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zaanind/gpt2_nmt_tune
zaanind
2024-10-28T14:59:57Z
134
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T11:04:55Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Qwenslerp1-7B-i1-GGUF
mradermacher
2024-10-28T14:59:05Z
35
1
transformers
[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:allknowingroger/Qwenslerp1-7B", "base_model:quantized:allknowingroger/Qwenslerp1-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2024-10-28T13:48:38Z
--- base_model: allknowingroger/Qwenslerp1-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/allknowingroger/Qwenslerp1-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwenslerp1-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.5 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.5 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwenslerp1-7B-i1-GGUF/resolve/main/Qwenslerp1-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF
mradermacher
2024-10-28T14:58:09Z
39
0
transformers
[ "transformers", "gguf", "en", "base_model:wangrongsheng/LongWriter-llama3.1-8b-abliterated", "base_model:quantized:wangrongsheng/LongWriter-llama3.1-8b-abliterated", "endpoints_compatible", "region:us" ]
null
2024-10-28T12:46:04Z
--- base_model: wangrongsheng/LongWriter-llama3.1-8b-abliterated language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/wangrongsheng/LongWriter-llama3.1-8b-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/LongWriter-llama3.1-8b-abliterated-GGUF/resolve/main/LongWriter-llama3.1-8b-abliterated.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mav23/dolly-v2-12b-GGUF
mav23
2024-10-28T14:52:53Z
19
0
transformers
[ "transformers", "gguf", "en", "dataset:databricks/databricks-dolly-15k", "license:mit", "region:us" ]
null
2024-10-28T13:23:26Z
--- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-12b Model Card ## Summary Databricks' `dolly-v2-12b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-12b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-12b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these smaller models sizes: * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` * [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-12b` is a 12 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-12b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-12b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-12b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-12b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-12b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-12b` is not state of the art, and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, but a robust statement as to the sources of these variations requires further study. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!
g-assismoraes/deberta-large-semeval25_EN08_fold4
g-assismoraes
2024-10-28T14:52:31Z
9
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T14:37:49Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer model-index: - name: deberta-large-semeval25_EN08_fold4 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. --> # deberta-large-semeval25_EN08_fold4 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.0968 - Precision Samples: 0.1277 - Recall Samples: 0.8179 - F1 Samples: 0.2131 - Precision Macro: 0.3800 - Recall Macro: 0.7101 - F1 Macro: 0.2707 - Precision Micro: 0.1256 - Recall Micro: 0.7889 - F1 Micro: 0.2167 - Precision Weighted: 0.2111 - Recall Weighted: 0.7889 - F1 Weighted: 0.2494 ## Model description More information needed ## Intended uses & 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.3595 | 1.0 | 73 | 10.0150 | 0.1187 | 0.4154 | 0.1640 | 0.9017 | 0.3154 | 0.2532 | 0.1115 | 0.3167 | 0.1650 | 0.6711 | 0.3167 | 0.0826 | | 9.1971 | 2.0 | 146 | 9.4160 | 0.1191 | 0.6148 | 0.1778 | 0.7693 | 0.4444 | 0.2773 | 0.1049 | 0.5417 | 0.1758 | 0.4587 | 0.5417 | 0.1310 | | 7.9996 | 3.0 | 219 | 8.8114 | 0.1176 | 0.7117 | 0.1924 | 0.5851 | 0.5468 | 0.2806 | 0.1088 | 0.6667 | 0.1871 | 0.3031 | 0.6667 | 0.1706 | | 7.463 | 4.0 | 292 | 8.5503 | 0.1224 | 0.7819 | 0.1931 | 0.5197 | 0.6480 | 0.2805 | 0.1125 | 0.7472 | 0.1955 | 0.2758 | 0.7472 | 0.1944 | | 8.4991 | 5.0 | 365 | 8.3932 | 0.1203 | 0.7938 | 0.2006 | 0.4699 | 0.6469 | 0.2725 | 0.1138 | 0.7472 | 0.1976 | 0.2545 | 0.7472 | 0.2056 | | 5.8266 | 6.0 | 438 | 8.2974 | 0.1222 | 0.8157 | 0.2042 | 0.4218 | 0.6797 | 0.2494 | 0.1148 | 0.7778 | 0.2001 | 0.2412 | 0.7778 | 0.2214 | | 6.4555 | 7.0 | 511 | 8.2044 | 0.1241 | 0.7945 | 0.2076 | 0.3889 | 0.6770 | 0.2569 | 0.1224 | 0.7667 | 0.2111 | 0.2286 | 0.7667 | 0.2450 | | 6.1701 | 8.0 | 584 | 8.2297 | 0.1285 | 0.8057 | 0.2131 | 0.3902 | 0.7018 | 0.2765 | 0.1267 | 0.7722 | 0.2176 | 0.2159 | 0.7722 | 0.2478 | | 6.2618 | 9.0 | 657 | 8.1061 | 0.1281 | 0.8229 | 0.2137 | 0.3794 | 0.7040 | 0.2694 | 0.1243 | 0.7833 | 0.2145 | 0.2090 | 0.7833 | 0.2462 | | 6.6155 | 10.0 | 730 | 8.0968 | 0.1277 | 0.8179 | 0.2131 | 0.3800 | 0.7101 | 0.2707 | 0.1256 | 0.7889 | 0.2167 | 0.2111 | 0.7889 | 0.2494 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
