup244/Qwen-Qwen1.5-1.8B-1719835855	chainup244	2024-07-01T12:12:48Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T12:10:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ProElectro07/MeddBot	ProElectro07	2024-07-01T12:12:31Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:11:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
youngdicey/tta-v2-sr-d30-h1024-p5	youngdicey	2024-07-02T02:22:07Z	0	null	[ "region:us" ]	null	2024-07-01T12:15:59Z	Entry not found
habulaj/2680026441	habulaj	2024-07-01T12:16:51Z	0	null	[ "region:us" ]	null	2024-07-01T12:16:42Z	Entry not found
chainup244/google-gemma-2b-1719836218	chainup244	2024-07-01T12:19:40Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T12:17:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
CristiD7/falcon7binstruct_workoutmodel_final	CristiD7	2024-07-01T14:02:16Z	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-07-01T12:17:11Z	Entry not found
vbafnaa/whisper-large-v3-hi	vbafnaa	2024-07-01T12:18:56Z	0	null	[ "region:us" ]	null	2024-07-01T12:18:56Z	Entry not found
ListIn360/naschain	ListIn360	2024-07-02T14:44:23Z	0	null	[ "region:us" ]	null	2024-07-01T12:19:31Z	Entry not found
diggy1/VisionForge	diggy1	2024-07-01T12:20:07Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T12:20:07Z	--- license: mit ---
shiowo/fastrepo	shiowo	2024-07-01T12:21:41Z	0	null	[ "region:us" ]	null	2024-07-01T12:20:43Z	Entry not found
Wipiii/semantic-test	Wipiii	2024-07-01T12:23:15Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-01T12:22:18Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-mpnet-base-v2 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('sentence-transformers/all-mpnet-base-v2') 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 import torch.nn.functional as F #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('sentence-transformers/all-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) 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=sentence-transformers/all-mpnet-base-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 384 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total* \| \| 1,170,060,424 \|
VKapseln475/DietollKapseln552	VKapseln475	2024-07-01T12:24:53Z	0	null	[ "region:us" ]	null	2024-07-01T12:23:47Z	# Dietoll Erfahrungen Bewertungen Deutschland kaufen Höhle Der Löwen Offizieller Preis Dietoll Deutschland Bewertungen Erfahrungen Das Medikament "Dietoll" ist eine bahnbrechende Lösung in der Welt der Gewichtsabnahme. Es bietet eine Reihe von Vorteilen, die es von anderen Produkten unterscheiden: Beschleunigung des Stoffwechsels: Dietoll fördert die Fettverbrennung durch eine Erhöhung des Stoffwechsels. Appetitzügler: Das Medikament hilft, den Heißhunger zu reduzieren und somit die tägliche Kalorienaufnahme zu begrenzen. Energieboost: Dietoll steigert die Energie und hilft Ihnen, aktiv zu bleiben und sich während Ihrer Gewichtsreduktionsreise fit zu fühlen. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Dietoll Kapseln zu kaufen](https://deutschlandbuzz.de/dietoll-de) ## Vorteile Dietol Kapseln bieten zahlreiche Vorteile für diejenigen, die schnell Gewicht verlieren möchten, insbesondere in Problemzonen wie Taille, Beine, Bauch und anderen Körperbereichen. Hier sind fünf der wichtigsten Vorteile von Dietol zur Gewichtsabnahme: Zielgerichtete Fettverbrennung: Dietol Kapseln fördern die Fettverbrennung in den Problemzonen, indem sie den Stoffwechsel ankurbeln und die Thermogenese erhöhen. Dadurch wird das Fett in diesen Bereichen effektiver verbrannt und abgebaut. Appetitkontrolle: Die natürlichen Inhaltsstoffe von Dietol helfen, das Hungergefühl zu reduzieren und den Appetit zu zügeln. Dies erleichtert das Einhalten einer ausgewogenen Ernährung und unterstützt die Gewichtsabnahme in den gewünschten Körperbereichen. Energie und Ausdauer: Durch die Verbesserung des Stoffwechsels und die Bereitstellung von Energie aus der Fettverbrennung können Dietol Kapseln dazu beitragen, dass Sie sich energiegeladener und leistungsfähiger fühlen. Dies kann Ihnen helfen, aktiver zu sein und zusätzliche Kalorien zu verbrennen, was zu einem schnelleren Gewichtsverlust in den Problemzonen führt. Entgiftung und Reinigung: Dietol Kapseln enthalten auch Inhaltsstoffe, die zur Entgiftung und Reinigung des Körpers beitragen. Dies kann dazu führen, dass überschüssige Flüssigkeiten und Toxine aus dem Körper ausgeschieden werden, was zu einer schlankeren Erscheinung und einer verbesserten Gesundheit führt. Gesunde Gewichtsabnahme: Im Gegensatz zu einigen Diätpillen, die auf chemischen Inhaltsstoffen basieren, sind Dietol Kapseln aus natürlichen Zutaten hergestellt, die eine gesunde und sichere Gewichtsabnahme fördern. Dies macht es zu einer idealen Wahl für diejenigen, die schnell und effektiv Gewicht verlieren möchten, ohne ihre Gesundheit zu beeinträchtigen. Durch die Einnahme von Dietol Kapseln können Sie erwarten, dass sich Ihre Problemzonen wie Taille, Beine, Bauch und andere Körperbereiche sichtbar verkleinern. Die Kombination aus Fettverbrennung, Appetitkontrolle, Energie, Entgiftung und gesunder Gewichtsabnahme macht Dietol zu einer effektiven Lösung für eine schnellere und gezielte Gewichtsabnahme. ## Wie soll man Dietoll einnehmen Um überschüssiges Fett in Problemzonen des Körpers mit Dietol Kapseln schnell loszuwerden, sollten Sie die folgenden Schritte befolgen:1. Einnahmehäufigkeit: Nehmen Sie die Kapseln jeden Tag, morgens und abends, ein, um eine kontinuierliche Wirkung zu gewährleisten. Tagesdosis: Sie sollten insgesamt zwei Kapseln pro Tag einnehmen – eine Kapsel morgens und eine weitere Kapsel abends. Einnahmedauer: Um optimale Ergebnisse zu erzielen, ist es wichtig, Dietol für mindestens 30 Tage einzunehmen.Durch die Einhaltung dieser einfachen Schritte können Sie mit Dietol effektiv Fett abbauen und Ihre Problemzonen gezielt reduzieren. ## Kontraindikationen Obwohl Dietol Kapseln aus natürlichen Inhaltsstoffen bestehen, gibt es einige Kontraindikationen, die beachtet werden sollten: ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Dietoll Kapseln zu kaufen](https://deutschlandbuzz.de/dietoll-de)
Nerdofdot/trial3	Nerdofdot	2024-07-01T12:23:51Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:23:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
xyymmm/Phi3-mini-finetune-0701	xyymmm	2024-07-01T12:25:28Z	0	null	[ "region:us" ]	null	2024-07-01T12:25:28Z	Entry not found
mmbutera/karinav2	mmbutera	2024-07-01T12:27:14Z	0	null	[ "license:openrail", "region:us" ]	null	2024-07-01T12:26:11Z	--- license: openrail ---
Meziane/qwuestion_answering_T5_policy_qa_2	Meziane	2024-07-01T13:20:27Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]	question-answering	2024-07-01T12:26:24Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: qwuestion_answering_T5_policy_qa_2 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. --> # qwuestion_answering_T5_policy_qa_2 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 4 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
metriccoders/fine-tuned-gpt2	metriccoders	2024-07-01T12:27:09Z	0	null	[ "region:us" ]	null	2024-07-01T12:27:09Z	Entry not found
Gaysa/hoistudios-2000	Gaysa	2024-07-02T03:47:29Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T12:27:22Z	--- license: apache-2.0 ---
greenbureau/intentsensor-01.4	greenbureau	2024-07-01T12:27:47Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:27:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
yemen2016/dfm_1_NC	yemen2016	2024-07-01T12:37:08Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-01T12:28:31Z	Entry not found
xbsu/LW-DETR	xbsu	2024-07-01T12:49:20Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T12:28:53Z	--- license: apache-2.0 ---
Seyhqn/seansty	Seyhqn	2024-07-01T12:29:20Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T12:29:20Z	--- license: apache-2.0 ---
metriccoders/fine-tuned-gpt2-colab	metriccoders	2024-07-01T12:30:49Z	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T12:30:31Z	Entry not found
joshnavi/comic	joshnavi	2024-07-01T12:33:41Z	0	null	[ "region:us" ]	null	2024-07-01T12:33:41Z	Entry not found
BiblioSaludGros/TOPA	BiblioSaludGros	2024-07-01T12:34:31Z	0	null	[ "region:us" ]	null	2024-07-01T12:34:31Z	Entry not found
gseven9977/klmlive	gseven9977	2024-07-01T12:44:33Z	0	null	[ "region:us" ]	null	2024-07-01T12:34:35Z	Entry not found
habulaj/2516624858	habulaj	2024-07-01T12:35:18Z	0	null	[ "region:us" ]	null	2024-07-01T12:35:16Z	Entry not found
CombsAI/jurisai-vita	CombsAI	2024-07-01T12:36:02Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:35:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
amaalanoosucs/rpe_llama3_1_epoch_dict_merged_4bit	amaalanoosucs	2024-07-01T12:36:16Z	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:36:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: amaalanoosucs - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
chengzhiyuan/save	chengzhiyuan	2024-07-01T12:37:12Z	0	null	[ "region:us" ]	null	2024-07-01T12:37:12Z	Entry not found
itay-nakash/model_42d9b05c5c_sweep_cosmic-spaceship-1148	itay-nakash	2024-07-01T12:37:24Z	0	null	[ "region:us" ]	null	2024-07-01T12:37:24Z	Entry not found
ClementineBleuze/roberta_prefix_cont_lr_SEP	ClementineBleuze	2024-07-01T14:01:31Z	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-01T12:38:06Z	--- license: mit base_model: FacebookAI/roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_prefix_cont_lr_SEP 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_prefix_cont_lr_SEP This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1079 - F1 Weighted: 0.8821 - F1 Samples: 0.8896 - F1 Macro: 0.7834 - F1 Micro: 0.8835 - Accuracy: 0.8606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Weighted \| F1 Samples \| F1 Macro \| F1 Micro \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-----------:\|:----------:\|:--------:\|:--------:\|:--------:\| \| 0.2596 \| 0.3381 \| 500 \| 0.1790 \| 0.7290 \| 0.7276 \| 0.4432 \| 0.7553 \| 0.7003 \| \| 0.1651 \| 0.6761 \| 1000 \| 0.1339 \| 0.8058 \| 0.8084 \| 0.6150 \| 0.8242 \| 0.7855 \| \| 0.1371 \| 1.0142 \| 1500 \| 0.1327 \| 0.8204 \| 0.8216 \| 0.6813 \| 0.8255 \| 0.7903 \| \| 0.1162 \| 1.3523 \| 2000 \| 0.1267 \| 0.8233 \| 0.8174 \| 0.6822 \| 0.8276 \| 0.7896 \| \| 0.114 \| 1.6903 \| 2500 \| 0.1172 \| 0.8455 \| 0.8546 \| 0.6805 \| 0.8541 \| 0.8241 \| \| 0.1074 \| 2.0284 \| 3000 \| 0.1130 \| 0.8560 \| 0.8606 \| 0.7147 \| 0.8604 \| 0.8288 \| \| 0.086 \| 2.3665 \| 3500 \| 0.1121 \| 0.8626 \| 0.8703 \| 0.7226 \| 0.8686 \| 0.8430 \| \| 0.0841 \| 2.7045 \| 4000 \| 0.1063 \| 0.8693 \| 0.8758 \| 0.7233 \| 0.8756 \| 0.8484 \| \| 0.0828 \| 3.0426 \| 4500 \| 0.1071 \| 0.8659 \| 0.8750 \| 0.7234 \| 0.8707 \| 0.8471 \| \| 0.067 \| 3.3807 \| 5000 \| 0.1075 \| 0.8700 \| 0.8773 \| 0.7265 \| 0.8750 \| 0.8478 \| \| 0.0644 \| 3.7187 \| 5500 \| 0.1090 \| 0.8681 \| 0.8770 \| 0.7223 \| 0.8723 \| 0.8444 \| \| 0.0639 \| 4.0568 \| 6000 \| 0.1114 \| 0.8631 \| 0.8657 \| 0.7236 \| 0.8659 \| 0.8356 \| \| 0.0488 \| 4.3949 \| 6500 \| 0.1079 \| 0.8821 \| 0.8896 \| 0.7834 \| 0.8835 \| 0.8606 \| \| 0.0516 \| 4.7329 \| 7000 \| 0.1082 \| 0.8752 \| 0.8836 \| 0.7423 \| 0.8775 \| 0.8505 \| \| 0.0475 \| 5.0710 \| 7500 \| 0.1082 \| 0.8821 \| 0.8910 \| 0.7550 \| 0.8849 \| 0.8620 \| \| 0.0427 \| 5.4091 \| 8000 \| 0.1164 \| 0.8774 \| 0.8835 \| 0.7714 \| 0.8760 \| 0.8505 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
