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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 18:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 18:28:34	card stringlengths 11 1.01M
guinmoon/mpt-7b-storywriter-GGUF	guinmoon	2023-10-18T08:06:39Z	315	4	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2023-10-16T15:18:31Z	[Original model](https://huggingface.co/mosaicml/mpt-7b-storywriter)
pavani8/my-pet-dog	pavani8	2023-10-18T07:54:37Z	8	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-18T07:49:06Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by pavani8 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/pavani8/my-pet-dog/resolve/main/sample_images/xzg_(2).jpg)
chaotec/yrdy	chaotec	2023-10-18T07:50:02Z	0	1	adapter-transformers	[ "adapter-transformers", "music", "finance", "text-classification", "ab", "aa", "af", "ay", "dataset:lmsys/lmsys-chat-1m", "dataset:vikp/textbook_quality_programming", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	text-classification	2023-10-18T07:45:01Z	--- license: apache-2.0 datasets: - lmsys/lmsys-chat-1m - vikp/textbook_quality_programming language: - ab - aa - af - ay metrics: - bertscore library_name: adapter-transformers pipeline_tag: text-classification tags: - music - finance --- # 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]
miittnnss/idk	miittnnss	2023-10-18T07:49:08Z	30	0	transformers	[ "transformers", "pytorch", "safetensors", "autotrain", "vision", "image-classification", "dataset:Carlangeloconcepcionrepoyo/autotrain-data-dambuhalang-pogi-scout", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	image-classification	2022-11-20T08:32:23Z	--- tags: - autotrain - vision - image-classification datasets: - Carlangeloconcepcionrepoyo/autotrain-data-dambuhalang-pogi-scout widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 1.7850904815735922 library_name: transformers --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2169069849 - CO2 Emissions (in grams): 1.7851 ## Validation Metrics - Loss: 0.026 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000
irfansk/my-pet-dog	irfansk	2023-10-18T07:48:21Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-18T07:44:07Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by irfansk following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/irfansk/my-pet-dog/resolve/main/sample_images/jak2.jpg)
sreejith8100/donut-base-sroie3	sreejith8100	2023-10-18T07:37:26Z	1	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-10-18T07:26:31Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-sroie3 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
hung200504/bert-5	hung200504	2023-10-18T07:25:41Z	14	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16", "base_model:finetune:bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16", "license:cc0-1.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T07:24:26Z	--- license: cc0-1.0 base_model: bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16 tags: - generated_from_trainer model-index: - name: bert-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-5 This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16) 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: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
joseluhf11/symptom_encoder_v8	joseluhf11	2023-10-18T07:25:11Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-10-18T07:24:37Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1968 with parameters: ``` {'batch_size': 64} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 5904, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 128, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lljllll2219/uk-mt5-base-xlsum-4000	lljllll2219	2023-10-18T07:18:30Z	64	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:xlsum", "base_model:kravchenko/uk-mt5-base", "base_model:finetune:kravchenko/uk-mt5-base", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-10-17T23:24:54Z	--- base_model: kravchenko/uk-mt5-base tags: - summarization - generated_from_trainer datasets: - xlsum metrics: - rouge model-index: - name: uk-mt5-base-xlsum-4000 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xlsum type: xlsum config: ukrainian split: validation args: ukrainian metrics: - name: Rouge1 type: rouge value: 4.2038 --- <!-- 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. --> # uk-mt5-base-xlsum-4000 This model is a fine-tuned version of [kravchenko/uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 1.7909 - Rouge1: 4.2038 - Rouge2: 0.6736 - Rougel: 4.1229 - Rougelsum: 4.1353 ## Model description More information needed ## 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: 5.6e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 2.871 \| 1.0 \| 7201 \| 1.9992 \| 3.157 \| 0.5155 \| 3.1283 \| 3.1298 \| \| 2.3902 \| 2.0 \| 14402 \| 1.9162 \| 3.6231 \| 0.595 \| 3.5878 \| 3.6125 \| \| 2.2273 \| 3.0 \| 21603 \| 1.8681 \| 3.8688 \| 0.5949 \| 3.8101 \| 3.8106 \| \| 2.1219 \| 4.0 \| 28804 \| 1.8264 \| 3.7935 \| 0.58 \| 3.741 \| 3.7647 \| \| 2.0448 \| 5.0 \| 36005 \| 1.8062 \| 3.9388 \| 0.7156 \| 3.8877 \| 3.9098 \| \| 1.9898 \| 6.0 \| 43206 \| 1.8077 \| 4.3916 \| 0.8113 \| 4.3133 \| 4.327 \| \| 1.9483 \| 7.0 \| 50407 \| 1.7935 \| 4.2474 \| 0.7119 \| 4.1732 \| 4.197 \| \| 1.9209 \| 8.0 \| 57608 \| 1.7909 \| 4.2038 \| 0.6736 \| 4.1229 \| 4.1353 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
mosuhy/llm-tolkien-llama_2_7B_local	mosuhy	2023-10-18T07:16:49Z	3	0	peft	[ "peft", "region:us" ]	null	2023-10-18T07:16:40Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0
hung200504/distilbert-4	hung200504	2023-10-18T07:15:49Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "base_model:OnePoint16/distilbert-medical-question_answer", "base_model:finetune:OnePoint16/distilbert-medical-question_answer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T07:15:34Z	--- license: apache-2.0 base_model: OnePoint16/distilbert-medical-question_answer tags: - generated_from_trainer model-index: - name: distilbert-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-4 This model is a fine-tuned version of [OnePoint16/distilbert-medical-question_answer](https://huggingface.co/OnePoint16/distilbert-medical-question_answer) 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: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Thuwanon/bert-finetuned-mrpc	Thuwanon	2023-10-18T07:12:40Z	3	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-15T07:15:40Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: bert-finetuned-mrpc results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.34.0 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.14.1
sabrinah/BERT-SQuAD	sabrinah	2023-10-18T07:01:52Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T01:14:16Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad model-index: - name: PoA 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. --> # PoA This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6105 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.2729 \| \| 2.6589 \| 2.0 \| 500 \| 1.6600 \| \| 2.6589 \| 3.0 \| 750 \| 1.6105 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cpu - Datasets 2.14.5 - Tokenizers 0.13.3
Mingfei0830/save_model	Mingfei0830	2023-10-18T06:52:04Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-18T06:00:15Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Mingfei0830/save_model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
gokuls/hBERTv2_new_pretrain_w_init_48_ver2_stsb	gokuls	2023-10-18T06:52:03Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-18T06:42:50Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: hBERTv2_new_pretrain_w_init_48_ver2_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue config: stsb split: validation args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.19761262239980293 --- <!-- 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. --> # hBERTv2_new_pretrain_w_init_48_ver2_stsb This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2194 - Pearson: 0.2187 - Spearmanr: 0.1976 - Combined Score: 0.2081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Pearson \| Spearmanr \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\|:--------------:\| \| 2.3584 \| 1.0 \| 90 \| 2.3085 \| 0.1702 \| 0.1471 \| 0.1586 \| \| 2.0513 \| 2.0 \| 180 \| 2.4060 \| 0.1479 \| 0.1342 \| 0.1411 \| \| 1.9851 \| 3.0 \| 270 \| 2.4888 \| 0.0897 \| 0.1163 \| 0.1030 \| \| 1.8287 \| 4.0 \| 360 \| 2.7571 \| 0.1643 \| 0.1827 \| 0.1735 \| \| 1.6845 \| 5.0 \| 450 \| 2.2194 \| 0.2187 \| 0.1976 \| 0.2081 \| \| 1.6892 \| 6.0 \| 540 \| 2.4431 \| 0.1882 \| 0.1858 \| 0.1870 \| \| 1.5272 \| 7.0 \| 630 \| 2.6124 \| 0.1433 \| 0.1572 \| 0.1503 \| \| 1.402 \| 8.0 \| 720 \| 2.8100 \| 0.1605 \| 0.1671 \| 0.1638 \| \| 1.3122 \| 9.0 \| 810 \| 2.7081 \| 0.1298 \| 0.1428 \| 0.1363 \| \| 1.187 \| 10.0 \| 900 \| 2.8638 \| 0.1724 \| 0.1825 \| 0.1775 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