Cloyne/vietnamese-sbert
Cloyne
2024-10-28T14:49:54Z
56
0
sentence-transformers
[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:120210", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:keepitreal/vietnamese-sbert", "base_model:finetune:keepitreal/vietnamese-sbert", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-10-28T14:49:39Z
--- base_model: keepitreal/vietnamese-sbert library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:120210 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Chủ tα»‹ch Ủy ban nhΓ’n dΓ’n xΓ£ cΓ³ quyền ra quyαΊΏt Δ‘α»‹nh cΖ°α»‘ng chαΊΏ thΓ‘o dα»‘ cΓ΄ng trΓ¬nh xΓ’y dα»±ng trΓͺn Δ‘αΊ₯t nΓ΄ng nghiệp khi chΖ°a chuyển mα»₯c Δ‘Γ­ch sα»­ dα»₯ng Δ‘αΊ₯t hay khΓ΄ng? sentences: - 'Đối tượng, Δ‘iều kiện kΓ©o dΓ i tuα»•i phα»₯c vα»₯ tαΊ‘i ngΕ© 1. Đối tượng: a) QuΓ’n nhΓ’n chuyΓͺn nghiệp cΓ³ trΓ¬nh Δ‘α»™ cao Δ‘αΊ³ng trở lΓͺn Δ‘ang Δ‘αΊ£m nhiệm cΓ‘c chα»©c danh: Kα»Ή thuαΊ­t viΓͺn, NhΓ’n viΓͺn Kα»Ή thuαΊ­t, HuαΊ₯n luyện viΓͺn, Nghệ sΔ©, NhαΊ‘c sΔ©, Diα»…n viΓͺn lΓ m việc Δ‘ΓΊng chuyΓͺn ngΓ nh Δ‘Γ o tαΊ‘o ở cΓ‘c cΖ‘ sở nghiΓͺn cα»©u, nhΓ  trường, bệnh viện, trung tΓ’m thể dα»₯c thể thao, Δ‘oΓ n nghệ thuαΊ­t, nhΓ  mΓ‘y, doanh nghiệp quα»‘c phΓ²ng; Δ‘Ζ‘n vα»‹ Δ‘Γ³ng quΓ’n ở Δ‘α»‹a bΓ n vΓΉng sΓ’u, vΓΉng xa, biΓͺn giα»›i, hαΊ£i Δ‘αΊ£o. b) QuΓ’n nhΓ’n chuyΓͺn nghiệp Δ‘ang lΓ m việc thuα»™c cΓ‘c chuyΓͺn ngΓ nh hαΊΉp được Δ‘Γ o tαΊ‘o cΓ΄ng phu hoαΊ·c chuyΓͺn ngΓ nh QuΓ’n Δ‘α»™i chΖ°a Δ‘Γ o tαΊ‘o được; thợ bαΊ­c cao. c) QuΓ’n nhΓ’n chuyΓͺn nghiệp Δ‘ang Δ‘αΊ£m nhiệm chα»©c vα»₯ chỉ huy, quαΊ£n lΓ½ ở cΓ‘c nhΓ  mΓ‘y, doanh nghiệp quα»‘c phΓ²ng. d) QuΓ’n nhΓ’n chuyΓͺn nghiệp khΓ΄ng thuα»™c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i Δ‘iểm a, Δ‘iểm b, Δ‘iểm c khoαΊ£n nΓ y do Bα»™ trưởng Bα»™ Quα»‘c phΓ²ng quyαΊΏt Δ‘α»‹nh. 2. Điều kiện: QuΓ’n nhΓ’n chuyΓͺn nghiệp thuα»™c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 1 Điều nΓ y được kΓ©o dΓ i tuα»•i phα»₯c vα»₯ tαΊ‘i ngΕ© khi cΓ³ đủ cΓ‘c Δ‘iều kiện sau: a) ĐƑn vα»‹ cΓ³ biΓͺn chαΊΏ vΓ  nhu cαΊ§u sα»­ dα»₯ng; b) HαΊΏt hαΊ‘n tuα»•i phα»₯c vα»₯ tαΊ‘i ngΕ© cao nhαΊ₯t theo cαΊ₯p bαΊ­c quΓ’n hΓ m quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 2 Điều 17 LuαΊ­t QuΓ’n nhΓ’n chuyΓͺn nghiệp, cΓ΄ng nhΓ’n vΓ  viΓͺn chα»©c quα»‘c phΓ²ng; chΖ°a cΓ³ người thay thαΊΏ; tα»± nguyện tiαΊΏp tα»₯c phα»₯c vα»₯ tαΊ‘i ngΕ©; c) CΓ³ đủ phαΊ©m chαΊ₯t chΓ­nh trα»‹, Δ‘αΊ‘o Δ‘α»©c, sα»©c khỏe để hoΓ n thΓ nh nhiệm vα»₯ được giao; d) CΓ³ trΓ¬nh Δ‘α»™ chuyΓͺn mΓ΄n kα»Ή thuαΊ­t, nghiệp vα»₯ giỏi; tay nghề cao; chαΊ₯t lượng, hiệu quαΊ£ cΓ΄ng tΓ‘c tα»‘t.' - 'Thi hΓ nh quyαΊΏt Δ‘α»‹nh cΖ°α»‘ng chαΊΏ 1. Người ra quyαΊΏt Δ‘α»‹nh cΖ°α»‘ng chαΊΏ cΓ³ trΓ‘ch nhiệm gα»­i ngay quyαΊΏt Δ‘α»‹nh cΖ°α»‘ng chαΊΏ cho cΓ‘c cΓ‘ nhΓ’n, tα»• chα»©c liΓͺn quan vΓ  tα»• chα»©c thα»±c hiện việc cΖ°α»‘ng chαΊΏ thi hΓ nh quyαΊΏt Δ‘α»‹nh xα»­ phαΊ‘t của mΓ¬nh vΓ  của cαΊ₯p dΖ°α»›i. ..."' - 'TrΓ¬nh tα»±, thủ tα»₯c Δ‘Δƒng kΓ½ tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­ Δ‘α»‘i vα»›i cΓ΄ng dΓ’n Việt Nam 1. Đăng kΓ½ tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­ mα»©c Δ‘α»™ 1 qua α»©ng dα»₯ng VNelD Δ‘α»‘i vα»›i cΓ΄ng dΓ’n Δ‘Γ£ cΓ³ thαΊ» CΔƒn cΖ°α»›c cΓ΄ng dΓ’n gαΊ―n chΓ­p Δ‘iện tα»­ a) CΓ΄ng dΓ’n sα»­ dα»₯ng thiαΊΏt bα»‹ di Δ‘α»™ng tαΊ£i vΓ  cΓ i Δ‘αΊ·t α»©ng dα»₯ng VNelD. b) CΓ΄ng dΓ’n sα»­ dα»₯ng α»©ng dα»₯ng VNelD để nhαΊ­p thΓ΄ng tin về sα»‘ Δ‘α»‹nh danh cΓ‘ nhΓ’n vΓ  sα»‘ Δ‘iện thoαΊ‘i hoαΊ·c Δ‘α»‹a chỉ thΖ° Δ‘iện tα»­; cung cαΊ₯p cΓ‘c thΓ΄ng tin theo hΖ°α»›ng dαΊ«n trΓͺn α»©ng dα»₯ng VNelD; thu nhαΊ­n αΊ£nh chΓ’n dung bαΊ±ng thiαΊΏt bα»‹ di Δ‘α»™ng vΓ  gα»­i yΓͺu cαΊ§u đề nghα»‹ cαΊ₯p tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­ tα»›i cΖ‘ quan quαΊ£n lΓ½ Δ‘α»‹nh danh vΓ  xΓ‘c thα»±c Δ‘iện tα»­ qua α»©ng dα»₯ng VNelD. c) CΖ‘ quan quαΊ£n lΓ½ Δ‘α»‹nh danh Δ‘iện tα»­ thΓ΄ng bΓ‘o kαΊΏt quαΊ£ Δ‘Δƒng kΓ½ tΓ i khoαΊ£n qua α»©ng dα»₯ng VNelD hoαΊ·c tin nhαΊ―n SMS hoαΊ·c Δ‘α»‹a chỉ thΖ° Δ‘iện tα»­. 