xyymmm/Phi3-mini-4k-finetune-0701	xyymmm	2024-07-01T12:38:23Z	0	null	[ "region:us" ]	null	2024-07-01T12:38:23Z	Entry not found
itsadeel/tinyllama-v2.1.0	itsadeel	2024-07-01T12:39:50Z	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-07-01T12:39:12Z	--- license: apache-2.0 ---
onnx-community/whisper-tiny.en_timestamped	onnx-community	2024-07-02T16:20:50Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:39:37Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
onnx-community/whisper-tiny_timestamped	onnx-community	2024-07-02T16:20:53Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:40:30Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
abdullah-jokergames/bert-7b-joker-v1	abdullah-jokergames	2024-07-01T12:41:06Z	0	transformers	[ "transformers", "safetensors", "distilbert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2024-07-01T12:40:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
onnx-community/whisper-base.en_timestamped	onnx-community	2024-07-02T16:20:56Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:41:09Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-base.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
onnx-community/whisper-base_timestamped	onnx-community	2024-07-02T16:20:58Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:42:02Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-base with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
itay-nakash/model_42d9b05c5c_sweep_helpful-sponge-1149	itay-nakash	2024-07-01T12:42:53Z	0	null	[ "region:us" ]	null	2024-07-01T12:42:53Z	Entry not found
onnx-community/whisper-small.en_timestamped	onnx-community	2024-07-02T16:21:00Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:42:55Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-small.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
onnx-community/whisper-small_timestamped	onnx-community	2024-07-02T16:21:02Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:45:02Z	--- library_name: transformers.js --- https://huggingface.co/openai/whisper-small with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
BenjaminBruenau/mistral-rag-instruct-raft	BenjaminBruenau	2024-07-01T12:45:08Z	0	null	[ "region:us" ]	null	2024-07-01T12:45:08Z	Entry not found
hsgwktb/GPT-SoVITS_Models	hsgwktb	2024-07-01T13:35:36Z	0	null	[ "region:us" ]	null	2024-07-01T12:45:45Z	Entry not found
618AI/dictalm2-it-qa-fine-tune	618AI	2024-07-02T11:54:14Z	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "he", "base_model:dicta-il/dictalm2.0-instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T12:46:44Z	--- library_name: transformers base_model: dicta-il/dictalm2.0-instruct license: apache-2.0 language: - he pipeline_tag: text-generation --- # Model Card for Guysh1805/dictalm2-it-qa-fine-tune This is a fine-tuned version of the Dicta-IL dictalm2.0-instruct model, specifically tailored for generating question-answer pairs in Hebrew. ## Model Details ### Model Description The model, Guysh1805/dictalm2-it-qa-fine-tunetuned, is a fine-tuned version of the dictalm2.0-instruct model on a both synthetically generated datasets and already existing Q&A datasets wrapped in insturction prompts. - Developed by: Guy Shapira - Model type: Transformer-based, fine-tuned Dicta-IL dictalm2.0-instruct - Language(s) (NLP): Hebrew - Finetuned from: dicta-il/dictalm2.0-instruct ## How to Get Started with the Model To get started, load the model using the Transformers library by Hugging Face: ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer model_name = "Guysh1805/dictalm2-it-qa-fine-tune" model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ```
Gaysa/Barzhanov	Gaysa	2024-07-01T12:52:02Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-07-01T12:47:06Z	--- license: apache-2.0 ---
onnx-community/distil-medium.en_timestamped	onnx-community	2024-07-02T16:21:05Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:47:08Z	--- library_name: transformers.js --- https://huggingface.co/distil-whisper/distil-medium.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
HassanSM/Model_A	HassanSM	2024-07-01T12:48:43Z	0	null	[ "region:us" ]	null	2024-07-01T12:48:43Z	Entry not found
SettiE03/finbertt	SettiE03	2024-07-01T12:48:59Z	0	null	[ "region:us" ]	null	2024-07-01T12:48:59Z	Entry not found
itay-nakash/model_42d9b05c5c_sweep_glad-glade-1150	itay-nakash	2024-07-01T12:49:09Z	0	null	[ "region:us" ]	null	2024-07-01T12:49:09Z	Entry not found
onnx-community/distil-small.en_timestamped	onnx-community	2024-07-02T16:21:08Z	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]	automatic-speech-recognition	2024-07-01T12:49:50Z	--- library_name: transformers.js --- https://huggingface.co/distil-whisper/distil-small.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
habulaj/335839301372	habulaj	2024-07-01T12:50:41Z	0	null	[ "region:us" ]	null	2024-07-01T12:50:37Z	Entry not found
casque/Cowgirlfrombehind-10	casque	2024-07-01T12:51:04Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-07-01T12:50:40Z	--- license: creativeml-openrail-m ---
somameeta/my_legal_llm	somameeta	2024-07-01T12:58:43Z	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-07-01T12:51:54Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
igorktech/nllb-spa-30k	igorktech	2024-07-01T12:52:31Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:52:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
olonok/phi-3-mini-4k-instruct-q8_0-gguf	olonok	2024-07-01T13:00:42Z	0	null	[ "phi-3-mini-4k-instruct-q8_0", "GGUF", "Model microsoft/phi-3-mini-4k-instruct converted to GGUF with LLAMA.cpp format int8", "license:mit", "region:us" ]	null	2024-07-01T12:53:43Z	--- license: mit tags: - phi-3-mini-4k-instruct-q8_0 - GGUF - Model microsoft/phi-3-mini-4k-instruct converted to GGUF with LLAMA.cpp format int8 ---
ShamelessAND/Customed-Tokenizer_Licensed	ShamelessAND	2024-07-01T12:54:22Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:54:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
niektchandra/learninig	niektchandra	2024-07-01T12:54:42Z	0	null	[ "region:us" ]	null	2024-07-01T12:54:42Z	Entry not found
hon9kon9ize/CantoneseLLMChat-v0.5	hon9kon9ize	2024-07-02T01:43:05Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T12:56:01Z	--- license: apache-2.0 ---
hsgwktb/GPT-SoVITS_Reference_Audio	hsgwktb	2024-07-01T13:40:36Z	0	null	[ "region:us" ]	null	2024-07-01T12:57:06Z	Entry not found
ShamelessAND/Customed-Tokenizer_UnLicensed	ShamelessAND	2024-07-01T12:59:50Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T12:59:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gokul00060/loara-LAMX22	gokul00060	2024-07-01T13:40:48Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-01T13:00:40Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 widget: - text: "Open browser" tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: gokul00060 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Faraz01/DemoModel_1	Faraz01	2024-07-01T13:21:35Z	0	null	[ "region:us" ]	null	2024-07-01T13:01:10Z	#Example Model Card This is my model card. --- license: mit ---
vayvahid/equ	vayvahid	2024-07-01T13:01:54Z	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2024-07-01T13:01:54Z	--- license: bsd-3-clause ---
suosuo321/last_try	suosuo321	2024-07-01T21:46:21Z	0	null	[ "region:us" ]	null	2024-07-01T13:02:16Z	Invalid username or password.
Sound24/t5-small-finetuned-xsum	Sound24	2024-07-01T13:24:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-07-01T13:03:03Z	Entry not found
ranyrotbard/trained-sd3-lora	ranyrotbard	2024-07-01T13:03:12Z	0	null	[ "region:us" ]	null	2024-07-01T13:03:12Z	Entry not found
nasbot/naschain	nasbot	2024-07-01T13:05:17Z	0	null	[ "region:us" ]	null	2024-07-01T13:05:12Z	Entry not found
itay-nakash/model_42d9b05c5c_sweep_light-star-1151	itay-nakash	2024-07-01T13:05:33Z	0	null	[ "region:us" ]	null	2024-07-01T13:05:33Z	Entry not found
v1vu/Deberta_science_exam	v1vu	2024-07-01T13:05:50Z	0	null	[ "license:mit", "region:us" ]	null	2024-07-01T13:05:50Z	--- license: mit ---
nasbot2/naschain	nasbot2	2024-07-01T13:08:02Z	0	null	[ "region:us" ]	null	2024-07-01T13:07:59Z	Entry not found
Abdine/Medical-NER	Abdine	2024-07-01T13:10:28Z	0	null	[ "region:us" ]	null	2024-07-01T13:10:28Z	Entry not found
TestTWS/naschain	TestTWS	2024-07-01T13:10:34Z	0	null	[ "region:us" ]	null	2024-07-01T13:10:31Z	Entry not found
olonok/phi-3-mini-128k-instruct-q8_0-gguf	olonok	2024-07-01T13:21:24Z	0	null	[ "arxiv:1910.09700", "license:mit", "region:us" ]	null	2024-07-01T13:10:55Z	--- license: mit --- # Model Card for Model ID Model Microsoft Phi3 mini 128k Instructct quantized to 8bits with Llama.cpp This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description Model Microsoft Phi3 mini 128k Instructct quantized to 8bits with Llama.cpp - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NeumannsPaule/FirstTry	NeumannsPaule	2024-07-01T13:10:59Z	0	null	[ "license:eupl-1.1", "region:us" ]	null	2024-07-01T13:10:59Z	--- license: eupl-1.1 ---
nasbot3/naschain	nasbot3	2024-07-01T22:45:02Z	0	null	[ "region:us" ]	null	2024-07-01T13:10:59Z	Entry not found
habulaj/98040185214	habulaj	2024-07-01T13:11:03Z	0	null	[ "region:us" ]	null	2024-07-01T13:11:01Z	Entry not found
habulaj/11265887480	habulaj	2024-07-01T13:12:08Z	0	null	[ "region:us" ]	null	2024-07-01T13:12:07Z	Entry not found
ikedachin/codeparrot	ikedachin	2024-07-01T13:22:04Z	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T13:12:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
itay-nakash/model_42d9b05c5c_sweep_easy-mountain-1152	itay-nakash	2024-07-01T13:12:50Z	0	null	[ "region:us" ]	null	2024-07-01T13:12:50Z	Entry not found
nasbot4/naschain	nasbot4	2024-07-01T22:29:32Z	0	null	[ "region:us" ]	null	2024-07-01T13:13:45Z	Entry not found
habulaj/10904388263	habulaj	2024-07-01T13:14:00Z	0	null	[ "region:us" ]	null	2024-07-01T13:13:54Z	Entry not found