qgyd2021/sft_llama2_stack_exchange	qgyd2021	2023-10-18T06:43:57Z	0	0	adapter-transformers	[ "adapter-transformers", "pytorch", "llama", "en", "license:apache-2.0", "region:us" ]	null	2023-10-16T08:57:05Z	--- license: apache-2.0 language: - en library_name: adapter-transformers --- I followed [this script](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/sft_llama2.py) to train this model. instead of the official [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) model, I used this repo [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf). The model trained on [lvwerra/stack-exchange-paired](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) dataset. seq_length: 1024 steps: 1600
usman7071/my-car-model	usman7071	2023-10-18T06:43:23Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-18T06:37:52Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-car-model Dreambooth model trained by usman7071 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/usman7071/my-car-model/resolve/main/sample_images/ABD_(2).jpg) ![1](https://huggingface.co/usman7071/my-car-model/resolve/main/sample_images/ABD_(1).jpg)
gokuls/hBERTv1_new_pretrain_48_ver2_qqp	gokuls	2023-10-18T06:39:59Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v1_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-18T02:10:04Z	--- language: - en base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: hBERTv1_new_pretrain_48_ver2_qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.7412317585951026 - name: F1 type: f1 value: 0.6035319084432319 --- <!-- 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. --> # hBERTv1_new_pretrain_48_ver2_qqp This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5524 - Accuracy: 0.7412 - F1: 0.6035 - Combined Score: 0.6724 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.5561 \| 1.0 \| 5686 \| 0.5524 \| 0.7412 \| 0.6035 \| 0.6724 \| \| 0.5673 \| 2.0 \| 11372 \| 0.6397 \| 0.6318 \| 0.0 \| 0.3159 \| \| 0.6117 \| 3.0 \| 17058 \| 0.6165 \| 0.6692 \| 0.4617 \| 0.5654 \| \| 0.64 \| 4.0 \| 22744 \| 0.6586 \| 0.6318 \| 0.0 \| 0.3159 \| \| 0.6592 \| 5.0 \| 28430 \| 0.6584 \| 0.6318 \| 0.0 \| 0.3159 \| \| 0.659 \| 6.0 \| 34116 \| 0.6582 \| 0.6318 \| 0.0 \| 0.3159 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
gokuls/hBERTv2_new_pretrain_w_init_48_ver2_qqp	gokuls	2023-10-18T06:39:44Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-18T02:26:35Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: hBERTv2_new_pretrain_w_init_48_ver2_qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.7573831313381153 - name: F1 type: f1 value: 0.6486622013682438 --- <!-- 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. --> # hBERTv2_new_pretrain_w_init_48_ver2_qqp This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5073 - Accuracy: 0.7574 - F1: 0.6487 - Combined Score: 0.7030 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.5438 \| 1.0 \| 5686 \| 0.5073 \| 0.7574 \| 0.6487 \| 0.7030 \| \| 0.5215 \| 2.0 \| 11372 \| 0.5411 \| 0.7379 \| 0.6475 \| 0.6927 \| \| 0.5467 \| 3.0 \| 17058 \| 0.6578 \| 0.6323 \| 0.0047 \| 0.3185 \| \| 0.5441 \| 4.0 \| 22744 \| 0.5636 \| 0.7429 \| 0.5943 \| 0.6686 \| \| 0.5524 \| 5.0 \| 28430 \| 0.5958 \| 0.7216 \| 0.5353 \| 0.6284 \| \| 0.5635 \| 6.0 \| 34116 \| 0.5578 \| 0.7358 \| 0.5946 \| 0.6652 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
hankokk/Taxi-v3	hankokk	2023-10-18T06:39:21Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-10-18T06:39:20Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="hankokk/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mosuhy/llm-tolkien-llama_2_7B	mosuhy	2023-10-18T05:58:01Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-18T05:57:53Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0
jfelgate/poca-SoccerTwos	jfelgate	2023-10-18T05:55:35Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-10-17T21:15:03Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jfelgate/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
acarp3422/AnythingsPossible	acarp3422	2023-10-18T05:47:29Z	0	0	null	[ "arxiv:1910.09700", "license:mit", "region:us" ]	null	2023-09-29T04:12:59Z	--- license: mit --- # 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]
hpandana/dqn-SpaceInvadersNoFrameskip-v4	hpandana	2023-10-18T05:45:10Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-18T05:44:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 583.00 +/- 150.87 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hpandana -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hpandana -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hpandana ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ZiaPratama/Yolov8_Pothole	ZiaPratama	2023-10-18T05:38:37Z	0	1	null	[ "object-detection", "en", "region:us" ]	object-detection	2023-10-18T05:31:10Z	--- language: - en pipeline_tag: object-detection --- This Dataset Training Model is from https://www.dropbox.com/s/qvglw8pqo16769f/pothole_dataset_v8.zip?dl=1. The Model Pre-trained used is Yolo V8. The transfered learning model detect the pot hole on the way.
hung200504/electra-finetuned-cpgqa	hung200504	2023-10-18T05:24:56Z	3	0	transformers	[ "transformers", "pytorch", "electra", "question-answering", "generated_from_trainer", "base_model:deepset/electra-base-squad2", "base_model:finetune:deepset/electra-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T05:24:37Z	--- license: cc-by-4.0 base_model: deepset/electra-base-squad2 tags: - generated_from_trainer model-index: - name: electra-finetuned-cpgqa 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. --> # electra-finetuned-cpgqa This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) 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: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
djimbe/my_awesome_billsum_model	djimbe	2023-10-18T05:20:45Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:indosum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-16T06:55:00Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - indosum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: indosum type: indosum config: indosum_fold0_source split: test args: indosum_fold0_source metrics: - name: Rouge1 type: rouge value: 0.2065 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the indosum dataset. It achieves the following results on the evaluation set: - Loss: 0.4806 - Rouge1: 0.2065 - Rouge2: 0.1639 - Rougel: 0.2038 - Rougelsum: 0.2038 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.7495 \| 1.0 \| 892 \| 0.5226 \| 0.2061 \| 0.1635 \| 0.2033 \| 0.2033 \| 19.0 \| \| 0.5326 \| 2.0 \| 1784 \| 0.4929 \| 0.2063 \| 0.1639 \| 0.2037 \| 0.2037 \| 19.0 \| \| 0.4982 \| 3.0 \| 2676 \| 0.4840 \| 0.2065 \| 0.1639 \| 0.2038 \| 0.2037 \| 19.0 \| \| 0.4958 \| 4.0 \| 3568 \| 0.4806 \| 0.2065 \| 0.1639 \| 0.2038 \| 0.2038 \| 19.0 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
gyr66/RoBERTa-finetuned-privacy-detection	gyr66	2023-10-18T05:11:55Z	25	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "zh", "dataset:gyr66/privacy_detection", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-10-16T15:25:06Z	--- language: - zh license: apache-2.0 tags: - generated_from_trainer datasets: - gyr66/privacy_detection metrics: - precision - recall - f1 - accuracy model-index: - name: RoBERTa-finetuned-privacy-detection results: - task: name: Token Classification type: token-classification dataset: name: gyr66/privacy_detection type: gyr66/privacy_detection config: privacy_detection split: train args: privacy_detection metrics: - name: Precision type: precision value: 0.6168845082494108 - name: Recall type: recall value: 0.7248237663645518 - name: F1 type: f1 value: 0.6665123278157193 - name: Accuracy type: accuracy value: 0.9061190926862569 --- <!-- 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-finetuned-privacy-detection This model is a fine-tuned version of [gyr66/RoBERTa-finetuned-privacy-detection](https://huggingface.co/gyr66/RoBERTa-finetuned-privacy-detection) on the gyr66/privacy_detection dataset. It achieves the following results on the evaluation set: - Loss: 0.3534 - Precision: 0.6169 - Recall: 0.7248 - F1: 0.6665 - Accuracy: 0.9061 ## Model description More information needed ## 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: 56 - eval_batch_size: 56 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2027 \| 1.0 \| 36 \| 0.3485 \| 0.5913 \| 0.7273 \| 0.6523 \| 0.9030 \| \| 0.1652 \| 2.0 \| 72 \| 0.3534 \| 0.6153 \| 0.7314 \| 0.6684 \| 0.9053 \| \| 0.143 \| 3.0 \| 108 \| 0.3534 \| 0.6169 \| 0.7248 \| 0.6665 \| 0.9061 \| ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.2