2. Đăng kΓ½ tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­ mα»©c Δ‘α»™ 2 a) Đối vα»›i cΓ΄ng dΓ’n Δ‘Γ£ được cαΊ₯p thαΊ» CΔƒn cΖ°α»›c cΓ΄ng dΓ’n gαΊ―n chΓ­p Δ‘iện tα»­: CΓ΄ng dΓ’n Δ‘αΊΏn CΓ΄ng an xΓ£, phường, thα»‹ trαΊ₯n hoαΊ·c nΖ‘i lΓ m thủ tα»₯c cαΊ₯p thαΊ» CΔƒn cΖ°α»›c cΓ΄ng dΓ’n để lΓ m thủ tα»₯c cαΊ₯p tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­. CΓ΄ng dΓ’n xuαΊ₯t trΓ¬nh thαΊ» CΔƒn cΖ°α»›c cΓ΄ng dΓ’n gαΊ―n chΓ­p Δ‘iện tα»­, cung cαΊ₯p thΓ΄ng tin về sα»‘ Δ‘iện thoαΊ‘i hoαΊ·c Δ‘α»‹a chỉ thΖ° Δ‘iện tα»­ vΓ  đề nghα»‹ bα»• sung thΓ΄ng tin được tΓ­ch hợp vΓ o tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­. CΓ‘n bα»™ tiαΊΏp nhαΊ­n nhαΊ­p thΓ΄ng tin cΓ΄ng dΓ’n cung cαΊ₯p vΓ o hệ thα»‘ng Δ‘α»‹nh danh vΓ  xΓ‘c thα»±c Δ‘iện tα»­; chα»₯p αΊ£nh chΓ’n dung, thu nhαΊ­n vΓ’n tay của cΓ΄ng dΓ’n Δ‘αΊΏn lΓ m thủ tα»₯c để xΓ‘c thα»±c vα»›i CΖ‘ sở dα»― liệu cΔƒn cΖ°α»›c cΓ΄ng dΓ’n vΓ  khαΊ³ng Δ‘α»‹nh sα»± Δ‘α»“ng Γ½ Δ‘Δƒng kΓ½ tαΊ‘o lαΊ­p tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­. CΖ‘ quan quαΊ£n lΓ½ Δ‘α»‹nh danh Δ‘iện tα»­ thΓ΄ng bΓ‘o kαΊΏt quαΊ£ Δ‘Δƒng kΓ½ tΓ i khoαΊ£n qua α»©ng dα»₯ng VNelD hoαΊ·c tin nhαΊ―n SMS hoαΊ·c Δ‘α»‹a chỉ thΖ° Δ‘iện tα»­. b) CΖ‘ quan CΓ΄ng an tiαΊΏn hΓ nh cαΊ₯p tΓ i khoαΊ£n Δ‘α»‹nh danh Δ‘iện tα»­ mα»©c Δ‘α»™ 2 cΓΉng vα»›i cαΊ₯p thαΊ» CΔƒn cΖ°α»›c cΓ΄ng dΓ’n vα»›i trường hợp cΓ΄ng dΓ’n chΖ°a được cαΊ₯p CΔƒn cΖ°α»›c cΓ΄ng dΓ’n gαΊ―n chΓ­p Δ‘iện tα»­.' - source_sentence: Mα»©c hưởng chαΊΏ Δ‘α»™ thai sαΊ£n Δ‘α»‘i vα»›i lao Δ‘α»™ng nam lΓ  người nΖ°α»›c ngoΓ i được phΓ‘p luαΊ­t quy Δ‘α»‹nh nhΖ° thαΊΏ nΓ o? sentences: - '"Điều 21. ThΓ΄ng bΓ‘o kαΊΏt quαΊ£ vΓ  xΓ‘c nhαΊ­n nhαΊ­p học 1. CΖ‘ sở Δ‘Γ o tαΊ‘o gα»­i giαΊ₯y bΓ‘o trΓΊng tuyển cho nhα»―ng thΓ­ sinh trΓΊng tuyển, trong Δ‘Γ³ ghi rΓ΅ nhα»―ng thủ tα»₯c cαΊ§n thiαΊΏt Δ‘α»‘i vα»›i thΓ­ sinh khi nhαΊ­p học vΓ  phΖ°Ζ‘ng thα»©c nhαΊ­p học của thΓ­ sinh. 2. ThΓ­ sinh xΓ‘c nhαΊ­n nhαΊ­p học bαΊ±ng hΓ¬nh thα»©c trα»±c tuyαΊΏn trΓͺn hệ thα»‘ng, trΖ°α»›c khi nhαΊ­p học tαΊ‘i cΖ‘ sở Δ‘Γ o tαΊ‘o. 3. Đối vα»›i nhα»―ng thΓ­ sinh khΓ΄ng xΓ‘c nhαΊ­n nhαΊ­p học trong thời hαΊ‘n quy Δ‘α»‹nh: a) NαΊΏu khΓ΄ng cΓ³ lΓ½ do chΓ­nh Δ‘Γ‘ng thΓ¬ coi nhΖ° thΓ­ sinh tα»« chα»‘i nhαΊ­p học vΓ  cΖ‘ sở Δ‘Γ o tαΊ‘o cΓ³ quyền khΓ΄ng tiαΊΏp nhαΊ­n; b) NαΊΏu do α»‘m Δ‘au, tai nαΊ‘n, cΓ³ giαΊ₯y xΓ‘c nhαΊ­n của bệnh viện quαΊ­n, huyện trở lΓͺn hoαΊ·c do thiΓͺn tai cΓ³ xΓ‘c nhαΊ­n của UBND quαΊ­n, huyện trở lΓͺn, cΖ‘ sở Δ‘Γ o tαΊ‘o xem xΓ©t quyαΊΏt Δ‘α»‹nh tiαΊΏp nhαΊ­n thΓ­ sinh vΓ o học hoαΊ·c bαΊ£o lΖ°u kαΊΏt quαΊ£ tuyển sinh để thΓ­ sinh vΓ o học sau; c) NαΊΏu do sai sΓ³t, nhαΊ§m lαΊ«n của cΓ‘n bα»™ thα»±c hiện cΓ΄ng tΓ‘c tuyển sinh hoαΊ·c cΓ‘ nhΓ’n thΓ­ sinh gΓ’y ra, cΖ‘ sở Δ‘Γ o tαΊ‘o chủ Δ‘α»™ng phα»‘i hợp vα»›i cΓ‘c cΓ‘ nhΓ’n, tα»• chα»©c liΓͺn quan xem xΓ©t cΓ‘c minh chα»©ng vΓ  quyαΊΏt Δ‘α»‹nh việc tiαΊΏp nhαΊ­n thΓ­ sinh vΓ o học hoαΊ·c bαΊ£o lΖ°u kαΊΏt quαΊ£ tuyển sinh để thΓ­ sinh vΓ o học sau. 4. ThΓ­ sinh Δ‘Γ£ xΓ‘c nhαΊ­n nhαΊ­p học tαΊ‘i mα»™t cΖ‘ sở Δ‘Γ o tαΊ‘o khΓ΄ng được tham gia xΓ©t tuyển ở nΖ‘i khΓ‘c hoαΊ·c ở cΓ‘c đợt xΓ©t tuyển bα»• sung, trα»« trường hợp được cΖ‘ sở Δ‘Γ o tαΊ‘o cho phΓ©p."' - 'Tα»• chα»©c, nhiệm vα»₯, quyền hαΊ‘n của Ban Chỉ huy ... 2. Nhiệm vα»₯, quyền hαΊ‘n của Ban Chỉ huy: a) Chỉ Δ‘αΊ‘o xΓ’y dα»±ng, ban hΓ nh quy Δ‘α»‹nh về cΓ΄ng tΓ‘c bαΊ£o Δ‘αΊ£m an toΓ n PCCC vΓ  CNCH tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p. b) HΖ°α»›ng dαΊ«n, phα»‘i hợp vα»›i cΓ‘c Δ‘Ζ‘n vα»‹ thuα»™c Bα»™ vΓ  chỉ Δ‘αΊ‘o Đội PCCC vΓ  CNCH cΖ‘ sở tα»• chα»©c tuyΓͺn truyền, bα»“i dΖ°α»‘ng nghiệp vα»₯ PCCC vΓ  CNCH. c) Chỉ Δ‘αΊ‘o Đội PCCC vΓ  CNCH cΖ‘ sở tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p xΓ’y dα»±ng, trΓ¬nh cαΊ₯p cΓ³ thαΊ©m quyền phΓͺ duyệt vΓ  tα»• chα»©c thα»±c tαΊ­p phΖ°Ζ‘ng Γ‘n PCCC, phΖ°Ζ‘ng Γ‘n CNCH. d) Chỉ Δ‘αΊ‘o Đội PCCC vΓ  CNCH cΖ‘ sở tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p quαΊ£n lΓ½ cΓ‘c trang thiαΊΏt bα»‹ PCCC vΓ  CNCH. Δ‘) Chỉ Δ‘αΊ‘o chα»―a chΓ‘y, CNCH khi xαΊ£y ra chΓ‘y, sα»± cα»‘, tai nαΊ‘n tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p. e) Chỉ Δ‘αΊ‘o việc tα»• chα»©c lαΊ­p vΓ  lΖ°u giα»― hα»“ sΖ‘ quαΊ£n lΓ½, theo dΓ΅i hoαΊ‘t Δ‘α»™ng PCCC, CNCH tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p. g) Chỉ Δ‘αΊ‘o việc sΖ‘ kαΊΏt, tα»•ng kαΊΏt cΓ‘c hoαΊ‘t Δ‘α»™ng về PCCC vΓ  CNCH của cΖ‘ quan; kiểm tra, Δ‘Γ΄n Δ‘α»‘c việc chαΊ₯p hΓ nh cΓ‘c quy Δ‘α»‹nh về PCCC vΓ  CNCH. h) Đề xuαΊ₯t việc khen thưởng, kα»· luαΊ­t cΓ‘c tαΊ­p thể, cΓ‘ nhΓ’n trong việc thα»±c hiện cΓ΄ng tΓ‘c PCCC, CNCH. i) Chỉ Δ‘αΊ‘o Đội PCCC vΓ  CNCH cΖ‘ sở dα»± trΓΉ kinh phΓ­ cho cΓ‘c hoαΊ‘t Δ‘α»™ng PCCC vΓ  CNCH tαΊ‘i Trα»₯ sở cΖ‘ quan Bα»™ TΖ° phΓ‘p. k) Thα»±c hiện cΓ‘c nhiệm vα»₯ khΓ‘c do Bα»™ trưởng giao vΓ  theo quy Δ‘α»‹nh của phΓ‘p luαΊ­t.' - 'Mα»©c hưởng chαΊΏ Δ‘α»™ thai sαΊ£n ... b) Mα»©c hưởng mα»™t ngΓ y Δ‘α»‘i vα»›i trường hợp quy Δ‘α»‹nh tαΊ‘i Điều 32 vΓ  khoαΊ£n 2 Điều 34 của LuαΊ­t nΓ y được tΓ­nh bαΊ±ng mα»©c hưởng chαΊΏ Δ‘α»™ thai sαΊ£n theo thΓ‘ng chia cho 24 ngΓ y.' - source_sentence: Doanh nghiệp được Γ‘p dα»₯ng chαΊΏ Δ‘α»™ Ζ°u tiΓͺn khΓ΄ng cung cαΊ₯p bΓ‘o cΓ‘o kiểm toΓ‘n Δ‘ΓΊng thời hαΊ‘n bα»‹ phαΊ‘t bao nhiΓͺu tiền? sentences: - 'Thay Δ‘α»•i ThαΊ©m phΓ‘n, Hα»™i thαΊ©m 1. ThαΊ©m phΓ‘n, Hα»™i thαΊ©m phαΊ£i tα»« chα»‘i tham gia xΓ©t xα»­ hoαΊ·c bα»‹ thay Δ‘α»•i khi thuα»™c mα»™t trong cΓ‘c trường hợp: a) Trường hợp quy Δ‘α»‹nh tαΊ‘i Điều 49 của Bα»™ luαΊ­t nΓ y; b) Họ cΓΉng trong mα»™t Hα»™i Δ‘α»“ng xΓ©t xα»­ vΓ  lΓ  người thΓ’n thΓ­ch vα»›i nhau; c) Đã tham gia xΓ©t xα»­ sΖ‘ thαΊ©m hoαΊ·c phΓΊc thαΊ©m hoαΊ·c tiαΊΏn hΓ nh tα»‘ tα»₯ng vα»₯ Γ‘n Δ‘Γ³ vα»›i tΖ° cΓ‘ch lΓ  Điều tra viΓͺn, CΓ‘n bα»™ Δ‘iều tra, Kiểm sΓ‘t viΓͺn, Kiểm tra viΓͺn, ThαΊ©m tra viΓͺn, ThΖ° kΓ½ TΓ²a Γ‘n. 2. Việc thay Δ‘α»•i ThαΊ©m phΓ‘n, Hα»™i thαΊ©m trΖ°α»›c khi mở phiΓͺn tΓ²a do ChΓ‘nh Γ‘n hoαΊ·c PhΓ³ ChΓ‘nh Γ‘n TΓ²a Γ‘n được phΓ’n cΓ΄ng giαΊ£i quyαΊΏt vα»₯ Γ‘n quyαΊΏt Δ‘α»‹nh. ThαΊ©m phΓ‘n bα»‹ thay Δ‘α»•i lΓ  ChΓ‘nh Γ‘n TΓ²a Γ‘n thΓ¬ do ChΓ‘nh Γ‘n TΓ²a Γ‘n trΓͺn mα»™t cαΊ₯p quyαΊΏt Δ‘α»‹nh. Việc thay Δ‘α»•i ThαΊ©m phΓ‘n, Hα»™i thαΊ©m tαΊ‘i phiΓͺn tΓ²a do Hα»™i Δ‘α»“ng xΓ©t xα»­ quyαΊΏt Δ‘α»‹nh trΖ°α»›c khi bαΊ―t Δ‘αΊ§u xΓ©t hỏi bαΊ±ng cΓ‘ch biểu quyαΊΏt tαΊ‘i phΓ²ng nghα»‹ Γ‘n. Khi xem xΓ©t thay Δ‘α»•i thΓ nh viΓͺn nΓ o thΓ¬ thΓ nh viΓͺn Δ‘Γ³ được trΓ¬nh bΓ y Γ½ kiαΊΏn của mΓ¬nh, Hα»™i Δ‘α»“ng quyαΊΏt Δ‘α»‹nh theo Δ‘a sα»‘. Trường hợp phαΊ£i thay Δ‘α»•i ThαΊ©m phΓ‘n, Hα»™i thαΊ©m tαΊ‘i phiΓͺn tΓ²a thΓ¬ Hα»™i Δ‘α»“ng xΓ©t xα»­ ra quyαΊΏt Δ‘α»‹nh hoΓ£n phiΓͺn tΓ²a.' - 'β€œΔiều 21. ChαΊ₯m dα»©t hưởng trợ cαΊ₯p thαΊ₯t nghiệp 1. CΓ‘c trường hợp người lao Δ‘α»™ng Δ‘ang hưởng trợ cαΊ₯p thαΊ₯t nghiệp bα»‹ chαΊ₯m dα»©t hưởng trợ cαΊ₯p thαΊ₯t nghiệp được quy Δ‘α»‹nh nhΖ° sau: e) Trong thời gian hưởng trợ cαΊ₯p thαΊ₯t nghiệp, 03 thΓ‘ng liΓͺn tα»₯c khΓ΄ng thα»±c hiện thΓ΄ng bΓ‘o hαΊ±ng thΓ‘ng về việc tΓ¬m kiαΊΏm việc lΓ m vα»›i trung tΓ’m dα»‹ch vα»₯ việc lΓ m theo quy Δ‘α»‹nh NgΓ y mΓ  người lao Δ‘α»™ng được xΓ‘c Δ‘α»‹nh bα»‹ chαΊ₯m dα»©t hưởng trợ cαΊ₯p thαΊ₯t nghiệp lΓ  ngΓ y kαΊΏt thΓΊc của thời hαΊ‘n thΓ΄ng bΓ‘o tΓ¬m kiαΊΏm việc lΓ m của thΓ‘ng thα»© 3 liΓͺn tα»₯c mΓ  người lao Δ‘α»™ng khΓ΄ng thα»±c hiện thΓ΄ng bΓ‘o hαΊ±ng thΓ‘ng về việc tΓ¬m kiαΊΏm việc lΓ m."' - 'Vi phαΊ‘m quy Δ‘α»‹nh về thời hαΊ‘n lΓ m thủ tα»₯c hαΊ£i quan, nα»™p hα»“ sΖ‘ thuαΊΏ ... 2. PhαΊ‘t tiền tα»« 1.000.000 Δ‘α»“ng Δ‘αΊΏn 2.000.000 Δ‘α»“ng Δ‘α»‘i vα»›i hΓ nh vi khΓ΄ng thα»±c hiện Δ‘ΓΊng thời hαΊ‘n quy Δ‘α»‹nh thuα»™c mα»™t trong cΓ‘c trường hợp sau: a) Cung cαΊ₯p bΓ‘o cΓ‘o kiểm toΓ‘n, bΓ‘o cΓ‘o tΓ i chΓ­nh của doanh nghiệp được Γ‘p dα»₯ng chαΊΏ Δ‘α»™ Ζ°u tiΓͺn; b) ThΓ΄ng bΓ‘o cho cΖ‘ quan hαΊ£i quan quyαΊΏt Δ‘α»‹nh xα»­ lΓ½ vi phαΊ‘m phΓ‘p luαΊ­t về quαΊ£n lΓ½ thuαΊΏ, kαΊΏ toΓ‘n Δ‘α»‘i vα»›i doanh nghiệp được Γ‘p dα»₯ng chαΊΏ Δ‘α»™ Ζ°u tiΓͺn; c) BΓ‘o cΓ‘o về lượng hΓ ng hΓ³a nhαΊ­p khαΊ©u phα»₯c vα»₯ xΓ’y dα»±ng nhΓ  xưởng, hΓ ng hΓ³a gα»­i kho bΓͺn ngoΓ i của doanh nghiệp chαΊΏ xuαΊ₯t; d) BΓ‘o cΓ‘o về lượng hΓ ng hΓ³a trung chuyển Δ‘Ζ°a vΓ o, Δ‘Ζ°a ra, cΓ²n lΖ°u tαΊ‘i cαΊ£ng; Δ‘) BΓ‘o cΓ‘o thα»‘ng kΓͺ thΓ΄ng quan hΓ ng bΖ°u chΓ­nh Δ‘Ζ°a vΓ o Việt Nam để chuyển tiαΊΏp Δ‘i quα»‘c tαΊΏ. ...' - source_sentence: TΓ i chΓ­nh của Hα»™i Kiểm toΓ‘n viΓͺn hΓ nh nghề Việt Nam được chi cho nhα»―ng khoαΊ£n nΓ o? sentences: - 'GiαΊ£i thể vΓ  xα»­ lΓ½ tΓ i chΓ­nh khi giαΊ£i thể 1. Khi xΓ©t thαΊ₯y hoαΊ‘t Δ‘α»™ng của Hα»™i khΓ΄ng cΓ³ hiệu quαΊ£, khΓ΄ng mang lαΊ‘i lợi Γ­ch cho Hα»™i viΓͺn hoαΊ·c gΓ’y phiền hΓ , cαΊ£n trở cho Hα»™i viΓͺn thΓ¬ BCH Hα»™i quyαΊΏt Δ‘α»‹nh triệu tαΊ­p Đẑi hα»™i để bΓ n biện phΓ‘p củng cα»‘ tα»• chα»©c hoαΊ·c giαΊ£i thể Hα»™i. NαΊΏu giαΊ£i thể Hα»™i thΓ¬ do Đẑi hα»™i Δ‘αΊ‘i biểu hoαΊ·c Đẑi hα»™i toΓ n quα»‘c của Hα»™i thΓ΄ng qua vΓ  đề nghα»‹ cΖ‘ quan NhΓ  nΖ°α»›c cΓ³ thαΊ©m quyền xem xΓ©t, quyαΊΏt Δ‘α»‹nh. 2. Khi Hα»™i bα»‹ giαΊ£i thể, Ban Thường trα»±c vΓ  Ban Kiểm tra của Hα»™i phαΊ£i tiαΊΏn hΓ nh kiểm kΓͺ tΓ i sαΊ£n, kiểm quα»Ή vΓ  bΓ‘o cΓ‘o BCH Hα»™i quyαΊΏt Δ‘α»‹nh việc xα»­ lΓ½ tΓ i sαΊ£n, tiền tα»“n quα»Ή vΓ  tiαΊΏn hΓ nh thủ tα»₯c giαΊ£i thể theo quy Δ‘α»‹nh của phΓ‘p luαΊ­t.' - '"Điều 14. Miα»…n trα»« Δ‘α»‘i vα»›i thỏa thuαΊ­n hαΊ‘n chαΊΏ cαΊ‘nh tranh bα»‹ cαΊ₯m 1. Thỏa thuαΊ­n hαΊ‘n chαΊΏ cαΊ‘nh tranh quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 1, 2, 3, 7, 8, 9, 10 vΓ  11 Điều 11 bα»‹ cαΊ₯m theo quy Δ‘α»‹nh tαΊ‘i Điều 12 của LuαΊ­t nΓ y được miα»…n trα»« cΓ³ thời hαΊ‘n nαΊΏu cΓ³ lợi cho người tiΓͺu dΓΉng vΓ  Δ‘Γ‘p α»©ng mα»™t trong cΓ‘c Δ‘iều kiện sau Δ‘Γ’y: a) TΓ‘c Δ‘α»™ng thΓΊc Δ‘αΊ©y tiαΊΏn bα»™ kα»Ή thuαΊ­t, cΓ΄ng nghệ, nΓ’ng cao chαΊ₯t lượng hΓ ng hΓ³a, dα»‹ch vα»₯; b) TΔƒng cường sα»©c cαΊ‘nh tranh của doanh nghiệp Việt Nam trΓͺn thα»‹ trường quα»‘c tαΊΏ; c) ThΓΊc Δ‘αΊ©y việc Γ‘p dα»₯ng thα»‘ng nhαΊ₯t tiΓͺu chuαΊ©n chαΊ₯t lượng, Δ‘α»‹nh mα»©c kα»Ή thuαΊ­t của chủng loαΊ‘i sαΊ£n phαΊ©m; d) Thα»‘ng nhαΊ₯t cΓ‘c Δ‘iều kiện thα»±c hiện hợp Δ‘α»“ng, giao hΓ ng, thanh toΓ‘n nhΖ°ng khΓ΄ng liΓͺn quan Δ‘αΊΏn giΓ‘ vΓ  cΓ‘c yαΊΏu tα»‘ của giΓ‘. 2. Thỏa thuαΊ­n lao Δ‘α»™ng, thỏa thuαΊ­n hợp tΓ‘c trong cΓ‘c ngΓ nh, lΔ©nh vα»±c Δ‘αΊ·c thΓΉ được thα»±c hiện theo quy Δ‘α»‹nh của luαΊ­t khΓ‘c thΓ¬ thα»±c hiện theo quy Δ‘α»‹nh của luαΊ­t Δ‘Γ³".' - '"Điều 2. Sα»­a Δ‘α»•i, bα»• sung mα»™t sα»‘ Δ‘iều của Nghα»‹ Δ‘α»‹nh sα»‘ 15/2019/NĐ-CP ngΓ y 01 thΓ‘ng 02 nΔƒm 2019 của ChΓ­nh phủ quy Δ‘α»‹nh chi tiαΊΏt mα»™t sα»‘ Δ‘iều vΓ  biện phΓ‘p thi hΓ nh LuαΊ­t GiΓ‘o dα»₯c nghề nghiệp ... 