oleshy/ontochem_bioberti_cross_valid_1	oleshy	2024-07-01T13:16:13Z	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-01T13:14:11Z	--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_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. --> # ontochem_bioberti_cross_valid_1 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0598 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 0.0631 \| \| No log \| 2.0 \| 10 \| 0.0628 \| \| No log \| 3.0 \| 15 \| 0.0623 \| \| No log \| 4.0 \| 20 \| 0.0617 \| \| No log \| 5.0 \| 25 \| 0.0612 \| \| No log \| 6.0 \| 30 \| 0.0607 \| \| No log \| 7.0 \| 35 \| 0.0604 \| \| No log \| 8.0 \| 40 \| 0.0601 \| \| No log \| 9.0 \| 45 \| 0.0600 \| \| No log \| 10.0 \| 50 \| 0.0598 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Makkoen/whisper-large-cit-synth-do015-wd0-lr5e-06-1000	Makkoen	2024-07-01T17:25:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-01T13:15:38Z	--- language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: ./whisper-large-cit-synth-do015-wd0-lr5e-06-1000 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-large-cit-synth-do015-wd0-lr5e-06-1000 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 1000 dataset. It achieves the following results on the evaluation set: - Loss: 0.4526 - Wer: 20.3899 ## Model description More information needed ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - 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 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.7187 \| 0.8889 \| 50 \| 0.4062 \| 24.2105 \| \| 0.4122 \| 1.7778 \| 100 \| 0.3523 \| 22.3782 \| \| 0.2917 \| 2.6667 \| 150 \| 0.3494 \| 23.5867 \| \| 0.2242 \| 3.5556 \| 200 \| 0.3618 \| 23.0019 \| \| 0.1529 \| 4.4444 \| 250 \| 0.3770 \| 22.3392 \| \| 0.1322 \| 5.3333 \| 300 \| 0.3906 \| 21.2476 \| \| 0.0987 \| 6.2222 \| 350 \| 0.4133 \| 20.9747 \| \| 0.0798 \| 7.1111 \| 400 \| 0.4302 \| 23.8986 \| \| 0.0613 \| 8.0 \| 450 \| 0.4438 \| 20.5848 \| \| 0.0545 \| 8.8889 \| 500 \| 0.4526 \| 20.3899 \| ### Framework versions - Transformers 4.42.3 - Pytorch 1.13.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
renatoguariglia/Pietra	renatoguariglia	2024-07-01T13:16:23Z	0	null	[ "region:us" ]	null	2024-07-01T13:16:23Z	Entry not found
AI4MIRO/demo	AI4MIRO	2024-07-02T12:33:45Z	0	null	[ "en", "region:us" ]	null	2024-07-01T13:16:54Z	--- language: - en --- # <!--> Provide a quick summary of what the model is/does. <!--> # Table of Contents - [](#-model_name) - [Table of Contents](#table-of-contents) - [1. Model Basic Information](#model-basic-information) - [2. Technical Specifications](#technical-specifications) - [3. Training Details](#training-details) - [4. Model Evaluation](#model-evaluation) - [5. Model Examination](#model-examination) # 1. Model Basic Information <!--> This section provides basic information about what the model is, its current status, and where it came from.. <!--> - Version: 0 ## 1.1. Intended use - Treatment site ICD10: More information needed - Treatment modality: More information needed - Prescription levels [Gy]: More information needed - Additional information: More information needed - Motivation for development: More information needed - Class: More information needed - Creation date: 2024-07-02 - Type of architecture More information needed ## 1.2. Development and deployment - Developed by: Name: More information needed, Instutution: More information needed, Email: More information needed - Funded by: More information needed - Shared by: More information needed - License: More information needed - Finetuned from model: More information needed - Related Research Paper(s): More information needed - Related Git Repository: More information needed ## 1.3. How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> More information needed </details> # 2. Technical Specifications ## 2.1. Model architecture - Total number of parameters: 5 - Input channels: More information needed - Loss function More information needed - Batch size 1 - Patch dimension More information needed <img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/xGGUE5spRY6tar5R4VeKX.png" alt="error while loading image"> _Figure 1: Model architecture_ - Libraries/Dependencies: More information needed - Hardware recommended: More information needed - Inference time for recommended [seconds] 10 # 3. Training Details - Training set size: 10 - Validation set size: 10 Age distribution \| Sex distribution --- \| --- ![](age_distribution.png) \| ![](sex_distribution.png) - Dataset source: More information needed - Acquisition date from 2024-07-02 to 2024-07-02 # 4. Model Evaluation # 5. Model Examination <!--> This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. <!-->
nasbot6/naschain	nasbot6	2024-07-01T22:22:20Z	0	null	[ "region:us" ]	null	2024-07-01T13:17:16Z	Entry not found
Abdine/bert-base-cased	Abdine	2024-07-01T13:21:36Z	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-01T13:17:48Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2087 - Precision: 0.3464 - Recall: 0.4037 - F1: 0.3728 - Accuracy: 0.9315 ## Model description More information needed ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 88 \| 0.2396 \| 0.2519 \| 0.2699 \| 0.2606 \| 0.9228 \| \| No log \| 2.0 \| 176 \| 0.2117 \| 0.3490 \| 0.3397 \| 0.3443 \| 0.9315 \| \| No log \| 3.0 \| 264 \| 0.2087 \| 0.3464 \| 0.4037 \| 0.3728 \| 0.9315 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0 - Datasets 2.14.7 - Tokenizers 0.19.1
oleshy/ontochem_bioberti_cross_valid_1_20	oleshy	2024-07-01T13:20:12Z	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-01T13:18:08Z	--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_1_20 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. --> # ontochem_bioberti_cross_valid_1_20 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 0.0598 \| \| No log \| 2.0 \| 10 \| 0.0598 \| \| No log \| 3.0 \| 15 \| 0.0597 \| \| No log \| 4.0 \| 20 \| 0.0596 \| \| No log \| 5.0 \| 25 \| 0.0594 \| \| No log \| 6.0 \| 30 \| 0.0593 \| \| No log \| 7.0 \| 35 \| 0.0591 \| \| No log \| 8.0 \| 40 \| 0.0588 \| \| No log \| 9.0 \| 45 \| 0.0585 \| \| No log \| 10.0 \| 50 \| 0.0583 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
itay-nakash/model_42d9b05c5c_sweep_jolly-water-1153	itay-nakash	2024-07-01T13:18:17Z	0	null	[ "region:us" ]	null	2024-07-01T13:18:17Z	Entry not found
JrX44/gemma-2b-it-fine-tune-email	JrX44	2024-07-01T13:23:07Z	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T13:18:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
pratapaadii/SignLang_v1	pratapaadii	2024-07-01T13:25:15Z	0	null	[ "license:cc", "region:us" ]	null	2024-07-01T13:18:43Z	--- license: cc ---
sail/data-mixture-random-1b	sail	2024-07-03T00:50:25Z	0	null	[ "en", "dataset:sail/regmix-data", "dataset:sail/regmix-data-sample", "arxiv:2407.01492", "license:mit", "region:us" ]	null	2024-07-01T13:19:38Z	--- license: mit datasets: - sail/regmix-data - sail/regmix-data-sample language: - en --- # Models Trained with Random Mixture This is a collection of 64 language models, each with approximately 1B parameters, trained on different random mixtures of data. This project aims to validate the generalization capabilities of the RegMix approach (https://huggingface.co/papers/2407.01492) from small-scale (e.g., 1M parameters) to large-scale (e.g., 1B parameters) models. ## Key Features - Model Size: 64 separate models, each with ~1B parameters - Training Data: Random data mixtures on the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset - Purpose: To validate the effectiveness of RegMix on identifying high-performing data mixture ## Dataset The models were trained using the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset, which is split into different domains from The Pile dataset. ## Training Hyperparameters \| Hyperparameter \| Value \| \|:---------------\|:------\| \| Batch Size \| 1M tokens \| \| Learning Rate \| 4e-4 \| \| Minimum Learning Rate \| 1e-5 \| \| Learning Rate Schedule \| Cosine \| \| Warmup Ratio \| 4% \| \| Total Tokens \| 25B \| ## How to Load a Model You can load any model using the corresponding branch with the Hugging Face Transformers library: ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1") tokenizer = AutoTokenizer.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1") ``` ## Data Mixture The specific data mixture used for training each 1B model can be found in the file `train_config.yaml` in each corresponding model branch. ## Model Variants To access different model variants, simply change the `revision` parameter in the `from_pretrained` method to the desired model index (e.g., "model-index-2", "model-index-3"), and the maxium index is 64. ## Usage Notes - These models are primarily intended for research purposes. - Performance may vary depending on the specific task and domain. ## Citation If you use these models in your research, please cite the RegMix paper: ``` @misc{liu2024regmix, title={RegMix: Data Mixture as Regression for Language Model Pre-training}, author={Qian Liu and Xiaosen Zheng and Niklas Muennighoff and Guangtao Zeng and Longxu Dou and Tianyu Pang and Jing Jiang and Min Lin}, year={2024}, eprint={2407.01492}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.01492}, } ``` For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492). ## Performance We evaluated each model using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The performance metric for each task is the average of 0-shot to 5-shot `accnorm` (accuracy normalized, if available) or `acc` (accuracy) scores. ### Table 1: Model Index 1-8 \| Task \| Model 1 \| Model 2 \| Model 3 \| Model 4 \| Model 5 \| Model 6 \| Model 7 \| Model 8 \| \|---------------\|---------\|---------\|---------\|---------\|---------\|---------\|---------\|---------\| \| Social IQA \| 33.27 \| 33.33 \| 33.62 \| 33.53 \| 33.49 \| 33.56 \| 33.62 \| 33.55 \| \| HellaSwag \| 40.58 \| 36.86 \| 40.58 \| 36.06 \| 40.07 \| 37.85 \| 37.93 \| 39.59 \| \| PiQA \| 67.29 \| 65.14 \| 67.97 \| 64.66 \| 67.03 \| 65.36 \| 66.00 \| 66.55 \| \| OpenBookQA \| 28.63 \| 27.87 \| 29.33 \| 29.10 \| 29.23 \| 28.33 \| 29.13 \| 28.73 \| \| Lambada \| 29.17 \| 26.86 \| 31.55 \| 27.11 \| 29.16 \| 28.92 \| 31.53 \| 30.92 \| \| SciQ \| 80.68 \| 79.98 \| 81.05 \| 80.80 \| 82.40 \| 79.88 \| 78.67 \| 79.70 \| \| COPA \| 70.50 \| 63.83 \| 69.17 \| 65.00 \| 67.50 \| 66.00 \| 66.67 \| 68.67 \| \| RACE \| 29.47 \| 30.00 \| 32.11 \| 28.82 \| 31.13 \| 30.06 \| 29.90 \| 30.75 \| \| ARC Easy \| 50.03 \| 48.72 \| 50.01 \| 46.64 \| 51.06 \| 47.46 \| 46.75 \| 48.39 \| \| LogiQA \| 23.76 \| 24.17 \| 25.29 \| 25.29 \| 24.55 \| 25.96 \| 25.45 \| 26.32 \| \| QQP \| 55.71 \| 55.90 \| 54.84 \| 56.52 \| 54.01 \| 56.34 \| 52.35 \| 54.20 \| \| WinoGrande \| 51.54 \| 51.59 \| 51.39 \| 50.91 \| 53.13 \| 52.26 \| 51.26 \| 51.45 \| \| MultiRC \| 52.65 \| 53.39 \| 51.89 \| 50.92 \| 49.03 \| 53.09 \| 53.64 \| 50.23 \| \| Average \| 47.18 \| 45.97 \| 47.60 \| 45.80 \| 47.06 \| 46.54 \| 46.38 \| 46.85 \| ### Table 2: Model Index 9-16 \| Task \| Model 9 \| Model 10 \| Model 11 \| Model 12 \| Model 13 \| Model 14 \| Model 15 \| Model 16 \| \|---------------\|---------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.43 \| 33.21 \| 33.31 \| 33.17 \| 33.28 \| 32.43 \| 33.57 \| 33.70 \| \| HellaSwag \| 40.05 \| 35.89 \| 39.55 \| 39.89 \| 38.63 \| 36.18 \| 39.52 \| 35.94 \| \| PiQA \| 66.60 \| 64.74 \| 66.29 \| 66.27 \| 66.90 \| 64.05 \| 66.70 \| 64.51 \| \| OpenBookQA \| 28.87 \| 