uukuguy/Mistral-7B-OpenOrca-lora-merged	uukuguy	2023-10-18T05:06:18Z	0	1	peft	[ "peft", "pytorch", "mistral", "Mistral", "text-generation", "en", "license:llama2", "model-index", "region:us" ]	text-generation	2023-10-16T10:21:44Z	--- language: - en library_name: peft pipeline_tag: text-generation tags: - Mistral license: llama2 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.0 verified: false --- # Mistral-7B-OpenOrca-lora-merged This is a test. This is a regenerated model that combines the base model Mistral-7B-v0.1 with the LoRA model [Mistral-7B-OpenOrca-lora](https://huggingface.co/uukuguy/Mistral-7B-OpenOrca-lora). This LoRA model is extracted from the efficient parameter fine-tuned model ([Mistral-7B-OpenOra](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)), and now it needs to be verified whether this LoRA model can achieve comparable performance with the original model. The final goal is to create a toolkit that can simultaneously load multiple LoRA modules, and automatically switch to the appropriate combination of LoRA modules based on user queries to generate the best answer. The source code is [here](https://github.com/uukuguy/multi_loras) ## Mistral-7B-OpenOrca - Extract lora model [Mistral-7B-OpenOrca-lora](https://huggingface.co/uukuguy/Mistral-7B-OpenOrca-lora) from [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca); - Merge the base model [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) with lora model to [Mistral-7B-OpenOrca-lora-merged](https://huggingface.co/uukuguy/Mistral-7B-OpenOrca-lora-merged) - LLM Evaluation ... ### Local Test \| \| ARC_acc_norm (25-shot) \| HellaSwag_acc_norm (10-shot) \| MMLU_acc (5-shot) \| TruthfulQA_mc2 (0-shot) \| GSM8K_acc (8-shot) \| Open LLM Score \| \| ------ \| ------ \| ------ \| ------ \| ------ \| ------ \| ------ \| \| Mistral-7B-OpenOrca \| 71 \| 83 \| 61.42 \| 45 \| 40 \| 65.11 \| \| r=256 \| 68 \| 84 \| 64.28 \| 46.953 \| 41 \| 65.81 \| \| r=64 \| 67 \| 84 \| 64.26 \| 47.32 \| 41 \| 65.65 \| \| r=16 \| 65 \| 83 \| 62.84 \| 46.95 \| 38 \| 64.45 \| ### Open LLM Leaderboard \| \| ARC_acc_norm (25-shot) \| HellaSwag_acc_norm (10-shot) \| MMLU_acc (5-shot) \| TruthfulQA_mc2 (0-shot) \| Open LLM Score \| \| ------ \| ------ \| ------ \| ------ \| ------ \| ------ \| \| Mistral-7B-SlimOrca \| 62.54 \| 83.86 \| 62.77 \| 54.23 \| 65.85 \| \| Mistral-7B-OpenOrca \| 64.08 \| 83.99 \| 62.24 \| 53.05 \| 65.84 \| ## lm-evaluation-harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) \| Metric \| Mistral-7B-OpenOrca \| Mistral-7B-OpenOrca-lora\| Mistral-7B-OpenOrca-lora-merged \| \| --- \| --- \|--- \| --- \| \| ARC \| 64.08 \| \| \| \| HellaSwag \| 83.99 \| \| \| \| MMLU \| 62.24 \| \| \| \| TruthfulQA \| 53.05 \| \| \| \| Average \| 65.84 \| \| \| ## HumanEval \| Metric \| Mistral-7B-OpenOrca \| Mistral-7B-OpenOrca-lora\| Mistral-7B-OpenOrca-lora-merged \| \| --- \| --- \| --- \| --- \| \| humaneval-python \| 35.976 \| \| \| ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
hung200504/bert-uncased-finetuned-cpgqa	hung200504	2023-10-18T04:57:34Z	3	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:twmkn9/bert-base-uncased-squad2", "base_model:finetune:twmkn9/bert-base-uncased-squad2", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T04:57:18Z	--- base_model: twmkn9/bert-base-uncased-squad2 tags: - generated_from_trainer model-index: - name: bert-uncased-finetuned-cpgqa 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-uncased-finetuned-cpgqa This model is a fine-tuned version of [twmkn9/bert-base-uncased-squad2](https://huggingface.co/twmkn9/bert-base-uncased-squad2) 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: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
fahdsoliman/my_awesome_qa_model	fahdsoliman	2023-10-18T04:57:26Z	4	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-16T06:54:05Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: fahdsoliman/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # fahdsoliman/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7409 - Validation Loss: 1.9577 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5728 \| 2.3969 \| 0 \| \| 2.0129 \| 1.9577 \| 1 \| \| 1.7409 \| 1.9577 \| 2 \| ### Framework versions - Transformers 4.34.0 - TensorFlow 2.12.0 - Datasets 2.14.5 - Tokenizers 0.14.1
LoneStriker/speechless-code-mistral-7b-v1.0-recalibrate-8.0bpw-h6-exl2	LoneStriker	2023-10-18T04:46:58Z	5	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "llama-2", "code", "en", "dataset:jondurbin/airoboros-2.2", "dataset:Open-Orca/OpenOrca", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:TokenBender/python_eval_instruct_51k", "license:llama2", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-18T04:42:17Z	--- language: - en library_name: transformers pipeline_tag: text-generation datasets: - jondurbin/airoboros-2.2 - Open-Orca/OpenOrca - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_V2_196k - TokenBender/python_eval_instruct_51k tags: - llama-2 - code license: llama2 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 50.0 verified: false --- <p><h1> speechless-code-mistral-7b-v1.0 </h1></p> ### NOTE: Requantized using WizardLM_evol_instruct_V2_196k for calibration * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF) Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities. Total 201,981 samples. - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples ## HumanEval \| Metric \| Value \| \| --- \| --- \| \| humaneval-python \| 50.0\| [Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) CodeLlama-34B-Python: 53.29 CodeLlama-34B-Instruct: 50.79 CodeLlama-13B-Instruct: 50.6 CodeLlama-34B: 45.11 CodeLlama-13B-Python: 42.89 CodeLlama-13B: 35.07 ## lm-evaluation-harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) \| Metric \| Value \| \| --- \| --- \| \| ARC \|59.64 \| \| HellaSwag \|82.25 \| \| MMLU \| 61.33 \| \| TruthfulQA \| 48.45 \| \| Average \| 62.92 \| ## Parameters \| \| \| \|------ \| ------ \| \| lr \| 2e-4 \| \| lr_scheduler_type \| cosine \| \| weight_decay \| 0.0 \| \| optim \| paged_adamw_8bit \| \| flash_attention \| True \| \| rerope \| False \| \| max_new_tokens \| 4096 \| \| num_train_epochs \| 2 \| \| bits \| 4 \| \| lora_r \| 64 \| \| lora_alpha \| 16 \| \| lora_dropout \| 0.05 \| \| double_quant \| True \| \| quant_type \| nf4 \| \| dataset_format \| airoboros \| \| mini_batch_size \| 2 \| \| grandient_accumulation_steps \| 32 \| \| bf16 \| True \| A40-48G x 2 \| \| \| \|------ \| ------ \| \| epoch \| 2.0 \| \| etrain_loss \| 0.5 \| \| etrain_runtime \| 1 day, 10:25:26.77 \| \| etrain_samples_per_second \| 3.194 \| \| etrain_steps_per_second \| 0.025 \| \| eeval_loss \| 0.5146 \| \| eeval_runtime \| 0:00:25.04 \| \| eeval_samples_per_second \| 7.985 \| \| eeval_steps_per_second \| \|
peteryushunli/distilbert-base-uncased-finetuned-rap-lyrics-v1	peteryushunli	2023-10-18T04:25:19Z	9	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-30T01:27:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-rap-lyrics-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-rap-lyrics-v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.977 \| 1.0 \| 1258 \| 1.9930 \| \| 1.9568 \| 2.0 \| 2516 \| 1.9718 \| \| 1.947 \| 3.0 \| 3774 \| 1.9477 \| \| 1.9445 \| 4.0 \| 5032 \| 1.9329 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
allenai/specter	allenai	2023-10-18T04:19:07Z	62,408	60	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "feature-extraction", "en", "dataset:SciDocs", "arxiv:2004.07180", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	--- language: en thumbnail: "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642e706e67" license: apache-2.0 datasets: - SciDocs metrics: - F1 - accuracy - map - ndcg --- ## SPECTER SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. If you're coming here because you want to embed papers, SPECTER has now been superceded by [SPECTER2](https://huggingface.co/allenai/specter2_proximity). Use that instead. Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf) Original Repo: [Github](https://github.com/allenai/specter) Evaluation Benchmark: [SciDocs](https://github.com/allenai/scidocs) Authors: Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld
openaccess-ai-collective/neft-exp1	openaccess-ai-collective	2023-10-18T04:16:32Z	3	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-18T03:54:48Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: out 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # out This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9422 \| 0.02 \| 1 \| 1.0091 \| \| 1.0215 \| 0.2 \| 13 \| 1.0004 \| \| 0.9933 \| 0.41 \| 26 \| 1.0071 \| \| 0.9197 \| 0.61 \| 39 \| 1.0136 \| \| 0.9285 \| 0.81 \| 52 \| 1.0075 \| \| 0.5858 \| 1.02 \| 65 \| 1.0082 \| \| 0.5522 \| 1.22 \| 78 \| 1.0546 \| \| 0.4992 \| 1.42 \| 91 \| 1.0683 \| \| 0.6085 \| 1.62 \| 104 \| 1.0638 \| \| 0.5118 \| 1.83 \| 117 \| 1.0654 \| \| 0.3243 \| 2.03 \| 130 \| 1.1113 \| \| 0.3196 \| 2.23 \| 143 \| 1.1957 \| \| 0.2582 \| 2.44 \| 156 \| 1.2038 \| \| 0.273 \| 2.64 \| 169 \| 1.1949 \| \| 0.2818 \| 2.84 \| 182 \| 1.2000 \| \| 0.1427 \| 3.05 \| 195 \| 1.2817 \| \| 0.1246 \| 3.25 \| 208 \| 1.3245 \| \| 0.1394 \| 3.45 \| 221 \| 1.3561 \| \| 0.1088 \| 3.66 \| 234 \| 1.3770 \| \| 0.0985 \| 3.86 \| 247 \| 1.3731 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.14.0