12. Sα»­a Δ‘α»•i, bα»• sung Điều 24 nhΖ° sau: Điều 24. ThαΊ©m quyền cαΊ₯p giαΊ₯y chα»©ng nhαΊ­n Δ‘Δƒng kΓ½ hoαΊ‘t Δ‘α»™ng liΓͺn kαΊΏt Δ‘Γ o tαΊ‘o vα»›i nΖ°α»›c ngoΓ i 1. Tα»•ng cα»₯c GiΓ‘o dα»₯c nghề nghiệp cαΊ₯p giαΊ₯y chα»©ng nhαΊ­n Δ‘Δƒng kΓ½ hoαΊ‘t Δ‘α»™ng liΓͺn kαΊΏt Δ‘Γ o tαΊ‘o vα»›i nΖ°α»›c ngoΓ i Δ‘α»‘i vα»›i trường cao Δ‘αΊ³ng. 2. Sở Lao Δ‘α»™ng - ThΖ°Ζ‘ng binh vΓ  XΓ£ hα»™i nΖ‘i trường trung cαΊ₯p, trung tΓ’m giΓ‘o dα»₯c nghề nghiệp, trung tΓ’m giΓ‘o dα»₯c nghề nghiệp - giΓ‘o dα»₯c thường xuyΓͺn vΓ  doanh nghiệp tα»• chα»©c hoαΊ‘t Δ‘α»™ng liΓͺn kαΊΏt Δ‘Γ o tαΊ‘o vα»›i nΖ°α»›c ngoΓ i cαΊ₯p giαΊ₯y chα»©ng nhαΊ­n Δ‘Δƒng kΓ½ hoαΊ‘t Δ‘α»™ng liΓͺn kαΊΏt Δ‘Γ o tαΊ‘o vα»›i nΖ°α»›c ngoΓ i Δ‘α»‘i vα»›i trường trung cαΊ₯p, trung tΓ’m giΓ‘o dα»₯c nghề nghiệp, trung tΓ’m giΓ‘o dα»₯c nghề nghiệp - giΓ‘o dα»₯c thường xuyΓͺn vΓ  doanh nghiệp."' - source_sentence: NLĐ kΓ½ nhiều hợp Δ‘α»“ng lao Δ‘α»™ng thΓ¬ Δ‘Γ³ng BHYT nhΖ° thαΊΏ nΓ o? sentences: - 'Hα»“ sΖ‘, thủ tα»₯c xΓ‘c Δ‘α»‹nh trường hợp được bα»“i thường [...] 3. Trong thời hαΊ‘n 05 ngΓ y lΓ m việc, kể tα»« ngΓ y nhαΊ­n được Δ‘Ζ‘n vΓ  cΓ‘c giαΊ₯y tờ hợp lệ, nαΊΏu xΓ‘c Δ‘α»‹nh yΓͺu cαΊ§u thuα»™c trΓ‘ch nhiệm giαΊ£i quyαΊΏt của mΓ¬nh thΓ¬ Sở Y tαΊΏ phαΊ£i thα»₯ lΓ½ vΓ  thΓ΄ng bΓ‘o bαΊ±ng vΔƒn bαΊ£n về việc thα»₯ lΓ½ Δ‘Ζ‘n cho người bα»‹ thiệt hαΊ‘i hoαΊ·c thΓ’n nhΓ’n của người bα»‹ thiệt hαΊ‘i (sau Δ‘Γ’y gọi tαΊ―t lΓ  người bα»‹ thiệt hαΊ‘i). Trường hợp hα»“ sΖ‘ khΓ΄ng Δ‘αΊ§y đủ thΓ¬ Sở Y tαΊΏ cΓ³ vΔƒn bαΊ£n hΖ°α»›ng dαΊ«n người bα»‹ thiệt hαΊ‘i bα»• sung. 4. Trong thời hαΊ‘n 15 ngΓ y, kể tα»« ngΓ y nhαΊ­n được Δ‘Ζ‘n yΓͺu cαΊ§u của người bα»‹ thiệt hαΊ‘i, Sở Y tαΊΏ phαΊ£i hoΓ n thΓ nh việc xΓ‘c Δ‘α»‹nh nguyΓͺn nhΓ’n gΓ’y tai biαΊΏn, mα»©c Δ‘α»™ tα»•n thΖ°Ζ‘ng vΓ  thΓ΄ng bΓ‘o bαΊ±ng vΔƒn bαΊ£n cho người yΓͺu cαΊ§u Δ‘α»“ng thời bΓ‘o cΓ‘o Bα»™ Y tαΊΏ.' - 'Chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n 1. Tα»• chα»©c, cΓ‘ nhΓ’n nhαΊ­n chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n phαΊ£i cΓ³ đủ Δ‘iều kiện để được cαΊ₯p GiαΊ₯y phΓ©p thΔƒm dΓ² khoΓ‘ng sαΊ£n theo quy Δ‘α»‹nh của LuαΊ­t nΓ y. 2. Việc chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n phαΊ£i được cΖ‘ quan quαΊ£n lΓ½ nhΓ  nΖ°α»›c cΓ³ thαΊ©m quyền cαΊ₯p GiαΊ₯y phΓ©p thΔƒm dΓ² khoΓ‘ng sαΊ£n chαΊ₯p thuαΊ­n; trường hợp được chαΊ₯p thuαΊ­n, tα»• chα»©c, cΓ‘ nhΓ’n nhαΊ­n chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n được cαΊ₯p GiαΊ₯y phΓ©p thΔƒm dΓ² khoΓ‘ng sαΊ£n mα»›i. 3. Tα»• chα»©c, cΓ‘ nhΓ’n chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n Δ‘Γ£ thα»±c hiện được Γ­t nhαΊ₯t 50% dα»± toΓ‘n của đề Γ‘n thΔƒm dΓ² khoΓ‘ng sαΊ£n. 4. ChΓ­nh phủ quy Δ‘α»‹nh chi tiαΊΏt việc chuyển nhượng quyền thΔƒm dΓ² khoΓ‘ng sαΊ£n.' - '"Sα»­a Δ‘α»•i, bα»• sung mα»™t sα»‘ Δ‘iều của LuαΊ­t bαΊ£o hiểm y tαΊΏ: ... 6. Sα»­a Δ‘α»•i, bα»• sung Điều 12 nhΖ° sau: β€œΔiều 12. Đối tượng tham gia bαΊ£o hiểm y tαΊΏ 1. NhΓ³m do người lao Δ‘α»™ng vΓ  người sα»­ dα»₯ng lao Δ‘α»™ng Δ‘Γ³ng, bao gα»“m: a) Người lao Δ‘α»™ng lΓ m việc theo hợp Δ‘α»“ng lao Δ‘α»™ng khΓ΄ng xΓ‘c Δ‘α»‹nh thời hαΊ‘n, hợp Δ‘α»“ng lao Δ‘α»™ng cΓ³ thời hαΊ‘n tα»« đủ 3 thΓ‘ng trở lΓͺn; người lao Δ‘α»™ng lΓ  người quαΊ£n lΓ½ doanh nghiệp hưởng tiền lΖ°Ζ‘ng; cΓ‘n bα»™, cΓ΄ng chα»©c, viΓͺn chα»©c (sau Δ‘Γ’y gọi chung lΓ  người lao Δ‘α»™ng); b) Người hoαΊ‘t Δ‘α»™ng khΓ΄ng chuyΓͺn trΓ‘ch ở xΓ£, phường, thα»‹ trαΊ₯n theo quy Δ‘α»‹nh của phΓ‘p luαΊ­t.= ... 4. NhΓ³m được ngΓ’n sΓ‘ch nhΓ  nΖ°α»›c hα»— trợ mα»©c Δ‘Γ³ng, bao gα»“m: a) Người thuα»™c hα»™ gia Δ‘Γ¬nh cαΊ­n nghΓ¨o; b) Học sinh, sinh viΓͺn. 5. NhΓ³m tham gia bαΊ£o hiểm y tαΊΏ theo hα»™ gia Δ‘Γ¬nh gα»“m nhα»―ng người thuα»™c hα»™ gia Δ‘Γ¬nh, trα»« Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 1, 2, 3 vΓ  4 Điều nΓ y. 6. ChΓ­nh phủ quy Δ‘α»‹nh cΓ‘c Δ‘α»‘i tượng khΓ‘c ngoΓ i cΓ‘c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 3, 4 vΓ  5 Điều nΓ y; quy Δ‘α»‹nh việc cαΊ₯p thαΊ» bαΊ£o hiểm y tαΊΏ Δ‘α»‘i vα»›i Δ‘α»‘i tượng do Bα»™ Quα»‘c phΓ²ng, Bα»™ CΓ΄ng an quαΊ£n lΓ½ vΓ  Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i Δ‘iểm 1 khoαΊ£n 3 Điều nΓ y; quy Δ‘α»‹nh lα»™ trΓ¬nh thα»±c hiện bαΊ£o hiểm y tαΊΏ, phαΊ‘m vi quyền lợi, mα»©c hưởng bαΊ£o hiểm y tαΊΏ, khΓ‘m bệnh, chα»―a bệnh bαΊ£o hiểm y tαΊΏ, quαΊ£n lΓ½, sα»­ dα»₯ng phαΊ§n kinh phΓ­ dΓ nh cho khΓ‘m bệnh, chα»―a bệnh bαΊ£o hiểm y tαΊΏ, giΓ‘m Δ‘α»‹nh bαΊ£o hiểm y tαΊΏ, thanh toΓ‘n, quyαΊΏt toΓ‘n bαΊ£o hiểm y tαΊΏ Δ‘α»‘i vα»›i cΓ‘c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i Δ‘iểm a khoαΊ£n 3 Điều nΓ y.”' --- # SentenceTransformer based on keepitreal/vietnamese-sbert This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 --> - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the πŸ€— Hub model = SentenceTransformer("Cloyne/vietnamese-embedding_finetuned") # Run inference sentences = [ 'NLĐ kΓ½ nhiều hợp Δ‘α»“ng lao Δ‘α»™ng thΓ¬ Δ‘Γ³ng BHYT nhΖ° thαΊΏ nΓ o?', '"Sα»­a Δ‘α»•i, bα»• sung mα»™t sα»‘ Δ‘iều của LuαΊ­t bαΊ£o hiểm y tαΊΏ:\n...