26.60 \| 29.33 \| 28.73 \| 29.40 \| 27.87 \| 29.67 \| 27.83 \| \| Lambada \| 31.39 \| 27.37 \| 30.32 \| 30.31 \| 31.38 \| 26.25 \| 29.86 \| 26.95 \| \| SciQ \| 81.10 \| 79.12 \| 79.97 \| 82.85 \| 79.42 \| 81.40 \| 81.38 \| 81.23 \| \| COPA \| 67.00 \| 64.50 \| 66.83 \| 69.50 \| 67.33 \| 65.83 \| 69.50 \| 66.33 \| \| RACE \| 30.57 \| 29.63 \| 30.49 \| 30.85 \| 30.35 \| 28.66 \| 31.21 \| 29.57 \| \| ARC Easy \| 50.66 \| 47.74 \| 47.47 \| 50.18 \| 49.92 \| 49.52 \| 50.73 \| 48.65 \| \| LogiQA \| 23.60 \| 25.65 \| 26.37 \| 23.81 \| 25.58 \| 26.29 \| 25.86 \| 25.12 \| \| QQP \| 54.89 \| 54.79 \| 54.20 \| 55.23 \| 53.69 \| 57.09 \| 53.95 \| 54.24 \| \| WinoGrande \| 50.83 \| 51.84 \| 51.05 \| 51.83 \| 52.12 \| 52.00 \| 51.01 \| 51.82 \| \| MultiRC \| 54.18 \| 54.48 \| 50.17 \| 52.12 \| 51.42 \| 52.69 \| 51.87 \| 53.48 \| \| Average \| 47.17 \| 45.81 \| 46.57 \| 47.29 \| 46.88 \| 46.17 \| 47.30 \| 46.11 \| ### Table 3: Model Index 17-24 \| Task \| Model 17 \| Model 18 \| Model 19 \| Model 20 \| Model 21 \| Model 22 \| Model 23 \| Model 24 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.89 \| 33.31 \| 33.53 \| 33.38 \| 33.75 \| 33.24 \| 33.56 \| 33.71 \| \| HellaSwag \| 38.68 \| 39.90 \| 34.67 \| 37.12 \| 37.44 \| 36.07 \| 42.15 \| 34.67 \| \| PiQA \| 66.83 \| 67.39 \| 63.33 \| 64.83 \| 65.00 \| 63.68 \| 67.80 \| 62.99 \| \| OpenBookQA \| 28.13 \| 30.67 \| 28.03 \| 29.40 \| 27.67 \| 27.77 \| 29.37 \| 25.83 \| \| Lambada \| 28.78 \| 28.56 \| 24.13 \| 29.41 \| 27.67 \| 28.03 \| 33.47 \| 24.04 \| \| SciQ \| 79.60 \| 78.83 \| 77.42 \| 78.98 \| 78.95 \| 78.72 \| 81.83 \| 79.12 \| \| COPA \| 65.17 \| 68.17 \| 65.33 \| 67.33 \| 67.67 \| 62.67 \| 69.83 \| 65.83 \| \| RACE \| 28.74 \| 30.03 \| 29.76 \| 29.49 \| 30.77 \| 29.76 \| 31.21 \| 27.91 \| \| ARC Easy \| 48.86 \| 49.42 \| 47.90 \| 48.30 \| 47.88 \| 46.68 \| 50.92 \| 45.24 \| \| LogiQA \| 25.91 \| 26.34 \| 26.24 \| 25.76 \| 26.11 \| 26.24 \| 24.17 \| 25.91 \| \| QQP \| 53.35 \| 53.18 \| 50.61 \| 51.49 \| 54.27 \| 54.99 \| 52.77 \| 55.19 \| \| WinoGrande \| 52.54 \| 51.17 \| 52.01 \| 51.09 \| 52.13 \| 52.03 \| 52.50 \| 50.28 \| \| MultiRC \| 51.49 \| 52.45 \| 55.40 \| 54.87 \| 51.73 \| 49.49 \| 50.61 \| 50.29 \| \| Average \| 46.30 \| 46.88 \| 45.26 \| 46.27 \| 46.23 \| 45.34 \| 47.71 \| 44.69 \| ### Table 4: Model Index 25-32 \| Task \| Model 25 \| Model 26 \| Model 27 \| Model 28 \| Model 29 \| Model 30 \| Model 31 \| Model 32 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.51 \| 33.40 \| 33.59 \| 33.52 \| 33.53 \| 33.49 \| 33.16 \| 33.56 \| \| HellaSwag \| 36.75 \| 36.97 \| 40.81 \| 38.25 \| 40.28 \| 35.71 \| 37.37 \| 37.39 \| \| PiQA \| 64.09 \| 64.74 \| 67.97 \| 66.15 \| 66.88 \| 63.84 \| 64.47 \| 65.05 \| \| OpenBookQA \| 29.47 \| 28.70 \| 29.57 \| 29.77 \| 29.50 \| 29.13 \| 29.47 \| 28.00 \| \| Lambada \| 26.69 \| 33.00 \| 31.60 \| 33.08 \| 31.49 \| 27.69 \| 26.99 \| 29.54 \| \| SciQ \| 80.03 \| 79.17 \| 80.12 \| 80.22 \| 81.92 \| 78.23 \| 77.42 \| 80.87 \| \| COPA \| 67.67 \| 65.50 \| 69.00 \| 65.67 \| 68.33 \| 63.33 \| 64.67 \| 67.17 \| \| RACE \| 30.05 \| 30.19 \| 30.96 \| 30.37 \| 30.08 \| 29.62 \| 30.13 \| 29.92 \| \| ARC Easy \| 47.50 \| 46.90 \| 50.26 \| 48.57 \| 50.55 \| 46.96 \| 48.77 \| 48.79 \| \| LogiQA \| 27.24 \| 25.55 \| 25.86 \| 24.37 \| 25.32 \| 25.12 \| 26.40 \| 24.30 \| \| QQP \| 49.68 \| 55.43 \| 50.94 \| 50.91 \| 51.99 \| 53.53 \| 49.53 \| 51.36 \| \| WinoGrande \| 51.68 \| 52.12 \| 51.93 \| 51.50 \| 52.32 \| 51.67 \| 52.13 \| 52.63 \| \| MultiRC \| 51.24 \| 51.91 \| 50.33 \| 52.42 \| 52.52 \| 54.04 \| 52.05 \| 53.04 \| \| Average \| 45.82 \| 46.43 \| 47.15 \| 46.52 \| 47.29 \| 45.57 \| 45.58 \| 46.28 \| ### Table 5: Model Index 33-40 \| Task \| Model 33 \| Model 34 \| Model 35 \| Model 36 \| Model 37 \| Model 38 \| Model 39 \| Model 40 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.48 \| 33.28 \| 33.35 \| 33.29 \| 33.63 \| 33.61 \| 33.21 \| 33.61 \| \| HellaSwag \| 38.00 \| 40.18 \| 43.37 \| 37.69 \| 32.96 \| 32.98 \| 37.31 \| 37.79 \| \| PiQA \| 65.30 \| 66.68 \| 69.04 \| 66.46 \| 62.25 \| 60.17 \| 65.24 \| 65.32 \| \| OpenBookQA \| 29.43 \| 30.37 \| 30.43 \| 27.63 \| 26.43 \| 26.83 \| 27.97 \| 28.70 \| \| Lambada \| 26.59 \| 31.46 \| 31.71 \| 30.21 \| 18.92 \| 20.29 \| 28.10 \| 28.58 \| \| SciQ \| 79.82 \| 80.58 \| 82.13 \| 80.83 \| 76.73 \| 77.90 \| 79.12 \| 79.60 \| \| COPA \| 64.33 \| 69.33 \| 67.00 \| 67.83 \| 61.50 \| 62.67 \| 64.67 \| 66.00 \| \| RACE \| 30.03 \| 30.16 \| 32.47 \| 30.49 \| 29.27 \| 28.12 \| 30.11 \| 30.21 \| \| ARC Easy \| 48.86 \| 49.88 \| 52.22 \| 48.32 \| 44.86 \| 45.54 \| 48.15 \| 48.86 \| \| LogiQA \| 25.91 \| 24.30 \| 23.35 \| 24.96 \| 26.19 \| 27.68 \| 25.47 \| 25.37 \| \| QQP \| 56.06 \| 56.56 \| 52.57 \| 56.70 \| 52.54 \| 48.04 \| 49.81 \| 57.12 \| \| WinoGrande \| 50.92 \| 50.97 \| 52.39 \| 52.70 \| 52.30 \| 51.68 \| 51.42 \| 52.80 \| \| MultiRC \| 53.09 \| 49.97 \| 52.18 \| 49.05 \| 53.78 \| 52.27 \| 51.45 \| 55.68 \| \| Average \| 46.29 \| 47.21 \| 47.86 \| 46.63 \| 43.95 \| 43.67 \| 45.54 \| 46.90 \| ### Table 6: Model Index 41-48 \| Task \| Model 41 \| Model 42 \| Model 43 \| Model 44 \| Model 45 \| Model 46 \| Model 47 \| Model 48 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.49 \| 33.43 \| 33.07 \| 33.28 \| 33.44 \| 33.08 \| 33.78 \| 33.17 \| \| HellaSwag \| 34.51 \| 37.59 \| 42.69 \| 37.37 \| 38.31 \| 38.30 \| 39.67 \| 41.07 \| \| PiQA \| 62.24 \| 65.58 \| 68.05 \| 66.62 \| 66.54 \| 65.52 \| 66.98 \| 67.21 \| \| OpenBookQA \| 27.10 \| 28.77 \| 28.90 \| 28.07 \| 28.07 \| 27.60 \| 31.17 \| 29.73 \| \| Lambada \| 22.78 \| 26.99 \| 31.34 \| 29.51 \| 27.87 \| 29.47 \| 30.34 \| 32.71 \| \| SciQ \| 77.78 \| 80.25 \| 79.47 \| 80.25 \| 80.70 \| 79.72 \| 81.35 \| 81.77 \| \| COPA \| 64.00 \| 66.33 \| 67.00 \| 67.00 \| 67.33 \| 68.33 \| 67.17 \| 67.67 \| \| RACE \| 28.33 \| 28.82 \| 30.78 \| 30.80 \| 30.08 \| 30.24 \| 30.24 \| 30.67 \| \| ARC Easy \| 45.48 \| 48.64 \| 51.49 \| 46.99 \| 48.79 \| 48.05 \| 49.58 \| 49.49 \| \| LogiQA \| 24.83 \| 24.96 \| 24.76 \| 23.25 \| 26.06 \| 25.55 \| 24.32 \| 24.68 \| \| QQP \| 50.27 \| 54.73 \| 53.96 \| 57.00 \| 53.73 \| 51.19 \| 57.52 \| 56.91 \| \| WinoGrande \| 51.79 \| 51.63 \| 51.32 \| 50.76 \| 53.18 \| 52.45 \| 50.72 \| 52.24 \| \| MultiRC \| 54.03 \| 53.96 \| 48.91 \| 50.74 \| 53.01 \| 50.89 \| 47.63 \| 53.84 \| \| Average \| 44.35 \| 46.28 \| 47.06 \| 46.28 \| 46.70 \| 46.18 \| 46.96 \| 47.78 \| ## Table 7: Model Index 49-56 \| Task \| Model 49 \| Model 50 \| Model 51 \| Model 52 \| Model 53 \| Model 54 \| Model 55 \| Model 56 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.53 \| 33.74 \| 33.37 \| 33.41 \| 32.96 \| 33.88 \| 33.75 \| 33.79 \| \| HellaSwag \| 39.09 \| 35.65 \| 38.68 \| 36.07 \| 37.68 \| 38.53 \| 35.40 \| 40.50 \| \| PiQA \| 66.81 \| 64.58 \| 65.68 \| 63.99 \| 65.85 \| 65.76 \| 64.51 \| 66.89 \| \| OpenBookQA \| 29.13 \| 27.57 \| 28.27 \| 29.10 \| 29.43 \| 28.73 \| 28.30 \| 29.87 \| \| Lambada \| 30.23 \| 26.19 \| 30.29 \| 30.84 \| 29.76 \| 29.03 \| 28.63 \| 30.74 \| \| SciQ \| 79.90 \| 80.83 \| 78.40 \| 80.03 \| 81.38 \| 80.92 \| 77.75 \| 82.07 \| \| COPA \| 68.17 \| 61.83 \| 67.00 \| 66.00 \| 66.17 \| 63.17 \| 66.33 \| 64.00 \| \| RACE \| 31.42 \| 29.35 \| 30.41 \| 31.08 \| 30.77 \| 29.73 \| 30.80 \| 31.42 \| \| ARC Easy \| 49.54 \| 47.71 \| 49.02 \| 47.64 \| 48.38 \| 49.36 \| 46.96 \| 51.22 \| \| LogiQA \| 24.99 \| 24.58 \| 25.32 \| 24.91 \| 25.17 \| 26.22 \| 24.63 \| 24.91 \| \| QQP \| 54.06 \| 56.48 \| 50.96 \| 56.62 \| 56.45 \| 53.86 \| 53.85 \| 53.26 \| \| WinoGrande \| 50.51 \| 50.26 \| 51.83 \| 51.33 \| 52.18 \| 51.89 \| 51.59 \| 50.50 \| \| MultiRC \| 50.25 \| 54.37 \| 50.94 \| 52.38 \| 51.21 \| 55.34 \| 54.52 \| 50.50 \| \| Average \| 46.74 \| 45.63 \| 46.17 \| 46.42 \| 46.72 \| 46.65 \| 45.92 \| 46.90 \| ## Table 8: Model Index 57-64 \| Task \| Model 57 \| Model 58 \| Model 59 \| Model 60 \| Model 61 \| Model 62 \| Model 63 \| Model 64 \| \|---------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Social IQA \| 33.24 \| 33.30 \| 33.56 \| 33.54 \| 33.42 \| 33.84 \| 33.32 \| 33.55 \| \| HellaSwag \| 41.74 \| 39.63 \| 35.36 \| 38.83 \| 38.53 \| 36.46 \| 38.80 \| 36.43 \| \| PiQA \| 68.07 \| 67.31 \| 64.44 \| 66.38 \| 66.50 \| 64.74 \| 66.54 \| 64.87 \| \| OpenBookQA \| 29.20 \| 29.50 \| 28.10 \| 27.97 \| 27.83 \| 27.37 \| 28.83 \| 27.87 \| \| Lambada \| 31.79 \| 31.11 \| 27.32 \| 30.17 \| 28.75 \| 26.22 \| 30.38 \| 26.25 \| \| SciQ \| 80.42 \| 79.83 \| 80.85 \| 79.60 \| 78.93 \| 80.05 \| 79.50 \| 78.65 \| \| COPA \| 66.17 \| 69.00 \| 64.00 \| 64.83 \| 67.00 \| 64.00 \| 66.00 \| 66.83 \| \| RACE \| 31.39 \| 29.82 \| 29.67 \| 30.08 \| 29.98 \| 29.46 \| 30.37 \| 29.19 \| \| ARC Easy \| 51.14 \| 49.24 \| 47.13 \| 47.88 \| 48.20 \| 47.09 \| 49.09 \| 46.90 \| \| LogiQA \| 25.19 \| 25.93 \| 23.68 \| 25.17 \| 25.70 \| 25.52 \| 26.50 \| 26.65 \| \| QQP \| 55.37 \| 54.46 \| 52.73 \| 53.17 \| 59.65 \| 58.15 \| 57.50 \| 55.31 \| \| WinoGrande \| 53.21 \| 51.46 \| 50.83 \| 52.16 \| 52.37 \| 51.41 \| 51.63 \| 51.85 \| \| MultiRC \| 53.58 \| 52.31 \| 52.22 \| 53.03 \| 50.41 \| 52.17 \| 52.27 \| 51.50 \| \| Average \| 47.73 \| 47.15 \| 45.38 \| 46.37 \| 46.71 \| 45.88 \| 46.98 \| 45.84 \|
itay-nakash/model_42d9b05c5c_sweep_pious-jazz-1154	itay-nakash	2024-07-01T13:21:34Z	0	null	[ "region:us" ]	null	2024-07-01T13:21:34Z	Entry not found
oleshy/ontochem_bioberti_cross_valid_1_30	oleshy	2024-07-01T13:24:14Z	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-01T13:22:11Z	--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_1_30 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. --> # ontochem_bioberti_cross_valid_1_30 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0570 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 0.0583 \| \| No log \| 2.0 \| 10 \| 0.0582 \| \| No log \| 3.0 \| 15 \| 0.0582 \| \| No log \| 4.0 \| 20 \| 0.0581 \| \| No log \| 5.0 \| 25 \| 0.0580 \| \| No log \| 6.0 \| 30 \| 0.0578 \| \| No log \| 7.0 \| 35 \| 0.0576 \| \| No log \| 8.0 \| 40 \| 0.0574 \| \| No log \| 9.0 \| 45 \| 0.0572 \| \| No log \| 10.0 \| 50 \| 0.0570 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Aditya5050/Continual_Learning	Aditya5050	2024-07-01T13:30:19Z	0	null	[ "license:unknown", "region:us" ]	null	2024-07-01T13:23:15Z	--- license: unknown --- All the Related information can be seen inside the attached Pdf File
freyza/playit	freyza	2024-07-01T13:24:44Z	0	null	[ "region:us" ]	null	2024-07-01T13:23:56Z	Entry not found
Jasaa/LLama-Text2SQL-v1	Jasaa	2024-07-02T07:52:12Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-07-01T13:25:15Z	Entry not found
PumeTu/wangchanbart-base-lora-xlsum	PumeTu	2024-07-01T13:27:57Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-07-01T13:27:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
itay-nakash/model_42d9b05c5c_sweep_silvery-silence-1155	itay-nakash	2024-07-01T13:27:48Z	0	null	[ "region:us" ]	null	2024-07-01T13:27:48Z	Entry not found