Rabul/Le	Rabul	2023-10-18T04:02:28Z	0	0	adapter-transformers	[ "adapter-transformers", "finance", "text-classification", "ae", "dataset:lmsys/lmsys-chat-1m", "license:apache-2.0", "region:us" ]	text-classification	2023-10-18T04:01:32Z	--- license: apache-2.0 datasets: - lmsys/lmsys-chat-1m language: - ae metrics: - bertscore library_name: adapter-transformers pipeline_tag: text-classification tags: - finance ---
Afishally/my_awesome_eli5_mlm_model	Afishally	2023-10-18T03:54:53Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:38:29Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9908 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2574 \| 1.0 \| 1141 \| 2.0525 \| \| 2.1639 \| 2.0 \| 2282 \| 2.0132 \| \| 2.118 \| 3.0 \| 3423 \| 1.9563 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
vulture/my_awesome_eli5_mlm_model	vulture	2023-10-18T03:53:26Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:33:53Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0183 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2346 \| 1.0 \| 1127 \| 2.1004 \| \| 2.1562 \| 2.0 \| 2254 \| 2.0598 \| \| 2.1183 \| 3.0 \| 3381 \| 2.0245 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Pssssss/my_awesome_eli5_mlm_model	Pssssss	2023-10-18T03:52:36Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:34:46Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0178 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2677 \| 1.0 \| 1132 \| 2.0885 \| \| 2.1499 \| 2.0 \| 2264 \| 2.0546 \| \| 2.1333 \| 3.0 \| 3396 \| 2.0309 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
ghzc/my_awesome_eli5_mlm_model	ghzc	2023-10-18T03:51:43Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:34:04Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9977 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2446 \| 1.0 \| 1143 \| 2.0583 \| \| 2.1637 \| 2.0 \| 2286 \| 2.0377 \| \| 2.1135 \| 3.0 \| 3429 \| 2.0078 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Cecilia0409/my_awesome_eli5_mlm_model	Cecilia0409	2023-10-18T03:51:19Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:35:04Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0031 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2236 \| 1.0 \| 1138 \| 2.0770 \| \| 2.1478 \| 2.0 \| 2276 \| 2.0293 \| \| 2.1061 \| 3.0 \| 3414 \| 2.0344 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
NYP-J/my_awesome_eli5_mlm_model	NYP-J	2023-10-18T03:50:55Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:35:48Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9711 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2456 \| 1.0 \| 1141 \| 2.0617 \| \| 2.1599 \| 2.0 \| 2282 \| 2.0269 \| \| 2.1218 \| 3.0 \| 3423 \| 1.9757 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Obanana/my_awesome_eli5_mlm_model	Obanana	2023-10-18T03:50:51Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:34:09Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0021 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2687 \| 1.0 \| 1137 \| 2.0715 \| \| 2.1714 \| 2.0 \| 2274 \| 2.0012 \| \| 2.1324 \| 3.0 \| 3411 \| 1.9764 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
PHILANDER/my_awesome_eli5_mlm_model	PHILANDER	2023-10-18T03:50:42Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:33:44Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9719 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2415 \| 1.0 \| 1146 \| 2.0722 \| \| 2.159 \| 2.0 \| 2292 \| 2.0261 \| \| 2.1127 \| 3.0 \| 3438 \| 2.0136 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Nateile/my_awesome_eli5_mlm_model	Nateile	2023-10-18T03:49:47Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-18T03:33:49Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0266 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2406 \| 1.0 \| 1117 \| 2.0649 \| \| 2.1558 \| 2.0 \| 2234 \| 2.0260 \| \| 2.1023 \| 3.0 \| 3351 \| 2.0075 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
hung200504/cpgqa	hung200504	2023-10-18T03:45:39Z	3	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T03:45:22Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-cpgqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-cpgqa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 3 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Ruri0804/Holicon	Ruri0804	2023-10-18T03:39:11Z	0	4	null	[ "stable-diffusion", "text-to-image", "safetensors", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-17T09:51:55Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image - safetensors --- # Holicon A series of models based on my personal preferences. Holicon has no actual meaning. Suffix may represent a certain artist or the abbreviation of the models used in merge. All models listed are baked in VAE. ## Tips 1. Please feel free to use whatever parameters you want. 2. If you want to get images similar to the examples images or want to know some of my usage of these models, you can refer to introduction. 3. Download the image with a suffix other than 0 to read the parameters. ## Introduction 1. Holicon-F79 Suitable for generating low saturation and more flat images, but it is weaker than other models in terms of prompt response. ![F79_0](https://huggingface.co/Ruri0804/Holicon/resolve/main/Examples/F79_0.png ) 2. Holicon-Hiten Suitable for generating my personal favorite character with slightly juvenile and whiter skin, thanks to the merging of PVC type models. ![Hiten_0](https://huggingface.co/Ruri0804/Holicon/resolve/main/Examples/Hiten_0.png ) 3. Holicon-mao This model can generate characters similar to Holicon-Hiten, but the image will be more pinkish. It has better scene generation capabilities. In terms of usage, I recommend using it only with Hires fix. (Automatic1111-stable-diffusion-webui 1.6.0+) ![mao_0](https://huggingface.co/Ruri0804/Holicon/resolve/main/Examples/mao_0.png ) Examples used on Hires fix. Nordrin_little v2.5 as the first pass and Holicon-mao as Hires fix. ![mao_first_pass_generate_0](https://huggingface.co/Ruri0804/Holicon/resolve/main/Examples/mao_first_pass_generate_0.png ) Holicon-F79 as the first pass and Holicon-mao as Hires fix. ![mao_F79_4_second_hires_fix_0](https://huggingface.co/Ruri0804/Holicon/resolve/main/Examples/mao_F79_4_second_hires_fix_0.png ) ## Recommended Settings Sampler: DPM++ SDE (30 ~ 50 steps) Hires fix Sampler: Euler a (15 ~ 40 steps) Upscaler 1: Latent (bicubic antialiased) for more details Upscaler 2: ScuNET PSNR for cleaner results Denoising: 0.5 ~ 0.6 CFG: 7 ~ 9
luhee/distilhubert-music-classifier-finetuned-gtzan	luhee	2023-10-18T03:38:16Z	6	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-10-17T17:31:23Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan model-index: - name: distilhubert-music-classifier-finetuned-gtzan 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. --> # distilhubert-music-classifier-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
BelleGroup/BELLE-on-Open-Datasets	BelleGroup	2023-10-18T03:16:37Z	0	12	null	[ "pytorch", "text2text-generation", "zh", "en", "arxiv:2304.07854", "license:gpl-3.0", "region:us" ]	text2text-generation	2023-04-17T10:16:09Z	--- license: gpl-3.0 tags: - text2text-generation pipeline_tag: text2text-generation language: - zh - en --- Considering LLaMA's license constraints, the model is for research and learning only. Please strictly respect LLaMA's usage policy. We are not allowed to publish weights for LLaMA, of course, even finetuned, but there is no problem publishing the difference, a patch that we suggest to apply to the files. The encryption is a simple XOR between files, ensuring that only the people that have access to the original weights (from completely legal sources, of course) can transform them into finetuned weights. You can find the decrypt code on https://github.com/LianjiaTech/BELLE/tree/main/models . # Model Card for Model ID ## Welcome If you find this model helpful, please like this model and star us on https://github.com/LianjiaTech/BELLE ! ## Model description We release our model described in the paper [Towards Better Instruction Following Language Models for Chinese](https://github.com/LianjiaTech/BELLE/blob/main/docs/Towards%20Better%20Instruction%20Following%20Language%20Models%20for%20Chinese.pdf) This model achieves the best performance comparing other instruction-following models with a score of 0.762 on our evaluation set. ![Experimental results](main_results.png) ## Download, Convert & Check 1. After you git clone this model ``` md5sum ./* 29db882bdab3131ef05943ee8ba82e2c ./config.json.6375ff434583e14cfc1fd45f9f599ddb9c689cb9b8c542d427dc6d5dc1059037.enc