\n6. Sα»­a Δ‘α»•i, bα»• sung Điều 12 nhΖ° sau:\nβ€œΔiều 12. Đối tượng tham gia bαΊ£o hiểm y tαΊΏ\n1. NhΓ³m do người lao Δ‘α»™ng vΓ  người sα»­ dα»₯ng lao Δ‘α»™ng Δ‘Γ³ng, bao gα»“m:\na) Người lao Δ‘α»™ng lΓ m việc theo hợp Δ‘α»“ng lao Δ‘α»™ng khΓ΄ng xΓ‘c Δ‘α»‹nh thời hαΊ‘n, hợp Δ‘α»“ng lao Δ‘α»™ng cΓ³ thời hαΊ‘n tα»« đủ 3 thΓ‘ng trở lΓͺn; người lao Δ‘α»™ng lΓ  người quαΊ£n lΓ½ doanh nghiệp hưởng tiền lΖ°Ζ‘ng; cΓ‘n bα»™, cΓ΄ng chα»©c, viΓͺn chα»©c (sau Δ‘Γ’y gọi chung lΓ  người lao Δ‘α»™ng);\nb) Người hoαΊ‘t Δ‘α»™ng khΓ΄ng chuyΓͺn trΓ‘ch ở xΓ£, phường, thα»‹ trαΊ₯n theo quy Δ‘α»‹nh của phΓ‘p luαΊ­t.=\n...\n4. NhΓ³m được ngΓ’n sΓ‘ch nhΓ  nΖ°α»›c hα»— trợ mα»©c Δ‘Γ³ng, bao gα»“m:\na) Người thuα»™c hα»™ gia Δ‘Γ¬nh cαΊ­n nghΓ¨o;\nb) Học sinh, sinh viΓͺn.\n5. NhΓ³m tham gia bαΊ£o hiểm y tαΊΏ theo hα»™ gia Δ‘Γ¬nh gα»“m nhα»―ng người thuα»™c hα»™ gia Δ‘Γ¬nh, trα»« Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 1, 2, 3 vΓ  4 Điều nΓ y.\n6. ChΓ­nh phủ quy Δ‘α»‹nh cΓ‘c Δ‘α»‘i tượng khΓ‘c ngoΓ i cΓ‘c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 3, 4 vΓ  5 Điều nΓ y; quy Δ‘α»‹nh việc cαΊ₯p thαΊ» bαΊ£o hiểm y tαΊΏ Δ‘α»‘i vα»›i Δ‘α»‘i tượng do Bα»™ Quα»‘c phΓ²ng, Bα»™ CΓ΄ng an quαΊ£n lΓ½ vΓ  Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i Δ‘iểm 1 khoαΊ£n 3 Điều nΓ y; quy Δ‘α»‹nh lα»™ trΓ¬nh thα»±c hiện bαΊ£o hiểm y tαΊΏ, phαΊ‘m vi quyền lợi, mα»©c hưởng bαΊ£o hiểm y tαΊΏ, khΓ‘m bệnh, chα»―a bệnh bαΊ£o hiểm y tαΊΏ, quαΊ£n lΓ½, sα»­ dα»₯ng phαΊ§n kinh phΓ­ dΓ nh cho khΓ‘m bệnh, chα»―a bệnh bαΊ£o hiểm y tαΊΏ, giΓ‘m Δ‘α»‹nh bαΊ£o hiểm y tαΊΏ, thanh toΓ‘n, quyαΊΏt toΓ‘n bαΊ£o hiểm y tαΊΏ Δ‘α»‘i vα»›i cΓ‘c Δ‘α»‘i tượng quy Δ‘α»‹nh tαΊ‘i Δ‘iểm a khoαΊ£n 3 Điều nΓ y.”', 'Hα»“ sΖ‘, thủ tα»₯c xΓ‘c Δ‘α»‹nh trường hợp được bα»“i thường\n[...]\n3. Trong thời hαΊ‘n 05 ngΓ y lΓ m việc, kể tα»« ngΓ y nhαΊ­n được Δ‘Ζ‘n vΓ  cΓ‘c giαΊ₯y tờ hợp lệ, nαΊΏu xΓ‘c Δ‘α»‹nh yΓͺu cαΊ§u thuα»™c trΓ‘ch nhiệm giαΊ£i quyαΊΏt của mΓ¬nh thΓ¬ Sở Y tαΊΏ phαΊ£i thα»₯ lΓ½ vΓ  thΓ΄ng bΓ‘o bαΊ±ng vΔƒn bαΊ£n về việc thα»₯ lΓ½ Δ‘Ζ‘n cho người bα»‹ thiệt hαΊ‘i hoαΊ·c thΓ’n nhΓ’n của người bα»‹ thiệt hαΊ‘i (sau Δ‘Γ’y gọi tαΊ―t lΓ  người bα»‹ thiệt hαΊ‘i). Trường hợp hα»“ sΖ‘ khΓ΄ng Δ‘αΊ§y đủ thΓ¬ Sở Y tαΊΏ cΓ³ vΔƒn bαΊ£n hΖ°α»›ng dαΊ«n người bα»‹ thiệt hαΊ‘i bα»• sung.\n4. Trong thời hαΊ‘n 15 ngΓ y, kể tα»« ngΓ y nhαΊ­n được Δ‘Ζ‘n yΓͺu cαΊ§u của người bα»‹ thiệt hαΊ‘i, Sở Y tαΊΏ phαΊ£i hoΓ n thΓ nh việc xΓ‘c Δ‘α»‹nh nguyΓͺn nhΓ’n gΓ’y tai biαΊΏn, mα»©c Δ‘α»™ tα»•n thΖ°Ζ‘ng vΓ  thΓ΄ng bΓ‘o bαΊ±ng vΔƒn bαΊ£n cho người yΓͺu cαΊ§u Δ‘α»“ng thời bΓ‘o cΓ‘o Bα»™ Y tαΊΏ.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 120,210 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 25.08 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 206.98 tokens</li><li>max: 256 tokens</li></ul> | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Nα»™i dung lα»“ng ghΓ©p vαΊ₯n đề bΓ¬nh Δ‘αΊ³ng giα»›i trong xΓ’y dα»±ng vΔƒn bαΊ£n quy phαΊ‘m phΓ‘p luαΊ­t được quy Δ‘α»‹nh thαΊΏ nΓ o?</code> | <code>Nα»™i dung lα»“ng ghΓ©p vαΊ₯n đề bΓ¬nh Δ‘αΊ³ng giα»›i trong xΓ’y dα»±ng vΔƒn bαΊ£n quy phαΊ‘m phΓ‘p luαΊ­t<br>Trong phαΊ‘m vi Δ‘iều chỉnh của vΔƒn bαΊ£n quy phαΊ‘m phΓ‘p luαΊ­t:<br>1. XΓ‘c Δ‘α»‹nh nα»™i dung liΓͺn quan Δ‘αΊΏn vαΊ₯n đề bΓ¬nh Δ‘αΊ³ng giα»›i hoαΊ·c vαΊ₯n đề bαΊ₯t bΓ¬nh Δ‘αΊ³ng giα»›i, phΓ’n biệt Δ‘α»‘i xα»­ về giα»›i.<br>2. Quy Δ‘α»‹nh cΓ‘c biện phΓ‘p cαΊ§n thiαΊΏt để thα»±c hiện bΓ¬nh Δ‘αΊ³ng giα»›i hoαΊ·c để giαΊ£i quyαΊΏt vαΊ₯n đề bαΊ₯t bΓ¬nh Δ‘αΊ³ng giα»›i, phΓ’n biệt Δ‘α»‘i xα»­ về giα»›i; dα»± bΓ‘o tΓ‘c Δ‘α»™ng của cΓ‘c quy Δ‘α»‹nh Δ‘Γ³ Δ‘α»‘i vα»›i nam vΓ  nα»― sau khi được ban hΓ nh.<br>3. XΓ‘c Δ‘α»‹nh nguα»“n nhΓ’n lα»±c, tΓ i chΓ­nh cαΊ§n thiαΊΏt để triển khai cΓ‘c biện phΓ‘p thα»±c hiện bΓ¬nh Δ‘αΊ³ng giα»›i hoαΊ·c để giαΊ£i quyαΊΏt vαΊ₯n đề bαΊ₯t bΓ¬nh Δ‘αΊ³ng giα»›i, phΓ’n biệt Δ‘α»‘i xα»­ về giα»›i.</code> | | <code>Điều kiện để giΓ‘o viΓͺn trong cΖ‘ sở giΓ‘o dα»₯c mαΊ§m non, tiểu học ngoΓ i cΓ΄ng lαΊ­p bα»‹ αΊ£nh hưởng bởi Covid-19 được hưởng chΓ­nh sΓ‘ch hα»— trợ lΓ  gΓ¬?</code> | <code>Điều kiện được hưởng<br>CΓ‘n bα»™ quαΊ£n lΓ½, giΓ‘o viΓͺn, nhΓ’n viΓͺn được hưởng chΓ­nh sΓ‘ch khi bαΊ£o Δ‘αΊ£m cΓ‘c Δ‘iều kiện sau:<br>1. LΓ  người Δ‘ang lΓ m việc tαΊ‘i cΖ‘ sở giΓ‘o dα»₯c ngoΓ i cΓ΄ng lαΊ­p trΖ°α»›c khi cΖ‘ sở phαΊ£i tαΊ‘m dα»«ng hoαΊ‘t Δ‘α»™ng theo yΓͺu cαΊ§u của cΖ‘ quan nhΓ  nΖ°α»›c cΓ³ thαΊ©m quyền để phΓ²ng, chα»‘ng dα»‹ch COVID-19 tΓ­nh tα»« ngΓ y 01 thΓ‘ng 5 nΔƒm 2021 Δ‘αΊΏn hαΊΏt ngΓ y 31 thΓ‘ng 12 nΔƒm 2021.<br>2. Nghỉ việc khΓ΄ng hưởng lΖ°Ζ‘ng tα»« 01 thΓ‘ng trở lΓͺn tΓ­nh tα»« ngΓ y 01 thΓ‘ng 5 nΔƒm 2021 Δ‘αΊΏn hαΊΏt ngΓ y 31 thΓ‘ng 12 nΔƒm 2021.