chainup244/Qwen-Qwen1.5-1.8B-1719835855

chainup244

2024-07-01T12:12:48Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T12:10:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ProElectro07/MeddBot

ProElectro07

2024-07-01T12:12:31Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:11:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

youngdicey/tta-v2-sr-d30-h1024-p5

youngdicey

2024-07-02T02:22:07Z

0

null

[ "region:us" ]

null

2024-07-01T12:15:59Z

Entry not found

habulaj/2680026441

habulaj

2024-07-01T12:16:51Z

0

null

[ "region:us" ]

null

2024-07-01T12:16:42Z

Entry not found

chainup244/google-gemma-2b-1719836218

chainup244

2024-07-01T12:19:40Z

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T12:17:00Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CristiD7/falcon7binstruct_workoutmodel_final

CristiD7

2024-07-01T14:02:16Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-07-01T12:17:11Z

Entry not found

vbafnaa/whisper-large-v3-hi

vbafnaa

2024-07-01T12:18:56Z

0

null

[ "region:us" ]

null

2024-07-01T12:18:56Z

Entry not found

ListIn360/naschain

ListIn360

2024-07-02T14:44:23Z

0

null

[ "region:us" ]

null

2024-07-01T12:19:31Z

Entry not found

diggy1/VisionForge

diggy1

2024-07-01T12:20:07Z

0

null

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

null

2024-07-01T12:20:07Z

--- license: mit ---

shiowo/fastrepo

shiowo

2024-07-01T12:21:41Z

0

null

[ "region:us" ]

null

2024-07-01T12:20:43Z

Entry not found

Wipiii/semantic-test

Wipiii

2024-07-01T12:23:15Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-07-01T12:22:18Z

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-mpnet-base-v2 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('sentence-transformers/all-mpnet-base-v2') 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 import torch.nn.functional as F #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('sentence-transformers/all-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) 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=sentence-transformers/all-mpnet-base-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 384 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |

VKapseln475/DietollKapseln552

VKapseln475

2024-07-01T12:24:53Z

0

null

[ "region:us" ]

null

2024-07-01T12:23:47Z

# Dietoll Erfahrungen Bewertungen Deutschland kaufen Höhle Der Löwen Offizieller Preis Dietoll Deutschland Bewertungen Erfahrungen Das Medikament "Dietoll" ist eine bahnbrechende Lösung in der Welt der Gewichtsabnahme. Es bietet eine Reihe von Vorteilen, die es von anderen Produkten unterscheiden: Beschleunigung des Stoffwechsels: Dietoll fördert die Fettverbrennung durch eine Erhöhung des Stoffwechsels. Appetitzügler: Das Medikament hilft, den Heißhunger zu reduzieren und somit die tägliche Kalorienaufnahme zu begrenzen. Energieboost: Dietoll steigert die Energie und hilft Ihnen, aktiv zu bleiben und sich während Ihrer Gewichtsreduktionsreise fit zu fühlen. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Dietoll Kapseln zu kaufen](https://deutschlandbuzz.de/dietoll-de)** ## Vorteile Dietol Kapseln bieten zahlreiche Vorteile für diejenigen, die schnell Gewicht verlieren möchten, insbesondere in Problemzonen wie Taille, Beine, Bauch und anderen Körperbereichen. Hier sind fünf der wichtigsten Vorteile von Dietol zur Gewichtsabnahme: Zielgerichtete Fettverbrennung: Dietol Kapseln fördern die Fettverbrennung in den Problemzonen, indem sie den Stoffwechsel ankurbeln und die Thermogenese erhöhen. Dadurch wird das Fett in diesen Bereichen effektiver verbrannt und abgebaut. Appetitkontrolle: Die natürlichen Inhaltsstoffe von Dietol helfen, das Hungergefühl zu reduzieren und den Appetit zu zügeln. Dies erleichtert das Einhalten einer ausgewogenen Ernährung und unterstützt die Gewichtsabnahme in den gewünschten Körperbereichen. Energie und Ausdauer: Durch die Verbesserung des Stoffwechsels und die Bereitstellung von Energie aus der Fettverbrennung können Dietol Kapseln dazu beitragen, dass Sie sich energiegeladener und leistungsfähiger fühlen. Dies kann Ihnen helfen, aktiver zu sein und zusätzliche Kalorien zu verbrennen, was zu einem schnelleren Gewichtsverlust in den Problemzonen führt. Entgiftung und Reinigung: Dietol Kapseln enthalten auch Inhaltsstoffe, die zur Entgiftung und Reinigung des Körpers beitragen. Dies kann dazu führen, dass überschüssige Flüssigkeiten und Toxine aus dem Körper ausgeschieden werden, was zu einer schlankeren Erscheinung und einer verbesserten Gesundheit führt. Gesunde Gewichtsabnahme: Im Gegensatz zu einigen Diätpillen, die auf chemischen Inhaltsstoffen basieren, sind Dietol Kapseln aus natürlichen Zutaten hergestellt, die eine gesunde und sichere Gewichtsabnahme fördern. Dies macht es zu einer idealen Wahl für diejenigen, die schnell und effektiv Gewicht verlieren möchten, ohne ihre Gesundheit zu beeinträchtigen. Durch die Einnahme von Dietol Kapseln können Sie erwarten, dass sich Ihre Problemzonen wie Taille, Beine, Bauch und andere Körperbereiche sichtbar verkleinern. Die Kombination aus Fettverbrennung, Appetitkontrolle, Energie, Entgiftung und gesunder Gewichtsabnahme macht Dietol zu einer effektiven Lösung für eine schnellere und gezielte Gewichtsabnahme. ## Wie soll man Dietoll einnehmen Um überschüssiges Fett in Problemzonen des Körpers mit Dietol Kapseln schnell loszuwerden, sollten Sie die folgenden Schritte befolgen:1. Einnahmehäufigkeit: Nehmen Sie die Kapseln jeden Tag, morgens und abends, ein, um eine kontinuierliche Wirkung zu gewährleisten. Tagesdosis: Sie sollten insgesamt zwei Kapseln pro Tag einnehmen – eine Kapsel morgens und eine weitere Kapsel abends. Einnahmedauer: Um optimale Ergebnisse zu erzielen, ist es wichtig, Dietol für mindestens 30 Tage einzunehmen.Durch die Einhaltung dieser einfachen Schritte können Sie mit Dietol effektiv Fett abbauen und Ihre Problemzonen gezielt reduzieren. ## Kontraindikationen Obwohl Dietol Kapseln aus natürlichen Inhaltsstoffen bestehen, gibt es einige Kontraindikationen, die beachtet werden sollten: ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Dietoll Kapseln zu kaufen](https://deutschlandbuzz.de/dietoll-de)**

Nerdofdot/trial3

Nerdofdot

2024-07-01T12:23:51Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:23:49Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

xyymmm/Phi3-mini-finetune-0701

xyymmm

2024-07-01T12:25:28Z

0

null

[ "region:us" ]

null

2024-07-01T12:25:28Z

Entry not found

mmbutera/karinav2

mmbutera

2024-07-01T12:27:14Z

0

null

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

null

2024-07-01T12:26:11Z

--- license: openrail ---

Meziane/qwuestion_answering_T5_policy_qa_2

Meziane

2024-07-01T13:20:27Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]

question-answering

2024-07-01T12:26:24Z

--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: qwuestion_answering_T5_policy_qa_2 results: [] ---  # qwuestion_answering_T5_policy_qa_2 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 4 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

metriccoders/fine-tuned-gpt2

metriccoders

2024-07-01T12:27:09Z

0

null

[ "region:us" ]

null

2024-07-01T12:27:09Z

Entry not found

Gaysa/hoistudios-2000

Gaysa

2024-07-02T03:47:29Z

0

null

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

null

2024-07-01T12:27:22Z

--- license: apache-2.0 ---

greenbureau/intentsensor-01.4

greenbureau

2024-07-01T12:27:47Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:27:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

yemen2016/dfm_1_NC

yemen2016

2024-07-01T12:37:08Z

0

transformers

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

text-classification

2024-07-01T12:28:31Z

Entry not found

xbsu/LW-DETR

xbsu

2024-07-01T12:49:20Z

0

null

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

null

2024-07-01T12:28:53Z

--- license: apache-2.0 ---

Seyhqn/seansty

Seyhqn

2024-07-01T12:29:20Z

0

null

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

null

2024-07-01T12:29:20Z

--- license: apache-2.0 ---

metriccoders/fine-tuned-gpt2-colab

metriccoders

2024-07-01T12:30:49Z

0

transformers

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

text-generation

2024-07-01T12:30:31Z

Entry not found

joshnavi/comic

joshnavi

2024-07-01T12:33:41Z

0

null

[ "region:us" ]

null

2024-07-01T12:33:41Z

Entry not found

BiblioSaludGros/TOPA

BiblioSaludGros

2024-07-01T12:34:31Z

0

null

[ "region:us" ]

null

2024-07-01T12:34:31Z

Entry not found

gseven9977/klmlive

gseven9977

2024-07-01T12:44:33Z

0

null

[ "region:us" ]

null

2024-07-01T12:34:35Z

Entry not found

habulaj/2516624858

habulaj

2024-07-01T12:35:18Z

0

null

[ "region:us" ]

null

2024-07-01T12:35:16Z

Entry not found

CombsAI/jurisai-vita

CombsAI

2024-07-01T12:36:02Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:35:40Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

amaalanoosucs/rpe_llama3_1_epoch_dict_merged_4bit

amaalanoosucs

2024-07-01T12:36:16Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:36:15Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** amaalanoosucs - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

chengzhiyuan/save

chengzhiyuan

2024-07-01T12:37:12Z

0

null

[ "region:us" ]

null

2024-07-01T12:37:12Z

Entry not found

itay-nakash/model_42d9b05c5c_sweep_cosmic-spaceship-1148

itay-nakash

2024-07-01T12:37:24Z

0

null

[ "region:us" ]

null

2024-07-01T12:37:24Z

Entry not found

ClementineBleuze/roberta_prefix_cont_lr_SEP

ClementineBleuze

2024-07-01T14:01:31Z

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-07-01T12:38:06Z

--- license: mit base_model: FacebookAI/roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_prefix_cont_lr_SEP results: [] ---  # roberta_prefix_cont_lr_SEP This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1079 - F1 Weighted: 0.8821 - F1 Samples: 0.8896 - F1 Macro: 0.7834 - F1 Micro: 0.8835 - Accuracy: 0.8606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Weighted | F1 Samples | F1 Macro | F1 Micro | Accuracy | |:-------------:|:------:|:----:|:---------------:|:-----------:|:----------:|:--------:|:--------:|:--------:| | 0.2596 | 0.3381 | 500 | 0.1790 | 0.7290 | 0.7276 | 0.4432 | 0.7553 | 0.7003 | | 0.1651 | 0.6761 | 1000 | 0.1339 | 0.8058 | 0.8084 | 0.6150 | 0.8242 | 0.7855 | | 0.1371 | 1.0142 | 1500 | 0.1327 | 0.8204 | 0.8216 | 0.6813 | 0.8255 | 0.7903 | | 0.1162 | 1.3523 | 2000 | 0.1267 | 0.8233 | 0.8174 | 0.6822 | 0.8276 | 0.7896 | | 0.114 | 1.6903 | 2500 | 0.1172 | 0.8455 | 0.8546 | 0.6805 | 0.8541 | 0.8241 | | 0.1074 | 2.0284 | 3000 | 0.1130 | 0.8560 | 0.8606 | 0.7147 | 0.8604 | 0.8288 | | 0.086 | 2.3665 | 3500 | 0.1121 | 0.8626 | 0.8703 | 0.7226 | 0.8686 | 0.8430 | | 0.0841 | 2.7045 | 4000 | 0.1063 | 0.8693 | 0.8758 | 0.7233 | 0.8756 | 0.8484 | | 0.0828 | 3.0426 | 4500 | 0.1071 | 0.8659 | 0.8750 | 0.7234 | 0.8707 | 0.8471 | | 0.067 | 3.3807 | 5000 | 0.1075 | 0.8700 | 0.8773 | 0.7265 | 0.8750 | 0.8478 | | 0.0644 | 3.7187 | 5500 | 0.1090 | 0.8681 | 0.8770 | 0.7223 | 0.8723 | 0.8444 | | 0.0639 | 4.0568 | 6000 | 0.1114 | 0.8631 | 0.8657 | 0.7236 | 0.8659 | 0.8356 | | 0.0488 | 4.3949 | 6500 | 0.1079 | 0.8821 | 0.8896 | 0.7834 | 0.8835 | 0.8606 | | 0.0516 | 4.7329 | 7000 | 0.1082 | 0.8752 | 0.8836 | 0.7423 | 0.8775 | 0.8505 | | 0.0475 | 5.0710 | 7500 | 0.1082 | 0.8821 | 0.8910 | 0.7550 | 0.8849 | 0.8620 | | 0.0427 | 5.4091 | 8000 | 0.1164 | 0.8774 | 0.8835 | 0.7714 | 0.8760 | 0.8505 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