f9b33d359f17a437f6c24b4de6f2272e ./generation_config.json.fd7ff399e5568cc21a0a8414f43df88ef7c424995b9b97a90563165d2cf79efd.enc 794e28fff16ef8c3fe9e48e3aa6ccf3a ./pytorch_model-00001-of-00002.bin.b552ebc4dd499812cfe1e45ffcaad0ee93851ef83df95eb4f824be53b25e5531.enc 1ab136a4489016c3004e3f04c438f268 ./pytorch_model-00002-of-00002.bin.45adb5c7b91f81b2c03c913f2e52487a0e22663e088063b699c6a903101b7968.enc 0d6db7f247a51589f3dd6d08dbfe64ce ./pytorch_model.bin.index.json.4f08b269e18619675bc3fd62f6efb3a8d59f9d54fa50f5625d0bba7adabaf90e.enc 34696bfce7b27548cfc2410e2b55762e ./special_tokens_map.json.96bdbb8504d9967606e5f661ccc7cbbac44a3661af863a7a58614670a0ccab33.enc 6014cf2235521f974c8d9fb69b6cf07e ./tokenizer_config.json.7078cc180b3d35e7ccd06b49ede4a7fef85f2572bda40c1fe2fc8f9ab25418d3.enc 56724a79091f3d1877cca65c6412d646 ./tokenizer.model.0b716a618c9e7c45648f91d997431eba3b0ff111b17ce7b777280ed771a49f95.enc ``` 2. Decrypt the files using the scripts in https://github.com/LianjiaTech/BELLE/tree/main/models You can use the following command in Bash. Please replace "/path/to_encrypted" with the path where you stored your encrypted file, replace "/path/to_original_llama_7B" with the path where you stored your original llama7B file, and replace "/path/to_finetuned_model" with the path where you want to save your final trained model. ```bash mkdir /path/to_finetuned_model for f in "/path/to_encrypted"/; \ do if [ -f "$f" ]; then \ python3 decrypt.py "$f" "/path/to_original_llama_7B/consolidated.00.pth" "/path/to_finetuned_model/"; \ fi; \ done ``` After executing the aforementioned command, you will obtain the following files. ``` ./config.json ./generation_config.json ./pytorch_model-00001-of-00002.bin ./pytorch_model-00002-of-00002.bin ./pytorch_model.bin.index.json ./special_tokens_map.json ./tokenizer_config.json ./tokenizer.model ``` 3. Check md5sum You can verify the integrity of these files by performing an MD5 checksum to ensure their complete recovery. Here are the MD5 checksums for the relevant files: ``` md5sum ./ 139cb9dc0065bd878b277860c70add74 ./config.json 2917a1cafb895cf57e746cfd7696bfe5 ./generation_config.json 2f6cce3296b6bfeb8beb1629bf07dfe9 ./pytorch_model-00001-of-00002.bin 8fe5b4ad70788b3a6086ef28709a8730 ./pytorch_model-00002-of-00002.bin e5385004e4876ea6b93d6126e845a82f ./pytorch_model.bin.index.json 15f7a943faa91a794f38dd81a212cb01 ./special_tokens_map.json 08f6f621dba90b2a23c6f9f7af974621 ./tokenizer_config.json 6ffe559392973a92ea28032add2a8494 ./tokenizer.model ``` ## Use model Please note that the input should be formatted as follows in both training and inference. ``` python Human: {input} \n\nAssistant: ``` In order to load BELLE-LLAMA-7B-2M-enc with huggingface transformers, please install the main version, as the latest stable version doesn't support LLAMA (as of March 26, 2023). ``` python pip install git+https://github.com/huggingface/transformers ``` After you decrypt the files, BELLE-LLAMA-7B-2M can be easily loaded with LlamaForCausalLM. ``` python from transformers import LlamaForCausalLM, AutoTokenizer import torch ckpt = '/path/to_finetuned_model/' device = torch.device('cuda') model = LlamaForCausalLM.from_pretrained(ckpt, device_map='auto', low_cpu_mem_usage=True) tokenizer = AutoTokenizer.from_pretrained(ckpt) prompt = "Human: 写一首中文歌曲，赞美大自然 \n\nAssistant: " input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) generate_ids = model.generate(input_ids, max_new_tokens=300, do_sample = True, top_k = 30, top_p = 0.85, temperature = 0.5，repetition_penalty=1.2, eos_token_id=2, bos_token_id=1, pad_token_id=0) output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] response = output[len(prompt):] print(response) ``` ## Limitations There still exists a few issues in the model trained on current base model and data: 1. The model might generate factual errors when asked to follow instructions related to facts. 2. Occasionally generates harmful responses since the model still struggles to identify potential harmful instructions. 3. Needs improvements on reasoning and coding. Since the model still has its limitations, we require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed. ## Citation Please cite our paper and github when using our code, data or model. ``` @misc{ji2023better, title={Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation}, author={Yunjie Ji and Yan Gong and Yong Deng and Yiping Peng and Qiang Niu and Baochang Ma and Xiangang Li}, year={2023}, eprint={2304.07854}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{BELLE, author = {BELLEGroup}, title = {BELLE: Be Everyone's Large Language model Engine}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LianjiaTech/BELLE}}, } ```
darkmegahot/poca-SoccerTwos	darkmegahot	2023-10-18T03:12:03Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-10-18T03:11:53Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: darkmegahot/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
guocheng66/a2c-PandaReachDense-v3	guocheng66	2023-10-18T03:11:08Z	2	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-18T03:05:19Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.18 +/- 0.10 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Weni/WeniGPT-Mistral-7B-instructBase	Weni	2023-10-18T02:39:29Z	3	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-17T13:03:28Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.1 tags: - generated_from_trainer model-index: - name: WeniGPT-Mistral-7B-instructBase 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. --> # WeniGPT-Mistral-7B-instructBase This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.03 - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.13.0 - Tokenizers 0.14.1
SaiedAlshahrani/bloom_3B_8bit_qlora_flores	SaiedAlshahrani	2023-10-18T02:29:53Z	0	0	null	[ "generated_from_trainer", "base_model:asas-ai/bloom_3B_8bit", "base_model:finetune:asas-ai/bloom_3B_8bit", "region:us" ]	null	2023-10-18T01:28:26Z	--- base_model: asas-ai/bloom_3B_8bit tags: - generated_from_trainer model-index: - name: bloom_3B_8bit_qlora_flores 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. --> # bloom_3B_8bit_qlora_flores This model is a fine-tuned version of [asas-ai/bloom_3B_8bit](https://huggingface.co/asas-ai/bloom_3B_8bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2200 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.4.0 - Tokenizers 0.14.1
mangoxb/tangled3	mangoxb	2023-10-18T02:23:28Z	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-18T02:18:17Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of qzx rapunzel or vmn flynn tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - mangoxb/tangled3 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of qzx rapunzel or vmn flynn using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True.
gokuls/hBERTv1_new_pretrain_48_ver2_qnli	gokuls	2023-10-18T02:08:17Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v1_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-17T23:35:26Z	--- language: - en base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: hBERTv1_new_pretrain_48_ver2_qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue config: qnli split: validation args: qnli metrics: - name: Accuracy type: accuracy value: 0.5053999633900788 --- <!-- 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. --> # hBERTv1_new_pretrain_48_ver2_qnli This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.6982 \| 1.0 \| 1637 \| 0.6940 \| 0.5054 \| \| 0.6941 \| 2.0 \| 3274 \| 0.6932 \| 0.4946 \| \| 0.6938 \| 3.0 \| 4911 \| 0.6933 \| 0.4946 \| \| 0.6936 \| 4.0 \| 6548 \| 0.6931 \| 0.5054 \| \| 0.6934 \| 5.0 \| 8185 \| 0.6936 \| 0.4946 \| \| 0.6934 \| 6.0 \| 9822 \| 0.6936 \| 0.4946 \| \| 0.6934 \| 7.0 \| 11459 \| 0.6931 \| 0.5054 \| \| 0.6932 \| 8.0 \| 13096 \| 0.6931 \| 0.4946 \| \| 0.6932 \| 9.0 \| 14733 \| 0.6935 \| 0.5054 \| \| 0.6932 \| 10.0 \| 16370 \| 0.6932 \| 0.4946 \| \| 0.6932 \| 11.0 \| 18007 \| 0.6931 \| 0.5054 \| \| 0.6932 \| 12.0 \| 19644 \| 0.6932 \| 0.4946 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
codys12/MergeLlama-7b	codys12	2023-10-18T02:04:35Z	13	2	peft	[ "peft", "pytorch", "llama", "text-generation", "dataset:codys12/MergeLlama", "arxiv:1910.09700", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	text-generation	2023-10-11T20:49:26Z	--- library_name: peft base_model: codellama/CodeLlama-7b-hf license: llama2 datasets: - codys12/MergeLlama pipeline_tag: text-generation --- # Model Card for Model ID Automated merge conflict resolution ## Model Details Peft finetune of CodeLlama-7b ### Model Description - Developed by: DreamcatcherAI - License: llama2 - Finetuned from model [optional]: CodeLlama-7b ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: codys12/MergeLlama-7b - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses Input should be formatted as ``` <<<<<<< Current change ======= Incoming change >>>>>>> ``` MergeLlama will provide the resolution. You can chain multiple conflicts/resolutions for improved context ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