<br>3. ChΖ°a được hưởng chΓ­nh sΓ‘ch hα»— trợ Δ‘α»‘i vα»›i người lao Δ‘α»™ng tαΊ‘m hoΓ£n hợp Δ‘α»“ng lao Δ‘α»™ng, nghỉ việc khΓ΄ng hưởng lΖ°Ζ‘ng theo quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 4, khoαΊ£n 5, khoαΊ£n 6 Mα»₯c II Nghα»‹ quyαΊΏt sα»‘ 68/NQ-CP ngΓ y 01 thΓ‘ng 7 nΔƒm 2021 của ChΓ­nh phủ về mα»™t sα»‘ chΓ­nh sΓ‘ch hα»— trợ người lao Δ‘α»™ng vΓ  người sα»­ dα»₯ng lao Δ‘α»™ng gαΊ·p khΓ³ khΔƒn do Δ‘αΊ‘i dα»‹ch COVID-19, Nghα»‹ quyαΊΏt sα»‘ 126/NQ-CP ngΓ y 08 thΓ‘ng 10 nΔƒm 2021 của ChΓ­nh phủ sα»­a Δ‘α»•i, bα»• sung Nghα»‹ quyαΊΏt sα»‘ 68/NQ-CP ngΓ y 01 thΓ‘ng 7 nΔƒm 2021 của ChΓ­nh phủ về mα»™t sα»‘ chΓ­nh sΓ‘ch hα»— trợ người lao Δ‘α»™ng vΓ  người sα»­ dα»₯ng lao Δ‘α»™ng gαΊ·p khΓ³ khΔƒn do Δ‘αΊ‘i dα»‹ch COVID-19 (sau Δ‘Γ’y gọi tαΊ―t lΓ  Nghα»‹ quyαΊΏt sα»‘ 68/NQ-CP) do khΓ΄ng tham gia BαΊ£o hiểm xΓ£ hα»™i bαΊ―t buα»™c.<br>4. CΓ³ xΓ‘c nhαΊ­n lΓ m việc tαΊ‘i cΖ‘ sở giΓ‘o dα»₯c ngoΓ i cΓ΄ng lαΊ­p Γ­t nhαΊ₯t hαΊΏt nΔƒm học 2021 - 2022 theo kαΊΏ hoαΊ‘ch nΔƒm học của Δ‘α»‹a phΖ°Ζ‘ng, bao gα»“m cΖ‘ sở giΓ‘o dα»₯c ngoΓ i cΓ΄ng lαΊ­p Δ‘Γ£ lΓ m việc trΖ°α»›c Δ‘Γ’y hoαΊ·c cΖ‘ sở giΓ‘o dα»₯c ngoΓ i cΓ΄ng lαΊ­p khΓ‘c trong trường hợp cΖ‘ sở giΓ‘o dα»₯c ngoΓ i cΓ΄ng lαΊ­p trΖ°α»›c Δ‘Γ’y lΓ m việc khΓ΄ng hoαΊ‘t Δ‘α»™ng trở lαΊ‘i.</code> | | <code>NguyΓͺn tαΊ―c Γ‘p dα»₯ng phα»₯ cαΊ₯p Ζ°u Δ‘Γ£i nghề y tαΊΏ thαΊΏ nΓ o?</code> | <code>NguyΓͺn tαΊ―c Γ‘p dα»₯ng<br>1. Trường hợp cΓ΄ng chα»©c, viΓͺn chα»©c chuyΓͺn mΓ΄n y tαΊΏ thuα»™c Δ‘α»‘i tượng được hưởng cΓ‘c mα»©c phα»₯ cαΊ₯p Ζ°u Δ‘Γ£i theo nghề khΓ‘c nhau thΓ¬ được hưởng mα»™t mα»©c phα»₯ cαΊ₯p Ζ°u Δ‘Γ£i theo nghề cao nhαΊ₯t.<br>2. CΓ΄ng chα»©c, viΓͺn chα»©c Δ‘Γ£ hưởng phα»₯ cαΊ₯p Ζ°u Δ‘Γ£i theo nghề quy Δ‘α»‹nh tαΊ‘i ThΓ΄ng tΖ° liΓͺn tα»‹ch sα»‘ 06/2010/TTLT-BYT-BNV-BTC ngΓ y 22/3/2010 của Bα»™ Y tαΊΏ, Bα»™ Nα»™i vα»₯, Bα»™ TΓ i chΓ­nh hΖ°α»›ng dαΊ«n thα»±c hiện Nghα»‹ Δ‘α»‹nh sα»‘ 64/2009/NĐ-CP ngΓ y 30/7/2009 của ChΓ­nh phủ về chΓ­nh sΓ‘ch Δ‘α»‘i vα»›i cΓ‘n bα»™, viΓͺn chα»©c y tαΊΏ cΓ΄ng tΓ‘c ở vΓΉng cΓ³ Δ‘iều kiện kinh tαΊΏ - xΓ£ hα»™i Δ‘αΊ·c biệt khΓ³ khΔƒn thΓ¬ khΓ΄ng hưởng phα»₯ cαΊ₯p Ζ°u Δ‘Γ£i theo nghề quy Δ‘α»‹nh tαΊ‘i ThΓ΄ng tΖ° liΓͺn tα»‹ch nΓ y.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### train * Dataset: train * Size: 13,357 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 24.61 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 202.71 tokens</li><li>max: 256 tokens</li></ul> | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>ToΓ  Γ‘n cαΊ₯p nΓ o cΓ³ thαΊ©m quyền giαΊ£i quyαΊΏt việc Δ‘Γ²i tΓ i sαΊ£n Δ‘Γ£ cho người khΓ‘c vay theo hợp Δ‘α»“ng cho vay?</code> | <code>"Điều 35. ThαΊ©m quyền của TΓ²a Γ‘n nhΓ’n dΓ’n cαΊ₯p huyện<br>1. TΓ²a Γ‘n nhΓ’n dΓ’n cαΊ₯p huyện cΓ³ thαΊ©m quyền giαΊ£i quyαΊΏt theo thủ tα»₯c sΖ‘ thαΊ©m nhα»―ng tranh chαΊ₯p sau Δ‘Γ’y:<br>a) Tranh chαΊ₯p về dΓ’n sα»±, hΓ΄n nhΓ’n vΓ  gia Δ‘Γ¬nh quy Δ‘α»‹nh tαΊ‘i Điều 26 vΓ  Điều 28 của Bα»™ luαΊ­t nΓ y, trα»« tranh chαΊ₯p quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 7 Điều 26 của Bα»™ luαΊ­t nΓ y;<br>b) Tranh chαΊ₯p về kinh doanh, thΖ°Ζ‘ng mαΊ‘i quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 1 Điều 30 của Bα»™ luαΊ­t nΓ y;<br>c) Tranh chαΊ₯p về lao Δ‘α»™ng quy Δ‘α»‹nh tαΊ‘i Điều 32 của Bα»™ luαΊ­t nΓ y.<br>2. TΓ²a Γ‘n nhΓ’n dΓ’n cαΊ₯p huyện cΓ³ thαΊ©m quyền giαΊ£i quyαΊΏt nhα»―ng yΓͺu cαΊ§u sau Δ‘Γ’y:<br>a) YΓͺu cαΊ§u về dΓ’n sα»± quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 1, 2, 3, 4, 6, 7, 8, 9 vΓ  10 Điều 27 của Bα»™ luαΊ­t nΓ y;<br>b) YΓͺu cαΊ§u về hΓ΄n nhΓ’n vΓ  gia Δ‘Γ¬nh quy Δ‘α»‹nh tαΊ‘i cΓ‘c khoαΊ£n 1, 2, 3, 4, 5, 6, 7, 8, 10 vΓ  11 Điều 29 của Bα»™ luαΊ­t nΓ y;<br>c) YΓͺu cαΊ§u về kinh doanh, thΖ°Ζ‘ng mαΊ‘i quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 1 vΓ  khoαΊ£n 6 Điều 31 của Bα»™ luαΊ­t nΓ y;<br>d) YΓͺu cαΊ§u về lao Δ‘α»™ng quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 1 vΓ  khoαΊ£n 5 Điều 33 của Bα»™ luαΊ­t nΓ y.<br>3. Nhα»―ng tranh chαΊ₯p, yΓͺu cαΊ§u quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 1 vΓ  khoαΊ£n 2 Điều nΓ y mΓ  cΓ³ Δ‘Ζ°Ζ‘ng sα»± hoαΊ·c tΓ i sαΊ£n ở nΖ°α»›c ngoΓ i hoαΊ·c cαΊ§n phαΊ£i ủy thΓ‘c tΖ° phΓ‘p cho cΖ‘ quan Δ‘αΊ‘i diện nΖ°α»›c Cα»™ng hΓ²a xΓ£ hα»™i chủ nghΔ©a Việt Nam ở nΖ°α»›c ngoΓ i, cho TΓ²a Γ‘n, cΖ‘ quan cΓ³ thαΊ©m quyền của nΖ°α»›c ngoΓ i khΓ΄ng thuα»™c thαΊ©m quyền giαΊ£i quyαΊΏt của TΓ²a Γ‘n nhΓ’n dΓ’n cαΊ₯p huyện, trα»« trường hợp quy Δ‘α»‹nh tαΊ‘i khoαΊ£n 4 Điều nΓ y.<br>4. TΓ²a Γ‘n nhΓ’n dΓ’n cαΊ₯p huyện nΖ‘i cΖ° trΓΊ của cΓ΄ng dΓ’n Việt Nam hủy việc kαΊΏt hΓ΄n trΓ‘i phΓ‘p luαΊ­t, giαΊ£i quyαΊΏt việc ly hΓ΄n, cΓ‘c tranh chαΊ₯p về quyền vΓ  nghΔ©a vα»₯ của vợ chα»“ng, cha mαΊΉ vΓ  con, về nhαΊ­n cha, mαΊΉ, con, nuΓ΄i con nuΓ΄i vΓ  giΓ‘m hα»™ giα»―a cΓ΄ng dΓ’n Việt Nam cΖ° trΓΊ ở khu vα»±c biΓͺn giα»›i vα»›i cΓ΄ng dΓ’n của nΖ°α»›c lΓ‘ng giềng cΓΉng cΖ° trΓΊ ở khu vα»±c biΓͺn giα»›i vα»›i Việt Nam theo quy Δ‘α»‹nh của Bα»™ luαΊ­t nΓ y vΓ  cΓ‘c quy Δ‘α»‹nh khΓ‘c của phΓ‘p luαΊ­t Việt Nam."</code> | | <code>Nhα»―ng phiαΊΏu bαΊ§u nΓ o được xem lΓ  khΓ΄ng hợp lệ?