xyymmm/Phi3-mini-4k-finetune-0701

xyymmm

2024-07-01T12:38:23Z

0

null

[ "region:us" ]

null

2024-07-01T12:38:23Z

Entry not found

itsadeel/tinyllama-v2.1.0

itsadeel

2024-07-01T12:39:50Z

0

null

[ "safetensors", "license:apache-2.0", "region:us" ]

null

2024-07-01T12:39:12Z

--- license: apache-2.0 ---

onnx-community/whisper-tiny.en_timestamped

onnx-community

2024-07-02T16:20:50Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:39:37Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

onnx-community/whisper-tiny_timestamped

onnx-community

2024-07-02T16:20:53Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:40:30Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

abdullah-jokergames/bert-7b-joker-v1

abdullah-jokergames

2024-07-01T12:41:06Z

0

transformers

[ "transformers", "safetensors", "distilbert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

question-answering

2024-07-01T12:40:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

onnx-community/whisper-base.en_timestamped

onnx-community

2024-07-02T16:20:56Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:41:09Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-base.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

onnx-community/whisper-base_timestamped

onnx-community

2024-07-02T16:20:58Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:42:02Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-base with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

itay-nakash/model_42d9b05c5c_sweep_helpful-sponge-1149

itay-nakash

2024-07-01T12:42:53Z

0

null

[ "region:us" ]

null

2024-07-01T12:42:53Z

Entry not found

onnx-community/whisper-small.en_timestamped

onnx-community

2024-07-02T16:21:00Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:42:55Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-small.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

onnx-community/whisper-small_timestamped

onnx-community

2024-07-02T16:21:02Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:45:02Z

--- library_name: transformers.js --- https://huggingface.co/openai/whisper-small with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

BenjaminBruenau/mistral-rag-instruct-raft

BenjaminBruenau

2024-07-01T12:45:08Z

0

null

[ "region:us" ]

null

2024-07-01T12:45:08Z

Entry not found

hsgwktb/GPT-SoVITS_Models

hsgwktb

2024-07-01T13:35:36Z

0

null

[ "region:us" ]

null

2024-07-01T12:45:45Z

Entry not found

618AI/dictalm2-it-qa-fine-tune

618AI

2024-07-02T11:54:14Z

0

2

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "he", "base_model:dicta-il/dictalm2.0-instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T12:46:44Z

--- library_name: transformers base_model: dicta-il/dictalm2.0-instruct license: apache-2.0 language: - he pipeline_tag: text-generation --- # Model Card for Guysh1805/dictalm2-it-qa-fine-tune This is a fine-tuned version of the Dicta-IL dictalm2.0-instruct model, specifically tailored for generating question-answer pairs in Hebrew. ## Model Details ### Model Description The model, Guysh1805/dictalm2-it-qa-fine-tunetuned, is a fine-tuned version of the dictalm2.0-instruct model on a both synthetically generated datasets and already existing Q&A datasets wrapped in insturction prompts. - **Developed by:** Guy Shapira - **Model type:** Transformer-based, fine-tuned Dicta-IL dictalm2.0-instruct - **Language(s) (NLP):** Hebrew - **Finetuned from:** dicta-il/dictalm2.0-instruct ## How to Get Started with the Model To get started, load the model using the Transformers library by Hugging Face: ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer model_name = "Guysh1805/dictalm2-it-qa-fine-tune" model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ```

Gaysa/Barzhanov

Gaysa

2024-07-01T12:52:02Z

0

null

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

null

2024-07-01T12:47:06Z

--- license: apache-2.0 ---

onnx-community/distil-medium.en_timestamped

onnx-community

2024-07-02T16:21:05Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:47:08Z

--- library_name: transformers.js --- https://huggingface.co/distil-whisper/distil-medium.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

HassanSM/Model_A

HassanSM

2024-07-01T12:48:43Z

0

null

[ "region:us" ]

null

2024-07-01T12:48:43Z

Entry not found

SettiE03/finbertt

SettiE03

2024-07-01T12:48:59Z

0

null

[ "region:us" ]

null

2024-07-01T12:48:59Z

Entry not found

itay-nakash/model_42d9b05c5c_sweep_glad-glade-1150

itay-nakash

2024-07-01T12:49:09Z

0

null

[ "region:us" ]

null

2024-07-01T12:49:09Z

Entry not found

onnx-community/distil-small.en_timestamped

onnx-community

2024-07-02T16:21:08Z

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "region:us" ]

automatic-speech-recognition

2024-07-01T12:49:50Z

--- library_name: transformers.js --- https://huggingface.co/distil-whisper/distil-small.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

habulaj/335839301372

habulaj

2024-07-01T12:50:41Z

0

null

[ "region:us" ]

null

2024-07-01T12:50:37Z

Entry not found

casque/Cowgirlfrombehind-10

casque

2024-07-01T12:51:04Z

0

null

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

null

2024-07-01T12:50:40Z

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

somameeta/my_legal_llm

somameeta

2024-07-01T12:58:43Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2024-07-01T12:51:54Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

igorktech/nllb-spa-30k

igorktech

2024-07-01T12:52:31Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:52:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

olonok/phi-3-mini-4k-instruct-q8_0-gguf

olonok

2024-07-01T13:00:42Z

0

null

[ "phi-3-mini-4k-instruct-q8_0", "GGUF", "Model microsoft/phi-3-mini-4k-instruct converted to GGUF with LLAMA.cpp format int8", "license:mit", "region:us" ]

null

2024-07-01T12:53:43Z

--- license: mit tags: - phi-3-mini-4k-instruct-q8_0 - GGUF - Model microsoft/phi-3-mini-4k-instruct converted to GGUF with LLAMA.cpp format int8 ---

ShamelessAND/Customed-Tokenizer_Licensed

ShamelessAND

2024-07-01T12:54:22Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:54:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

niektchandra/learninig

niektchandra

2024-07-01T12:54:42Z

0

null

[ "region:us" ]

null

2024-07-01T12:54:42Z

Entry not found

hon9kon9ize/CantoneseLLMChat-v0.5

hon9kon9ize

2024-07-02T01:43:05Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T12:56:01Z

--- license: apache-2.0 ---

hsgwktb/GPT-SoVITS_Reference_Audio

hsgwktb

2024-07-01T13:40:36Z

0

null

[ "region:us" ]

null

2024-07-01T12:57:06Z

Entry not found

ShamelessAND/Customed-Tokenizer_UnLicensed

ShamelessAND

2024-07-01T12:59:50Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T12:59:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gokul00060/loara-LAMX22

gokul00060

2024-07-01T13:40:48Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-07-01T13:00:40Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 widget: - text: "Open browser" tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** gokul00060 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Faraz01/DemoModel_1

Faraz01

2024-07-01T13:21:35Z

0

null

[ "region:us" ]

null

2024-07-01T13:01:10Z

#Example Model Card This is my model card. --- license: mit ---

vayvahid/equ

vayvahid

2024-07-01T13:01:54Z

0

null

[ "license:bsd-3-clause", "region:us" ]

null

2024-07-01T13:01:54Z

--- license: bsd-3-clause ---

suosuo321/last_try

suosuo321

2024-07-01T21:46:21Z

0

null

[ "region:us" ]

null

2024-07-01T13:02:16Z

Invalid username or password.

Sound24/t5-small-finetuned-xsum

Sound24

2024-07-01T13:24:00Z

0

transformers

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

text2text-generation

2024-07-01T13:03:03Z

Entry not found

ranyrotbard/trained-sd3-lora

ranyrotbard

2024-07-01T13:03:12Z

0

null

[ "region:us" ]

null

2024-07-01T13:03:12Z

Entry not found

nasbot/naschain

nasbot

2024-07-01T13:05:17Z

0

null

[ "region:us" ]

null

2024-07-01T13:05:12Z

Entry not found

itay-nakash/model_42d9b05c5c_sweep_light-star-1151

itay-nakash

2024-07-01T13:05:33Z

0

null

[ "region:us" ]

null

2024-07-01T13:05:33Z

Entry not found

v1vu/Deberta_science_exam

v1vu

2024-07-01T13:05:50Z

0

null

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

null

2024-07-01T13:05:50Z

--- license: mit ---

nasbot2/naschain

nasbot2

2024-07-01T13:08:02Z

0

null

[ "region:us" ]

null

2024-07-01T13:07:59Z

Entry not found

Abdine/Medical-NER

Abdine

2024-07-01T13:10:28Z

0

null

[ "region:us" ]

null

2024-07-01T13:10:28Z

Entry not found

TestTWS/naschain

TestTWS

2024-07-01T13:10:34Z

0

null

[ "region:us" ]

null

2024-07-01T13:10:31Z

Entry not found

olonok/phi-3-mini-128k-instruct-q8_0-gguf

olonok

2024-07-01T13:21:24Z

0

null

[ "arxiv:1910.09700", "license:mit", "region:us" ]

null

2024-07-01T13:10:55Z

--- license: mit --- # Model Card for Model ID Model Microsoft Phi3 mini 128k Instructct quantized to 8bits with Llama.cpp This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description Model Microsoft Phi3 mini 128k Instructct quantized to 8bits with Llama.cpp - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

NeumannsPaule/FirstTry

NeumannsPaule

2024-07-01T13:10:59Z

0

null

[ "license:eupl-1.1", "region:us" ]

null

2024-07-01T13:10:59Z

--- license: eupl-1.1 ---

nasbot3/naschain

nasbot3

2024-07-01T22:45:02Z

0

null

[ "region:us" ]

null

2024-07-01T13:10:59Z

Entry not found

habulaj/98040185214

habulaj

2024-07-01T13:11:03Z

0

null

[ "region:us" ]

null

2024-07-01T13:11:01Z

Entry not found

habulaj/11265887480

habulaj

2024-07-01T13:12:08Z

0

null

[ "region:us" ]

null

2024-07-01T13:12:07Z

Entry not found

ikedachin/codeparrot

ikedachin

2024-07-01T13:22:04Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T13:12:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

itay-nakash/model_42d9b05c5c_sweep_easy-mountain-1152

itay-nakash

2024-07-01T13:12:50Z

0

null

[ "region:us" ]

null

2024-07-01T13:12:50Z

Entry not found

nasbot4/naschain

nasbot4

2024-07-01T22:29:32Z

0

null

[ "region:us" ]

null

2024-07-01T13:13:45Z

Entry not found

habulaj/10904388263

habulaj

2024-07-01T13:14:00Z

0

null

[ "region:us" ]

null

2024-07-01T13:13:54Z

Entry not found

oleshy/ontochem_bioberti_cross_valid_1

oleshy

2024-07-01T13:16:13Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-01T13:14:11Z

--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_1 results: [] ---  # ontochem_bioberti_cross_valid_1 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0598 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 0.0631 | | No log | 2.0 | 10 | 0.0628 | | No log | 3.0 | 15 | 0.0623 | | No log | 4.0 | 20 | 0.0617 | | No log | 5.0 | 25 | 0.0612 | | No log | 6.0 | 30 | 0.0607 | | No log | 7.0 | 35 | 0.0604 | | No log | 8.0 | 40 | 0.0601 | | No log | 9.0 | 45 | 0.0600 | | No log | 10.0 | 50 | 0.0598 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Makkoen/whisper-large-cit-synth-do015-wd0-lr5e-06-1000

Makkoen

2024-07-01T17:25:00Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "base_model:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-07-01T13:15:38Z