foreverip/poca-SoccerTwos	foreverip	2023-10-18T01:40:21Z	65	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-10-18T01:25:53Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: foreverip/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Tatvajsh/Lllama_AHS_V_7.1	Tatvajsh	2023-10-18T01:30:21Z	0	0	null	[ "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:finetune:openlm-research/open_llama_3b_v2", "license:apache-2.0", "region:us" ]	null	2023-10-17T22:10:06Z	--- license: apache-2.0 base_model: openlm-research/open_llama_3b_v2 tags: - generated_from_trainer model-index: - name: Lllama_AHS_V_7.1 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. --> # Lllama_AHS_V_7.1 This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-09 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
kaoriLeo/sd-class-butterflies-32	kaoriLeo	2023-10-18T01:27:38Z	1	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-10-18T01:26:01Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('kaoriLeo/sd-class-butterflies-32') image = pipeline().images[0] image ```
yesj1234/mbart-mmt_mid2_ko-en	yesj1234	2023-10-18T00:59:42Z	5	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "ko", "en", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-18T00:52:03Z	--- language: - ko - en base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer metrics: - bleu model-index: - name: ko-en_mbartLarge_mid2 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. --> # ko-en_mbartLarge_mid2 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3246 - Bleu: 22.9623 - Gen Len: 18.7197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.5377 \| 0.23 \| 2000 \| 1.6122 \| 17.2009 \| 18.7106 \| \| 1.3891 \| 0.46 \| 4000 \| 1.5059 \| 19.3345 \| 18.7688 \| \| 1.2812 \| 0.7 \| 6000 \| 1.4348 \| 20.6032 \| 18.9022 \| \| 1.2374 \| 0.93 \| 8000 \| 1.4035 \| 21.2391 \| 18.8434 \| \| 1.1734 \| 1.16 \| 10000 \| 1.4039 \| 21.304 \| 18.9964 \| \| 1.1531 \| 1.39 \| 12000 \| 1.3694 \| 21.9087 \| 18.8573 \| \| 1.1158 \| 1.62 \| 14000 \| 1.3574 \| 22.004 \| 18.5485 \| \| 1.0941 \| 1.86 \| 16000 \| 1.3457 \| 21.9785 \| 18.7119 \| \| 0.9809 \| 2.09 \| 18000 \| 1.3495 \| 22.7983 \| 18.8011 \| \| 0.9834 \| 2.32 \| 20000 \| 1.3429 \| 22.5654 \| 18.9416 \| \| 0.9981 \| 2.55 \| 22000 \| 1.3246 \| 22.9493 \| 18.7364 \| \| 1.0074 \| 2.78 \| 24000 \| 1.3539 \| 22.3874 \| 18.4428 \| \| 0.9752 \| 3.02 \| 26000 \| 1.3587 \| 22.1907 \| 18.8139 \| \| 0.8858 \| 3.25 \| 28000 \| 1.3457 \| 22.82 \| 18.8021 \| \| 0.8895 \| 3.48 \| 30000 \| 1.3603 \| 22.1575 \| 18.5638 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
teslanando/ChatterBotQA	teslanando	2023-10-18T00:57:16Z	16	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T00:56:49Z	--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: ChatterBotQA 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. --> # ChatterBotQA This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6871 ## Model description More information needed ## 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: 1.5899556108621122e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1445 \| 1.0 \| 750 \| 1.9222 \| \| 1.6222 \| 2.0 \| 1500 \| 1.6359 \| \| 1.1724 \| 3.0 \| 2250 \| 1.6205 \| \| 0.9271 \| 4.0 \| 3000 \| 1.6871 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
chargoddard/rpguild-chatml-13b	chargoddard	2023-10-18T00:41:37Z	1,416	10	transformers	[ "transformers", "pytorch", "llama", "text-generation", "roleplay", "en", "dataset:chargoddard/rpguild", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-17T23:57:26Z	--- datasets: - chargoddard/rpguild language: - en tags: - llama - roleplay license: cc-by-nc-4.0 --- It's a roleplay model! Trained on ~50k examples of human written roleplay sessions. Not a hint of gpt-flavor writing. Very creative and very able to roll with the punches. Does tend to speak for you more than it should and has a very, very strong bias towards speech in quotes. Uses ChatML prompt format, with optional length suffixes: ``` <\|im_start\|>system Enter roleplay mode. You are Steve. Steve is a nasty little man and solves all his problems by punching people in the face.<\|im_end\|> <\|im_start\|>Gundalf, Gandalf's cousin with a gun Gundalf runs frantically up to Steve and begins yelling. "We have a big problem, Steve!"<\|im_end\|> <\|im_start\|>Steve (Length: short) "What is it now Gundalf? Do you want me to punch you in the face?" Steve asked, looking down at him. He was pretty sure that this guy wasn't worth the trouble of punching him in the face, but he didn't like to leave loose ends hanging either. "There's been an incident," Gundalf said quickly, "A dwarf has stolen some gold from Smaug." "Well I don't care about any of those things," Steve responded, turning away. <\|im_end\|> ``` [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
gokuls/hBERTv2_new_pretrain_48_ver2_mrpc	gokuls	2023-10-18T00:41:19Z	4	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-18T00:26:54Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: hBERTv2_new_pretrain_48_ver2_mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.696078431372549 - name: F1 type: f1 value: 0.7832167832167833 --- <!-- 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. --> # hBERTv2_new_pretrain_48_ver2_mrpc This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5864 - Accuracy: 0.6961 - F1: 0.7832 - Combined Score: 0.7396 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.664 \| 1.0 \| 58 \| 0.6194 \| 0.6716 \| 0.7481 \| 0.7098 \| \| 0.6055 \| 2.0 \| 116 \| 0.5864 \| 0.6961 \| 0.7832 \| 0.7396 \| \| 0.5319 \| 3.0 \| 174 \| 0.6058 \| 0.6838 \| 0.7772 \| 0.7305 \| \| 0.4447 \| 4.0 \| 232 \| 0.7045 \| 0.6667 \| 0.7679 \| 0.7173 \| \| 0.3601 \| 5.0 \| 290 \| 0.7750 \| 0.6642 \| 0.7609 \| 0.7126 \| \| 0.2754 \| 6.0 \| 348 \| 1.0176 \| 0.6789 \| 0.7813 \| 0.7301 \| \| 0.1895 \| 7.0 \| 406 \| 1.4308 \| 0.6299 \| 0.7229 \| 0.6764 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
agoel3705/rl_course_vizdoom_health_gathering_supreme	agoel3705	2023-10-18T00:37:11Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-18T00:37:03Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.47 +/- 4.73 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r agoel3705/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
gokuls/hBERTv2_new_pretrain_48_ver2_cola	gokuls	2023-10-18T00:26:40Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-18T00:07:33Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation - accuracy model-index: - name: hBERTv2_new_pretrain_48_ver2_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 - name: Accuracy type: accuracy value: 0.6912751793861389 --- <!-- 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. --> # hBERTv2_new_pretrain_48_ver2_cola This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6182 - Matthews Correlation: 0.0 - Accuracy: 0.6913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\|:--------:\| \| 0.6316 \| 1.0 \| 134 \| 0.6287 \| 0.0 \| 0.6913 \| \| 0.6171 \| 2.0 \| 268 \| 0.6182 \| 0.0 \| 0.6913 \| \| 0.6141 \| 3.0 \| 402 \| 0.6182 \| 0.0 \| 0.6913 \| \| 0.613 \| 4.0 \| 536 \| 0.6184 \| 0.0 \| 0.6913 \| \| 0.6112 \| 5.0 \| 670 \| 0.6185 \| 0.0 \| 0.6913 \| \| 0.6127 \| 6.0 \| 804 \| 0.6248 \| 0.0 \| 0.6913 \| \| 0.6109 \| 7.0 \| 938 \| 0.6182 \| 0.0 \| 0.6913 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
hung200504/bert-base-cased	hung200504	2023-10-18T00:20:20Z	3	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T00:20:05Z	--- tags: - generated_from_trainer 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 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