--- language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: ./whisper-large-cit-synth-do015-wd0-lr5e-06-1000 results: [] ---  # ./whisper-large-cit-synth-do015-wd0-lr5e-06-1000 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 1000 dataset. It achieves the following results on the evaluation set: - Loss: 0.4526 - Wer: 20.3899 ## Model description More information needed ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - 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 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.7187 | 0.8889 | 50 | 0.4062 | 24.2105 | | 0.4122 | 1.7778 | 100 | 0.3523 | 22.3782 | | 0.2917 | 2.6667 | 150 | 0.3494 | 23.5867 | | 0.2242 | 3.5556 | 200 | 0.3618 | 23.0019 | | 0.1529 | 4.4444 | 250 | 0.3770 | 22.3392 | | 0.1322 | 5.3333 | 300 | 0.3906 | 21.2476 | | 0.0987 | 6.2222 | 350 | 0.4133 | 20.9747 | | 0.0798 | 7.1111 | 400 | 0.4302 | 23.8986 | | 0.0613 | 8.0 | 450 | 0.4438 | 20.5848 | | 0.0545 | 8.8889 | 500 | 0.4526 | 20.3899 | ### Framework versions - Transformers 4.42.3 - Pytorch 1.13.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

renatoguariglia/Pietra

renatoguariglia

2024-07-01T13:16:23Z

0

null

[ "region:us" ]

null

2024-07-01T13:16:23Z

Entry not found

AI4MIRO/demo

AI4MIRO

2024-07-02T12:33:45Z

0

null

[ "en", "region:us" ]

null

2024-07-01T13:16:54Z

--- language: - en --- #  # Table of Contents - [](#-model_name) - [Table of Contents](#table-of-contents) - [1. Model Basic Information](#model-basic-information) - [2. Technical Specifications](#technical-specifications) - [3. Training Details](#training-details) - [4. Model Evaluation](#model-evaluation) - [5. Model Examination](#model-examination) # 1. Model Basic Information  - **Version:** 0 ## 1.1. Intended use - **Treatment site ICD10:** More information needed - **Treatment modality:** More information needed - **Prescription levels [Gy]:** More information needed - **Additional information:** More information needed - **Motivation for development:** More information needed - **Class:** More information needed - **Creation date:** 2024-07-02 - **Type of architecture** More information needed ## 1.2. Development and deployment - **Developed by:** Name: More information needed, Instutution: More information needed, Email: More information needed - **Funded by:** More information needed - **Shared by:** More information needed - **License:** More information needed - **Finetuned from model:** More information needed - **Related Research Paper(s):** More information needed - **Related Git Repository:** More information needed ## 1.3. How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> More information needed </details> # 2. Technical Specifications ## 2.1. Model architecture - **Total number of parameters:** 5 - **Input channels:** More information needed - **Loss function** More information needed - **Batch size** 1 - **Patch dimension** More information needed <img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/xGGUE5spRY6tar5R4VeKX.png" alt="error while loading image"> _Figure 1: Model architecture_ - **Libraries/Dependencies:** More information needed - **Hardware recommended:** More information needed - **Inference time for recommended [seconds]** 10 # 3. Training Details - **Training set size:** 10 - **Validation set size:** 10 Age distribution | Sex distribution --- | --- ![](age_distribution.png) | ![](sex_distribution.png) - **Dataset source:** More information needed - **Acquisition date** from 2024-07-02 to 2024-07-02 # 4. Model Evaluation # 5. Model Examination

nasbot6/naschain

nasbot6

2024-07-01T22:22:20Z

0

null

[ "region:us" ]

null

2024-07-01T13:17:16Z

Entry not found

Abdine/bert-base-cased

Abdine

2024-07-01T13:21:36Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-01T13:17:48Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased results: [] ---  # bert-base-cased This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2087 - Precision: 0.3464 - Recall: 0.4037 - F1: 0.3728 - Accuracy: 0.9315 ## Model description More information needed ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 88 | 0.2396 | 0.2519 | 0.2699 | 0.2606 | 0.9228 | | No log | 2.0 | 176 | 0.2117 | 0.3490 | 0.3397 | 0.3443 | 0.9315 | | No log | 3.0 | 264 | 0.2087 | 0.3464 | 0.4037 | 0.3728 | 0.9315 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0 - Datasets 2.14.7 - Tokenizers 0.19.1

oleshy/ontochem_bioberti_cross_valid_1_20

oleshy

2024-07-01T13:20:12Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-01T13:18:08Z

--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_1_20 results: [] ---  # ontochem_bioberti_cross_valid_1_20 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 0.0598 | | No log | 2.0 | 10 | 0.0598 | | No log | 3.0 | 15 | 0.0597 | | No log | 4.0 | 20 | 0.0596 | | No log | 5.0 | 25 | 0.0594 | | No log | 6.0 | 30 | 0.0593 | | No log | 7.0 | 35 | 0.0591 | | No log | 8.0 | 40 | 0.0588 | | No log | 9.0 | 45 | 0.0585 | | No log | 10.0 | 50 | 0.0583 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

itay-nakash/model_42d9b05c5c_sweep_jolly-water-1153

itay-nakash

2024-07-01T13:18:17Z

0

null

[ "region:us" ]

null

2024-07-01T13:18:17Z

Entry not found

JrX44/gemma-2b-it-fine-tune-email

JrX44

2024-07-01T13:23:07Z

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-07-01T13:18:25Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pratapaadii/SignLang_v1

pratapaadii

2024-07-01T13:25:15Z

0

null

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

null

2024-07-01T13:18:43Z

--- license: cc ---

sail/data-mixture-random-1b

sail

2024-07-03T00:50:25Z

0

null

[ "en", "dataset:sail/regmix-data", "dataset:sail/regmix-data-sample", "arxiv:2407.01492", "license:mit", "region:us" ]

null

2024-07-01T13:19:38Z

--- license: mit datasets: - sail/regmix-data - sail/regmix-data-sample language: - en --- # Models Trained with Random Mixture This is a collection of 64 language models, each with approximately 1B parameters, trained on different random mixtures of data. This project aims to validate the generalization capabilities of the RegMix approach (https://huggingface.co/papers/2407.01492) from small-scale (e.g., 1M parameters) to large-scale (e.g., 1B parameters) models. ## Key Features - **Model Size**: 64 separate models, each with ~1B parameters - **Training Data**: Random data mixtures on the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset - **Purpose**: To validate the effectiveness of RegMix on identifying high-performing data mixture ## Dataset The models were trained using the [RegMix-Data](https://huggingface.co/datasets/sail/regmix-data) dataset, which is split into different domains from The Pile dataset. ## Training Hyperparameters | Hyperparameter | Value | |:---------------|:------| | Batch Size | 1M tokens | | Learning Rate | 4e-4 | | Minimum Learning Rate | 1e-5 | | Learning Rate Schedule | Cosine | | Warmup Ratio | 4% | | Total Tokens | 25B | ## How to Load a Model You can load any model using the corresponding branch with the Hugging Face Transformers library: ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1") tokenizer = AutoTokenizer.from_pretrained("sail/data-mixture-random-1b", revision="model-index-1") ``` ## Data Mixture The specific data mixture used for training each 1B model can be found in the file `train_config.yaml` in each corresponding model branch. ## Model Variants To access different model variants, simply change the `revision` parameter in the `from_pretrained` method to the desired model index (e.g., "model-index-2", "model-index-3"), and the maxium index is 64. ## Usage Notes - These models are primarily intended for research purposes. - Performance may vary depending on the specific task and domain. ## Citation If you use these models in your research, please cite the RegMix paper: ``` @misc{liu2024regmix, title={RegMix: Data Mixture as Regression for Language Model Pre-training}, author={Qian Liu and Xiaosen Zheng and Niklas Muennighoff and Guangtao Zeng and Longxu Dou and Tianyu Pang and Jing Jiang and Min Lin}, year={2024}, eprint={2407.01492}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.01492}, } ``` For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492). ## Performance We evaluated each model using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The performance metric for each task is the average of 0-shot to 5-shot `accnorm` (accuracy normalized, if available) or `acc` (accuracy) scores. ### Table 1: Model Index 1-8 | Task | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |---------------|---------|---------|---------|---------|---------|---------|---------|---------| | Social IQA | 33.27 | 33.33 | 33.62 | 33.53 | 33.49 | 33.56 | 33.62 | 33.55 | | HellaSwag | 40.58 | 36.86 | 40.58 | 36.06 | 40.07 | 37.85 | 37.93 | 39.59 | | PiQA | 67.29 | 65.14 | 67.97 | 64.66 | 67.03 | 65.36 | 66.00 | 66.55 | | OpenBookQA | 28.63 | 27.87 | 29.33 | 29.10 | 29.23 | 28.33 | 29.13 | 28.73 | | Lambada | 29.17 | 26.86 | 31.55 | 27.11 | 29.16 | 28.92 | 31.53 | 30.92 | | SciQ | 80.68 | 79.98 | 81.05 | 80.80 | 82.40 | 79.88 | 78.67 | 79.70 | | COPA | 70.50 | 63.83 | 69.17 | 65.00 | 67.50 | 66.00 | 66.67 | 68.67 | | RACE | 29.47 | 30.00 | 32.11 | 28.82 | 31.13 | 30.06 | 29.90 | 30.75 | | ARC Easy | 50.03 | 48.72 | 50.01 | 46.64 | 51.06 | 47.46 | 46.75 | 48.39 | | LogiQA | 23.76 | 24.17 | 25.29 | 25.29 | 24.55 | 25.96 | 25.45 | 26.32 | | QQP | 55.71 | 55.90 | 54.84 | 56.52 | 54.01 | 56.34 | 52.35 | 54.20 | | WinoGrande | 51.54 | 51.59 | 51.39 | 50.91 | 53.13 | 52.26 | 51.26 | 51.45 | | MultiRC | 52.65 | 53.39 | 51.89 | 50.92 | 49.03 | 53.09 | 53.64 | 50.23 | | **Average** | **47.18** | **45.97** | **47.60** | **45.80** | **47.06** | **46.54** | **46.38** | **46.85** | ### Table 2: Model Index 9-16 | Task | Model 9 | Model 10 | Model 11 | Model 12 | Model 13 | Model 14 | Model 15 | Model 16 | |---------------|---------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.43 | 33.21 | 33.31 | 33.17 | 33.28 | 32.43 | 33.57 | 33.70 | | HellaSwag | 40.05 | 35.89 | 39.55 | 39.89 | 38.63 | 36.18 | 39.52 | 35.94 | | PiQA | 66.60 | 64.74 | 66.29 | 66.27 | 66.90 | 64.05 | 66.70 | 64.51 | | OpenBookQA | 28.87 | 26.60 | 29.33 | 28.73 | 29.40 | 27.87 | 29.67 | 27.83 | | Lambada | 31.39 | 27.37 | 30.32 | 30.31 | 31.38 | 26.25 | 29.86 | 26.95 | | SciQ | 81.10 | 79.12 | 79.97 | 82.85 | 79.42 | 81.40 | 81.38 | 81.23 | | COPA | 67.00 | 64.50 | 66.83 | 69.50 | 67.33 | 65.83 | 69.50 | 66.33 | | RACE | 30.57 | 29.63 | 30.49 | 30.85 | 30.35 | 28.66 | 31.21 | 29.57 | | ARC Easy | 50.66 | 47.74 | 47.47 | 50.18 | 49.92 | 49.52 | 50.73 | 48.65 | | LogiQA | 23.60 | 25.65 | 26.37 | 23.81 | 25.58 | 26.29 | 25.86 | 25.12 | | QQP | 54.89 | 54.79 | 54.20 | 55.23 | 53.69 | 57.09 | 53.95 | 54.24 | | WinoGrande | 50.83 | 51.84 | 51.05 | 51.83 | 52.12 | 52.00 | 51.01 | 51.82 | | MultiRC | 54.18 | 54.48 | 50.17 | 52.12 | 51.42 | 52.69 | 51.87 | 53.48 | | **Average** | **47.17** | **45.81** | **46.57** | **47.29** | **46.88** | **46.17** | **47.30** | **46.11** | ### Table 3: Model Index 17-24 | Task | Model 17 | Model 18 | Model 19 | Model 20 | Model 21 | Model 22 | Model 23 | Model 24 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.89 | 33.31 | 33.53 | 33.38 | 33.75 | 33.24 | 33.56 | 33.71 | | HellaSwag | 38.68 | 39.90 | 34.67 | 37.12 | 37.44 | 36.07 | 42.15 | 34.67 | | PiQA | 66.83 | 67.39 | 63.33 | 64.83 | 65.00 | 63.68 | 67.80 | 62.99 | | OpenBookQA | 28.13 | 30.67 | 28.03 | 29.40 | 27.67 | 27.77 | 29.37 | 25.83 | | Lambada | 28.78 | 28.56 | 24.13 | 29.41 | 27.67 | 28.03 | 33.47 | 24.04 | | SciQ | 79.60 | 78.83 | 77.42 | 78.98 | 78.95 | 78.72 | 81.83 | 79.12 | | COPA | 65.17 | 68.17 | 65.33 | 67.33 | 67.67 | 62.67 | 69.83 | 65.83 | | RACE | 28.74 | 30.03 | 29.76 | 29.49 | 30.77 | 29.76 | 31.21 | 27.91 | | ARC Easy | 48.86 | 49.42 | 47.90 | 48.30 | 47.88 | 46.68 | 50.92 | 45.24 | | LogiQA | 25.91 | 26.34 | 26.24 | 25.76 | 26.11 | 26.24 | 24.17 | 25.91 | | QQP | 53.35 | 53.18 | 50.61 | 51.49 | 54.27 | 54.99 | 52.77 | 55.19 | | WinoGrande | 52.54 | 51.17 | 52.01 | 51.09 | 52.13 | 52.03 | 52.50 | 50.28 | | MultiRC | 51.49 | 52.45 | 55.40 | 54.87 | 51.73 | 49.49 | 50.61 | 50.29 | | **Average** | **46.30** | **46.88** | **45.26** | **46.27** | **46.23** | **45.34** | **47.71** | **44.69** | ### Table 4: Model Index 25-32 | Task | Model 25 | Model 26 | Model 27 | Model 28 | Model 29 | Model 30 | Model 31 | Model 32 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.51 | 33.40 | 33.59 | 33.52 | 33.53 | 33.49 | 33.16 | 33.56 | | HellaSwag | 36.75 | 36.97 | 40.81 | 38.25 | 40.28 | 35.71 | 37.37 | 37.39 | | PiQA | 64.09 | 64.74 | 67.97 | 66.15 | 66.88 | 63.84 | 64.47 | 65.05 | | OpenBookQA | 29.47 | 28.70 | 29.57 | 29.77 | 29.50 | 29.13 | 29.47 | 28.00 | | Lambada | 26.69 | 33.00 | 31.60 | 33.08 | 31.49 | 27.69 | 26.99 | 29.54 | | SciQ | 80.03 | 79.17 | 80.12 | 80.22 | 81.92 | 78.23 | 77.42 | 80.87 | | COPA | 67.67 | 65.50 | 69.00 | 65.67 | 68.33 | 63.33 | 64.67 | 67.17 | | RACE | 30.05 | 30.19 | 30.96 | 30.37 | 30.08 | 29.62 | 30.13 | 29.92 | | ARC Easy | 47.50 | 46.90 | 50.26 | 48.57 | 50.55 | 46.96 | 48.77 | 48.79 | | LogiQA | 27.24 | 25.55 | 25.86 | 24.37 | 25.32 | 25.12 | 26.40 | 24.30 | | QQP | 49.68 | 55.43 | 50.94 | 50.91 | 51.99 | 53.53 | 49.53 | 51.36 | | WinoGrande | 51.68 | 52.12 | 51.93 | 51.50 | 52.32 | 51.67 | 52.13 | 52.63 | | MultiRC | 51.24 | 51.91 | 50.33 | 52.42 | 52.52 | 54.04 | 52.05 | 53.04 | | **Average** | **45.82** | **46.43** | **47.15** | **46.52** | **47.29** | **45.57** | **45.58** | **46.28** | ### Table 5: Model Index 33-40 | Task | Model 33 | Model 34 | Model 35 | Model 36 | Model 37 | Model 38 | Model 39 | Model 40 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.48 | 33.28 | 33.35 | 33.29 | 33.63 | 33.61 | 33.21 | 33.61 | | HellaSwag | 38.00 | 40.18 | 43.37 | 37.69 | 32.96 | 32.98 | 37.31 | 37.79 | | PiQA | 65.30 | 66.68 | 69.04 | 66.46 | 62.25 | 60.17 | 65.24 | 65.32 | | OpenBookQA | 29.43 | 30.37 | 30.43 | 27.63 | 26.43 | 26.83 | 27.97 | 28.70 | | Lambada | 26.59 | 31.46 | 31.71 | 30.21 | 18.92 | 20.29 | 28.10 | 28.58 | | SciQ | 79.82 | 80.58 | 82.13 | 80.83 | 76.73 | 77.90 | 79.12 | 79.60 | | COPA | 64.33 | 69.33 | 67.00 | 67.83 | 61.50 | 62.67 | 64.67 | 66.00 | | RACE | 30.03 | 30.16 | 32.47 | 30.49 | 29.27 | 28.12 | 30.11 | 30.21 | | ARC Easy | 48.86 | 49.88 | 52.22 | 48.32 | 44.86 | 45.54 | 48.15 | 48.86 | | LogiQA | 25.91 | 24.30 | 23.35 | 24.96 | 26.19 | 27.68 | 25.47 | 25.37 | | QQP | 56.06 | 56.56 | 52.57 | 56.70 | 52.54 | 48.04 | 49.81 | 57.12 | | WinoGrande | 50.92 | 50.97 | 52.39 | 52.70 | 52.30 | 51.68 | 51.42 | 52.80 | | MultiRC | 53.09 | 49.97 | 52.18 | 49.05 | 53.78 | 52.27 | 51.45 | 55.68 | | **Average** | **46.29** | **47.21** | **47.86** | **46.63** | **43.95** | **43.67** | **45.54** | **46.90** | ### Table 6: Model Index 41-48 | Task | Model 41 | Model 42 | Model 43 | Model 44 | Model 45 | Model 46 | Model 47 | Model 48 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.49 | 33.43 | 33.07 | 33.28 | 33.44 | 33.08 | 33.78 | 33.17 | | HellaSwag | 34.51 | 37.59 | 42.69 | 37.37 | 38.31 | 38.30 | 39.67 | 41.07 | | PiQA | 62.24 | 65.58 | 68.05 | 66.62 | 66.54 | 65.52 | 66.98 | 67.21 | | OpenBookQA | 27.10 | 28.77 | 28.90 | 28.07 | 28.07 | 27.60 | 31.17 | 29.73 | | Lambada | 22.78 | 26.99 | 31.34 | 29.51 | 27.87 | 29.47 | 30.34 | 32.71 | | SciQ | 77.78 | 80.25 | 79.47 | 80.25 | 80.70 | 79.72 | 81.35 | 81.77 | | COPA | 64.00 | 66.33 | 67.00 | 67.00 | 67.33 | 68.33 | 67.17 | 67.67 | | RACE | 28.33 | 28.82 | 30.78 | 30.80 | 30.08 | 30.24 | 30.24 | 30.67 | | ARC Easy | 45.48 | 48.64 | 51.49 | 46.99 | 48.79 | 48.05 | 49.58 | 49.49 | | LogiQA | 24.83 | 24.96 | 24.76 | 23.25 | 26.06 | 25.55 | 24.32 | 24.68 | | QQP | 50.27 | 54.73 | 53.96 | 57.00 | 53.73 | 51.19 | 57.52 | 56.91 | | WinoGrande | 51.79 | 51.63 | 51.32 | 50.76 | 53.18 | 52.45 | 50.72 | 52.24 | | MultiRC | 54.03 | 53.96 | 48.91 | 50.74 | 53.01 | 50.89 | 47.63 | 53.84 | | **Average** | **44.35** | **46.28** | **47.06** | **46.28** | **46.70** | **46.18** | **46.96** | **47.78** | ## Table 7: Model Index 49-56 | Task | Model 49 | Model 50 | Model 51 | Model 52 | Model 53 | Model 54 | Model 55 | Model 56 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.53 | 33.74 | 33.37 | 33.41 | 32.96 | 33.88 | 33.75 | 33.79 | | HellaSwag | 39.09 | 35.65 | 38.68 | 36.07 | 37.68 | 38.53 | 35.40 | 40.50 | | PiQA | 66.81 | 64.58 | 65.68 | 63.99 | 65.85 | 65.76 | 64.51 | 66.89 | | OpenBookQA | 29.13 | 27.57 | 28.27 | 29.10 | 29.43 | 28.73 | 28.30 | 29.87 | | Lambada | 30.23 | 26.19 | 30.29 | 30.84 | 29.76 | 29.03 | 28.63 | 30.74 | | SciQ | 79.90 | 80.83 | 78.40 | 80.03 | 81.38 | 80.92 | 77.75 | 82.07 | | COPA | 68.17 | 61.83 | 67.00 | 66.00 | 66.17 | 63.17 | 66.33 | 64.00 | | RACE | 31.42 | 29.35 | 30.41 | 31.08 | 30.77 | 29.73 | 30.80 | 31.42 | | ARC Easy | 49.54 | 47.71 | 49.02 | 47.64 | 48.38 | 49.36 | 46.96 | 51.22 | | LogiQA | 24.99 | 24.58 | 25.32 | 24.91 | 25.17 | 26.22 | 24.63 | 24.91 | | QQP | 54.06 | 56.48 | 50.96 | 56.62 | 56.45 | 53.86 | 53.85 | 53.26 | | WinoGrande | 50.51 | 50.26 | 51.83 | 51.33 | 52.18 | 51.89 | 51.59 | 50.50 | | MultiRC | 50.25 | 54.37 | 50.94 | 52.38 | 51.21 | 55.34 | 54.52 | 50.50 | | **Average** | **46.74** | **45.63** | **46.17** | **46.42** | **46.72** | **46.65** | **45.92** | **46.90** | ## Table 8: Model Index 57-64 | Task | Model 57 | Model 58 | Model 59 | Model 60 | Model 61 | Model 62 | Model 63 | Model 64 | |---------------|----------|----------|----------|----------|----------|----------|----------|----------| | Social IQA | 33.24 | 33.30 | 33.56 | 33.54 | 33.42 | 33.84 | 33.32 | 33.55 | | HellaSwag | 41.74 | 39.63 | 35.36 | 38.83 | 38.53 | 36.46 | 38.80 | 36.43 | | PiQA | 68.07 | 67.31 | 64.44 | 66.38 | 66.50 | 64.74 | 66.54 | 64.87 | | OpenBookQA | 29.20 | 29.50 | 28.10 | 27.97 | 27.83 | 27.37 | 28.83 | 27.87 | | Lambada | 31.79 | 31.11 | 27.32 | 30.17 | 28.75 | 26.22 | 30.38 | 26.25 | | SciQ | 80.42 | 79.83 | 80.85 | 79.60 | 78.93 | 80.05 | 79.50 | 78.65 | | COPA | 66.17 | 69.00 | 64.00 | 64.83 | 67.00 | 64.00 | 66.00 | 66.83 | | RACE | 31.39 | 29.82 | 29.67 | 30.08 | 29.98 | 29.46 | 30.37 | 29.19 | | ARC Easy | 51.14 | 49.24 | 47.13 | 47.88 | 48.20 | 47.09 | 49.09 | 46.90 | | LogiQA | 25.19 | 25.93 | 23.68 | 25.17 | 25.70 | 25.52 | 26.50 | 26.65 | | QQP | 55.37 | 54.46 | 52.73 | 53.17 | 59.65 | 58.15 | 57.50 | 55.31 | | WinoGrande | 53.21 | 51.46 | 50.83 | 52.16 | 52.37 | 51.41 | 51.63 | 51.85 | | MultiRC | 53.58 | 52.31 | 52.22 | 53.03 | 50.41 | 52.17 | 52.27 | 51.50 | | **Average** | **47.73** | **47.15** | **45.38** | **46.37** | **46.71** | **45.88** | **46.98** | **45.84** |

itay-nakash/model_42d9b05c5c_sweep_pious-jazz-1154

itay-nakash

2024-07-01T13:21:34Z

0

null

[ "region:us" ]

null

2024-07-01T13:21:34Z

Entry not found

oleshy/ontochem_bioberti_cross_valid_1_30

oleshy

2024-07-01T13:24:14Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-07-01T13:22:11Z

--- tags: - generated_from_trainer model-index: - name: ontochem_bioberti_cross_valid_1_30 results: [] ---  # ontochem_bioberti_cross_valid_1_30 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0570 ## Model description More information needed ## 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: 64 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 0.0583 | | No log | 2.0 | 10 | 0.0582 | | No log | 3.0 | 15 | 0.0582 | | No log | 4.0 | 20 | 0.0581 | | No log | 5.0 | 25 | 0.0580 | | No log | 6.0 | 30 | 0.0578 | | No log | 7.0 | 35 | 0.0576 | | No log | 8.0 | 40 | 0.0574 | | No log | 9.0 | 45 | 0.0572 | | No log | 10.0 | 50 | 0.0570 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Aditya5050/Continual_Learning

Aditya5050

2024-07-01T13:30:19Z

0

null

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

null

2024-07-01T13:23:15Z

--- license: unknown --- All the Related information can be seen inside the attached Pdf File

freyza/playit

freyza

2024-07-01T13:24:44Z

0

null

[ "region:us" ]

null

2024-07-01T13:23:56Z

Entry not found

Jasaa/LLama-Text2SQL-v1

Jasaa

2024-07-02T07:52:12Z

0

transformers

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

text-generation

2024-07-01T13:25:15Z

Entry not found

PumeTu/wangchanbart-base-lora-xlsum

PumeTu

2024-07-01T13:27:57Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-07-01T13:27:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

itay-nakash/model_42d9b05c5c_sweep_silvery-silence-1155

itay-nakash

2024-07-01T13:27:48Z

0

null

[ "region:us" ]

null

2024-07-01T13:27:48Z

Entry not found