Taeyeun72/whisper-small	Taeyeun72	2023-10-18T00:10:16Z	4	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:arrow", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-06T04:34:04Z	--- language: - ko license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - arrow metrics: - wer model-index: - name: whisper-kor3 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: whisper-kor3 type: arrow config: default split: train args: 'config: ko, split: valid' metrics: - name: Wer type: wer value: 24.690290982425815 --- <!-- 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-kor3 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the whisper-kor3 dataset. It achieves the following results on the evaluation set: - Loss: 0.4157 - Wer: 24.6903 - Cer: 11.3851 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 1.2195 \| 0.05 \| 100 \| 1.0198 \| 34.4857 \| 16.2544 \| \| 0.7295 \| 0.09 \| 200 \| 0.7220 \| 32.6995 \| 14.9684 \| \| 0.5236 \| 0.14 \| 300 \| 0.5703 \| 31.4463 \| 14.2549 \| \| 0.4976 \| 0.18 \| 400 \| 0.5461 \| 31.8640 \| 14.6274 \| \| 0.479 \| 0.23 \| 500 \| 0.5296 \| 30.4091 \| 14.0902 \| \| 0.4544 \| 0.28 \| 600 \| 0.5219 \| 31.7920 \| 16.3916 \| \| 0.4672 \| 0.32 \| 700 \| 0.5100 \| 30.4955 \| 13.9138 \| \| 0.4305 \| 0.37 \| 800 \| 0.5043 \| 30.1354 \| 14.5960 \| \| 0.4561 \| 0.42 \| 900 \| 0.4941 \| 28.8101 \| 13.2513 \| \| 0.398 \| 0.46 \| 1000 \| 0.4846 \| 31.3166 \| 14.2980 \| \| 0.4338 \| 0.51 \| 1100 \| 0.4780 \| 28.0755 \| 12.8945 \| \| 0.4121 \| 0.55 \| 1200 \| 0.4728 \| 27.4128 \| 12.5417 \| \| 0.4217 \| 0.6 \| 1300 \| 0.4693 \| 28.2772 \| 14.4392 \| \| 0.3881 \| 0.65 \| 1400 \| 0.4639 \| 27.6577 \| 13.0082 \| \| 0.4035 \| 0.69 \| 1500 \| 0.4593 \| 26.9231 \| 12.4436 \| \| 0.4146 \| 0.74 \| 1600 \| 0.4555 \| 28.4212 \| 13.7609 \| \| 0.3837 \| 0.78 \| 1700 \| 0.4511 \| 28.8822 \| 13.7845 \| \| 0.3969 \| 0.83 \| 1800 \| 0.4485 \| 29.2135 \| 14.2235 \| \| 0.4368 \| 0.88 \| 1900 \| 0.4414 \| 26.5918 \| 12.1457 \| \| 0.3679 \| 0.92 \| 2000 \| 0.4376 \| 26.4477 \| 12.1770 \| \| 0.4496 \| 0.97 \| 2100 \| 0.4335 \| 30.1354 \| 14.9018 \| \| 0.3049 \| 1.02 \| 2200 \| 0.4314 \| 26.1164 \| 12.9180 \| \| 0.2213 \| 1.06 \| 2300 \| 0.4325 \| 25.9147 \| 11.8046 \| \| 0.2732 \| 1.11 \| 2400 \| 0.4303 \| 26.0012 \| 11.8987 \| \| 0.2568 \| 1.15 \| 2500 \| 0.4293 \| 25.9291 \| 11.7576 \| \| 0.2456 \| 1.2 \| 2600 \| 0.4289 \| 25.6986 \| 11.7066 \| \| 0.2702 \| 1.25 \| 2700 \| 0.4262 \| 25.8283 \| 11.8203 \| \| 0.2744 \| 1.29 \| 2800 \| 0.4235 \| 25.8139 \| 11.8124 \| \| 0.2742 \| 1.34 \| 2900 \| 0.4254 \| 25.6266 \| 11.6360 \| \| 0.2798 \| 1.39 \| 3000 \| 0.4238 \| 25.5546 \| 11.6399 \| \| 0.2593 \| 1.43 \| 3100 \| 0.4219 \| 26.1020 \| 12.4632 \| \| 0.2619 \| 1.48 \| 3200 \| 0.4208 \| 25.3241 \| 11.4714 \| \| 0.2633 \| 1.52 \| 3300 \| 0.4210 \| 26.6350 \| 12.9964 \| \| 0.2603 \| 1.57 \| 3400 \| 0.4189 \| 25.2809 \| 11.4243 \| \| 0.2992 \| 1.62 \| 3500 \| 0.4189 \| 25.2377 \| 11.3969 \| \| 0.2453 \| 1.66 \| 3600 \| 0.4176 \| 25.2377 \| 11.5145 \| \| 0.2475 \| 1.71 \| 3700 \| 0.4172 \| 24.8487 \| 11.3969 \| \| 0.2545 \| 1.75 \| 3800 \| 0.4164 \| 25.0216 \| 11.4596 \| \| 0.272 \| 1.8 \| 3900 \| 0.4160 \| 24.6471 \| 11.2714 \| \| 0.2339 \| 1.85 \| 4000 \| 0.4157 \| 24.6903 \| 11.3851 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
gokuls/hBERTv2_new_pretrain_48_ver2_sst2	gokuls	2023-10-18T00:07:17Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-17T22:00:15Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: hBERTv2_new_pretrain_48_ver2_sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.805045871559633 --- <!-- 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. --> # hBERTv2_new_pretrain_48_ver2_sst2 This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5059 - Accuracy: 0.8050 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.375 \| 1.0 \| 1053 \| 0.5059 \| 0.8050 \| \| 0.2445 \| 2.0 \| 2106 \| 0.5165 \| 0.8028 \| \| 0.224 \| 3.0 \| 3159 \| 0.5299 \| 0.8119 \| \| 0.2046 \| 4.0 \| 4212 \| 0.5749 \| 0.8073 \| \| 0.202 \| 5.0 \| 5265 \| 0.6168 \| 0.8050 \| \| 0.2027 \| 6.0 \| 6318 \| 0.5630 \| 0.8005 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
hung200504/ditilsBert	hung200504	2023-10-18T00:05:16Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-18T00:04:57Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: ditilsBert 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. --> # ditilsBert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
alessiodm/ppo-LunarLander-v2	alessiodm	2023-10-18T00:05:02Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-17T23:07:57Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 281.32 +/- 14.86 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dtorres-zAgile/opt-zc-misti-ft	dtorres-zAgile	2023-10-17T23:57:55Z	4	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "base_model:facebook/opt-350m", "base_model:finetune:facebook/opt-350m", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-17T05:18:33Z	--- license: other base_model: facebook/opt-350m tags: - generated_from_trainer model-index: - name: opt-zc 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. --> # opt-zc This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
wofmanaf/sd-knowledge-model-lora-sdxl-ft-encoder	wofmanaf	2023-10-17T23:42:04Z	2	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-17T13:23:46Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: datasets/knowledge_captions tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - wofmanaf/sd-knowledge-model-lora-sdxl-ft-encoder These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the datasets/knowledge_captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
gokuls/hBERTv2_new_pretrain_w_init_48_ver2_cola	gokuls	2023-10-17T23:41:03Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-17T23:27:44Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation - accuracy model-index: - name: hBERTv2_new_pretrain_w_init_48_ver2_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 - name: Accuracy type: accuracy value: 0.6912751793861389 --- <!-- 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. --> # hBERTv2_new_pretrain_w_init_48_ver2_cola This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6181 - Matthews Correlation: 0.0 - Accuracy: 0.6913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\|:--------:\| \| 0.6346 \| 1.0 \| 134 \| 0.6659 \| 0.0 \| 0.6913 \| \| 0.6202 \| 2.0 \| 268 \| 0.6223 \| 0.0 \| 0.6913 \| \| 0.616 \| 3.0 \| 402 \| 0.6202 \| 0.0 \| 0.6913 \| \| 0.6128 \| 4.0 \| 536 \| 0.6181 \| 0.0 \| 0.6913 \| \| 0.6104 \| 5.0 \| 670 \| 0.6182 \| 0.0 \| 0.6913 \| \| 0.6127 \| 6.0 \| 804 \| 0.6263 \| 0.0 \| 0.6913 \| \| 0.61 \| 7.0 \| 938 \| 0.6182 \| 0.0 \| 0.6913 \| \| 0.6098 \| 8.0 \| 1072 \| 0.6181 \| 0.0 \| 0.6913 \| \| 0.611 \| 9.0 \| 1206 \| 0.6205 \| 0.0 \| 0.6913 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
asas-ai/bloom_560M_4bit_qlora_flores	asas-ai	2023-10-17T23:38:03Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:asas-ai/bloom_560M_8bit", "base_model:finetune:asas-ai/bloom_560M_8bit", "region:us" ]	null	2023-10-17T23:37:42Z	--- base_model: asas-ai/bloom_560M_8bit tags: - generated_from_trainer model-index: - name: bloom_560M_4bit_qlora_flores 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. --> # bloom_560M_4bit_qlora_flores This model is a fine-tuned version of [asas-ai/bloom_560M_8bit](https://huggingface.co/asas-ai/bloom_560M_8bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2200 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.4.0 - Tokenizers 0.14.1
SaiedAlshahrani/bloom_560M_4bit_qlora_flores	SaiedAlshahrani	2023-10-17T23:37:44Z	0	0	null	[ "generated_from_trainer", "base_model:asas-ai/bloom_560M_8bit", "base_model:finetune:asas-ai/bloom_560M_8bit", "region:us" ]	null	2023-10-17T23:07:59Z	--- base_model: asas-ai/bloom_560M_8bit tags: - generated_from_trainer model-index: - name: bloom_560M_4bit_qlora_flores 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. --> # bloom_560M_4bit_qlora_flores This model is a fine-tuned version of [asas-ai/bloom_560M_8bit](https://huggingface.co/asas-ai/bloom_560M_8bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2200 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.4.0 - Tokenizers 0.14.1
gokuls/hBERTv2_new_pretrain_w_init_48_ver2_sst2	gokuls	2023-10-17T23:27:28Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-17T22:40:02Z	--- language: - en base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: hBERTv2_new_pretrain_w_init_48_ver2_sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8119266055045872 --- <!-- 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. --> # hBERTv2_new_pretrain_w_init_48_ver2_sst2 This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4256 - Accuracy: 0.8119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3386 \| 1.0 \| 1053 \| 0.4256 \| 0.8119 \| \| 0.2249 \| 2.0 \| 2106 \| 0.6293 \| 0.8085 \| \| 0.1865 \| 3.0 \| 3159 \| 0.4738 \| 0.7982 \| \| 0.1666 \| 4.0 \| 4212 \| 0.5173 \| 0.8142 \| \| 0.1429 \| 5.0 \| 5265 \| 0.6124 \| 0.7982 \| \| 0.119 \| 6.0 \| 6318 \| 0.6314 \| 0.8062 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
gokuls/hBERTv1_new_pretrain_48_ver2_cola	gokuls	2023-10-17T23:27:20Z	5	0	transformers	[ "transformers", "pytorch", "hybridbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokuls/bert_12_layer_model_v1_complete_training_new_48", "base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_48", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-17T23:16:25Z	--- language: - en base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation - accuracy model-index: - name: hBERTv1_new_pretrain_48_ver2_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 - name: Accuracy type: accuracy value: 0.6912751793861389 --- <!-- 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. --> # hBERTv1_new_pretrain_48_ver2_cola This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6181 - Matthews Correlation: 0.0 - Accuracy: 0.6913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\|:--------:\| \| 0.6454 \| 1.0 \| 134 \| 0.6330 \| 0.0 \| 0.6913 \| \| 0.6173 \| 2.0 \| 268 \| 0.6188 \| 0.0 \| 0.6913 \| \| 0.6141 \| 3.0 \| 402 \| 0.6181 \| 0.0 \| 0.6913 \| \| 0.6147 \| 4.0 \| 536 \| 0.6181 \| 0.0 \| 0.6913 \| \| 0.6134 \| 5.0 \| 670 \| 0.6191 \| 0.0 \| 0.6913 \| \| 0.6112 \| 6.0 \| 804 \| 0.6335 \| 0.0 \| 0.6913 \| \| 0.6114 \| 7.0 \| 938 \| 0.6183 \| 0.0 \| 0.6913 \| \| 0.6095 \| 8.0 \| 1072 \| 0.6181 \| 0.0 \| 0.6913 \| \| 0.6113 \| 9.0 \| 1206 \| 0.6206 \| 0.0 \| 0.6913 \| ### Framework versions - Transformers 4.34.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.14.1
stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:23:43Z	4	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T19:15:06Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:23:41Z	7	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T17:35:44Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:23:38Z	7	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T19:01:00Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4	stefan-it	2023-10-17T23:23:37Z	4	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T18:11:22Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:23:36Z	3	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T17:21:42Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
hmbert/flair-hipe-2022-hipe2020-fr	hmbert	2023-10-17T23:23:35Z	4	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T16:31:58Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:23:33Z	6	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T18:46:58Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4	stefan-it	2023-10-17T23:23:32Z	6	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T17:57:20Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:23:31Z	3	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T17:07:37Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2	stefan-it	2023-10-17T23:23:29Z	4	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T16:17:52Z	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' élément national du radicalisme genevois , en d ' autres termes , de défendre la politique intransigeante do M . Carteret , en opposition aux tendances du groupe _ > dont le Genevois est l ' organe . Bétail . --- # Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8314][1] \| [0.8377][2] \| [0.8359][3] \| [0.8214][4] \| [0.8364][5] \| 83.26 ± 0.6 \| \| bs8-e10-lr3e-05 \| [0.83][6] \| [0.8274][7] \| [0.8358][8] \| [0.8234][9] \| [0.8327][10] \| 82.99 ± 0.43 \| \| bs8-e10-lr5e-05 \| [0.8301][11] \| [0.8321][12] \| [0.8267][13] \| [0.8266][14] \| [0.8308][15] \| 82.93 ± 0.22 \| \| bs4-e10-lr5e-05 \| [0.8181][16] \| [0.8087][17] \| [0.8239][18] \| [0.8219][19] \| [0.8224][20] \| 81.9 ± 0.55 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4	stefan-it	2023-10-17T23:23:01Z	2	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T13:44:48Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:23:00Z	8	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T13:12:52Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1	stefan-it	2023-10-17T23:22:58Z	9	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T12:09:09Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:22:57Z	3	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T14:07:42Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:22:55Z	8	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T13:03:49Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:22:51Z	9	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T13:58:40Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2	stefan-it	2023-10-17T23:22:47Z	11	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T12:22:43Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1	stefan-it	2023-10-17T23:22:46Z	11	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T11:50:36Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2	stefan-it	2023-10-17T23:22:41Z	8	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T12:15:54Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1	stefan-it	2023-10-17T23:22:40Z	2	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "de", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-13T11:43:50Z	--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7876][1] \| [0.7978][2] \| [0.7803][3] \| [0.7859][4] \| [0.7907][5] \| 78.85 ± 0.58 \| \| bs8-e10-lr3e-05 \| [0.7904][6] \| [0.7884][7] \| [0.7876][8] \| [0.783][9] \| [0.7894][10] \| 78.78 ± 0.26 \| \| bs8-e10-lr5e-05 \| [0.7939][11] \| [0.7859][12] \| [0.7825][13] \| [0.7849][14] \| [0.7853][15] \| 78.65 ± 0.39 \| \| bs4-e10-lr5e-05 \| [0.7943][16] \| [0.786][17] \| [0.7834][18] \| [0.7824][19] \| [0.7736][20] \| 78.39 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	2023-10-17T23:22:23Z	6	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-17T00:01:49Z	--- language: en license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: On Wednesday , a public dinner was given by the Conservative Burgesses of Leads , to the Conservative members of the Leeds Town Council , in the Music Hall , Albion-street , which was very numerously attended . --- # Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [TopRes19th English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md) NER Dataset using hmBERT as backbone LM. The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C. The following NEs were annotated: `BUILDING`, `LOC` and `STREET`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs8-e10-lr3e-05 \| [0.8024][1] \| [0.7936][2] \| [0.8083][3] \| [0.8042][4] \| [0.8122][5] \| 80.41 ± 0.63 \| \| bs4-e10-lr3e-05 \| [0.791][6] \| [0.8143][7] \| [0.8017][8] \| [0.8065][9] \| [0.8065][10] \| 80.4 ± 0.77 \| \| bs8-e10-lr5e-05 \| [0.7974][11] \| [0.7983][12] \| [0.8092][13] \| [0.8094][14] \| [0.7828][15] \| 79.94 ± 0.98 \| \| bs4-e10-lr5e-05 \| [0.8058][16] \| [0.7966][17] \| [0.8033][18] \| [0.7889][19] \| [0.786][20] \| 79.61 ± 0.77 \| [1]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3	stefan-it	2023-10-17T23:22:21Z	5	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-16T22:37:55Z	--- language: en license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: On Wednesday , a public dinner was given by the Conservative Burgesses of Leads , to the Conservative members of the Leeds Town Council , in the Music Hall , Albion-street , which was very numerously attended . --- # Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [TopRes19th English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md) NER Dataset using hmBERT as backbone LM. The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C. The following NEs were annotated: `BUILDING`, `LOC` and `STREET`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs8-e10-lr3e-05 \| [0.8024][1] \| [0.7936][2] \| [0.8083][3] \| [0.8042][4] \| [0.8122][5] \| 80.41 ± 0.63 \| \| bs4-e10-lr3e-05 \| [0.791][6] \| [0.8143][7] \| [0.8017][8] \| [0.8065][9] \| [0.8065][10] \| 80.4 ± 0.77 \| \| bs8-e10-lr5e-05 \| [0.7974][11] \| [0.7983][12] \| [0.8092][13] \| [0.8094][14] \| [0.7828][15] \| 79.94 ± 0.98 \| \| bs4-e10-lr5e-05 \| [0.8058][16] \| [0.7966][17] \| [0.8033][18] \| [0.7889][19] \| [0.786][20] \| 79.61 ± 0.77 \| [1]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1	stefan-it	2023-10-17T23:22:19Z	4	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "base_model:dbmdz/bert-base-historic-multilingual-cased", "base_model:finetune:dbmdz/bert-base-historic-multilingual-cased", "license:mit", "region:us" ]	token-classification	2023-10-16T21:14:05Z	--- language: en license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-cased widget: - text: On Wednesday , a public dinner was given by the Conservative Burgesses of Leads , to the Conservative members of the Leeds Town Council , in the Music Hall , Albion-street , which was very numerously attended . --- # Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [TopRes19th English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md) NER Dataset using hmBERT as backbone LM. The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C. The following NEs were annotated: `BUILDING`, `LOC` and `STREET`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs8-e10-lr3e-05 \| [0.8024][1] \| [0.7936][2] \| [0.8083][3] \| [0.8042][4] \| [0.8122][5] \| 80.41 ± 0.63 \| \| bs4-e10-lr3e-05 \| [0.791][6] \| [0.8143][7] \| [0.8017][8] \| [0.8065][9] \| [0.8065][10] \| 80.4 ± 0.77 \| \| bs8-e10-lr5e-05 \| [0.7974][11] \| [0.7983][12] \| [0.8092][13] \| [0.8094][14] \| [0.7828][15] \| 79.94 ± 0.98 \| \| bs4-e10-lr5e-05 \| [0.8058][16] \| [0.7966][17] \| [0.8033][18] \| [0.7889][19] \| [0.786][20] \| 79.61 ± 0.77 \| [1]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️

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