</code> | <code>PhiαΊΏu bαΊ§u khΓ΄ng hợp lệ<br>1. Nhα»―ng phiαΊΏu bαΊ§u sau Δ‘Γ’y lΓ  phiαΊΏu bαΊ§u khΓ΄ng hợp lệ:<br>a) PhiαΊΏu khΓ΄ng theo mαΊ«u quy Δ‘α»‹nh do Tα»• bαΊ§u cα»­ phΓ‘t ra;<br>b) PhiαΊΏu khΓ΄ng cΓ³ dαΊ₯u của Tα»• bαΊ§u cα»­;<br>c) PhiαΊΏu để sα»‘ người được bαΊ§u nhiều hΖ‘n sα»‘ lượng Δ‘αΊ‘i biểu được bαΊ§u Δ‘Γ£ αΊ₯n Δ‘α»‹nh cho Δ‘Ζ‘n vα»‹ bαΊ§u cα»­;<br>d) PhiαΊΏu gαΊ‘ch xΓ³a hαΊΏt tΓͺn nhα»―ng người α»©ng cα»­;<br>Δ‘) PhiαΊΏu ghi thΓͺm tΓͺn người ngoΓ i danh sΓ‘ch nhα»―ng người α»©ng cα»­ hoαΊ·c phiαΊΏu cΓ³ ghi thΓͺm nα»™i dung khΓ‘c.<br>2. Trường hợp cΓ³ phiαΊΏu bαΊ§u được cho lΓ  khΓ΄ng hợp lệ thΓ¬ Tα»• trường Tα»• bαΊ§u cα»­ Δ‘Ζ°a ra để toΓ n Tα»• xem xΓ©t, quyαΊΏt Δ‘α»‹nh. Tα»• bαΊ§u cα»­ khΓ΄ng được gαΊ‘ch xΓ³a hoαΊ·c sα»­a cΓ‘c tΓͺn ghi trΓͺn phiαΊΏu bαΊ§u.</code> | | <code>Đề nghα»‹ tαΊ‘m Δ‘Γ¬nh chỉ chαΊ₯p hΓ nh quyαΊΏt Δ‘α»‹nh Γ‘p dα»₯ng biện phΓ‘p Δ‘Ζ°a vΓ o trường giΓ‘o dΖ°α»‘ng cho học sinh cαΊ§n Δ‘αΊ£m bαΊ£o nguyΓͺn tαΊ―c gΓ¬?</code> | <code>NguyΓͺn tαΊ―c xΓ©t duyệt, đề nghα»‹ giαΊ£m thời hαΊ‘n, tαΊ‘m Δ‘Γ¬nh chỉ chαΊ₯p hΓ nh quyαΊΏt Δ‘α»‹nh, miα»…n chαΊ₯p hΓ nh phαΊ§n thời gian cΓ²n lαΊ‘i cho học sinh trường giΓ‘o dΖ°α»‘ng, trαΊ‘i viΓͺn cΖ‘ sở giΓ‘o dα»₯c bαΊ―t buα»™c<br>1. TuΓ’n thủ quy Δ‘α»‹nh của phΓ‘p luαΊ­t về thi hΓ nh biện phΓ‘p xα»­ lΓ½ hΓ nh chΓ­nh Δ‘Ζ°a vΓ o trường giΓ‘o dΖ°α»‘ng, cΖ‘ sở giΓ‘o dα»₯c bαΊ―t buα»™c, quy Δ‘α»‹nh tαΊ‘i ThΓ΄ng tΖ° nΓ y vΓ  quy Δ‘α»‹nh của phΓ‘p luαΊ­t cΓ³ liΓͺn quan.<br>2. BαΊ£o Δ‘αΊ£m khΓ‘ch quan, cΓ΄ng khai, minh bαΊ‘ch, Δ‘ΓΊng trΓ¬nh tα»±, thủ tα»₯c, thαΊ©m quyền; tΓ΄n trọng vΓ  bαΊ£o vệ quyền, lợi Γ­ch hợp phΓ‘p của học sinh trường giΓ‘o dΖ°α»‘ng, trαΊ‘i viΓͺn cΖ‘ sở giΓ‘o dα»₯c bαΊ―t buα»™c.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | train loss | |:------:|:-----:|:-------------:|:----------:| | 0.1331 | 500 | 0.3247 | 0.2239 | | 0.2662 | 1000 | 0.1513 | 0.1605 | | 0.3993 | 1500 | 0.119 | 0.1664 | | 0.5323 | 2000 | 0.1047 | 0.1384 | | 0.6654 | 2500 | 0.0915 | 0.1269 | | 0.7985 | 3000 | 0.0861 | 0.1140 | | 0.9316 | 3500 | 0.0839 | 0.1091 | | 1.0647 | 4000 | 0.0693 | 0.0989 | | 1.1978 | 4500 | 0.0582 | 0.0931 | | 1.3308 | 5000 | 0.0457 | 0.0953 | | 1.4639 | 5500 | 0.0284 | 0.0826 | | 1.5970 | 6000 | 0.0233 | 0.0848 | | 1.7301 | 6500 | 0.0256 | 0.0785 | | 1.8632 | 7000 | 0.0236 | 0.0829 | | 1.9963 | 7500 | 0.0203 | 0.0827 | | 2.1294 | 8000 | 0.0182 | 0.0730 | | 2.2624 | 8500 | 0.0143 | 0.0718 | | 2.3955 | 9000 | 0.0103 | 0.0720 | | 2.5286 | 9500 | 0.0086 | 0.0720 | | 2.6617 | 10000 | 0.0058 | 0.0706 | | 2.7948 | 10500 | 0.0074 | 0.0675 | | 2.9279 | 11000 | 0.0073 | 0.0650 | | 3.0610 | 11500 | 0.0054 | 0.0651 | | 3.1940 | 12000 | 0.0043 | 0.0639 | | 3.3271 | 12500 | 0.004 | 0.0626 | | 3.4602 | 13000 | 0.0035 | 0.0617 | | 3.5933 | 13500 | 0.0022 | 0.0614 | | 3.7264 | 14000 | 0.003 | 0.0624 | | 3.8595 | 14500 | 0.0022 | 0.0616 | | 3.9925 | 15000 | 0.0028 | 0.0606 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.2.1 - Transformers: 4.45.1 - PyTorch: 2.4.0 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
g-assismoraes/deberta-large-semeval25_EN08_fold3
g-assismoraes
2024-10-28T14:37:33Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T14:23:46Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer model-index: - name: deberta-large-semeval25_EN08_fold3 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. --> # deberta-large-semeval25_EN08_fold3 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.2442 - Precision Samples: 0.1144 - Recall Samples: 0.7997 - F1 Samples: 0.1930 - Precision Macro: 0.3896 - Recall Macro: 0.6167 - F1 Macro: 0.2236 - Precision Micro: 0.1104 - Recall Micro: 0.7507 - F1 Micro: 0.1924 - Precision Weighted: 0.2237 - Recall Weighted: 0.7507 - F1 Weighted: 0.2130 ## Model description More information needed ## Intended uses & 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 8.2513 | 1.0 | 73 | 9.8237 | 0.1093 | 0.4542 | 0.1631 | 0.8860 | 0.2559 | 0.1808 | 0.1110 | 0.3399 | 0.1674 | 0.6512 | 0.3399 | 0.0922 | | 6.8883 | 2.0 | 146 | 9.3725 | 0.1082 | 0.6445 | 0.1710 | 0.7810 | 0.3726 | 0.1997 | 0.0995 | 0.5637 | 0.1692 | 0.4732 | 0.5637 | 0.1274 | | 8.4363 | 3.0 | 219 | 8.8450 | 0.1195 | 0.7090 | 0.1933 | 0.6684 | 0.4525 | 0.2167 | 0.1073 | 0.6374 | 0.1837 | 0.3811 | 0.6374 | 0.1603 | | 8.6787 | 4.0 | 292 | 8.5427 | 0.1068 | 0.7465 | 0.1790 | 0.5303 | 0.5162 | 0.1950 | 0.0967 | 0.6941 | 0.1697 | 0.2823 | 0.6941 | 0.1599 | | 6.8889 | 5.0 | 365 | 8.5407 | 0.1100 | 0.7823 | 0.1854 | 0.4867 | 0.5780 | 0.2249 | 0.1022 | 0.7337 | 0.1794 | 0.2365 | 0.7337 | 0.1842 | | 7.9121 | 6.0 | 438 | 8.4019 | 0.1096 | 0.7957 | 0.1858 | 0.4441 | 0.5804 | 0.2166 | 0.1041 | 0.7365 | 0.1825 | 0.2387 | 0.7365 | 0.1936 | | 7.1827 | 7.0 | 511 | 8.3315 | 0.1085 | 0.8046 | 0.1846 | 0.4158 | 0.6204 | 0.2251 | 0.1042 | 0.7507 | 0.1831 | 0.2210 | 0.7507 | 0.2018 | | 5.9674 | 8.0 | 584 | 8.1923 | 0.1100 | 0.8047 | 0.1857 | 0.3929 | 0.6172 | 0.2292 | 0.1046 | 0.7620 | 0.1839 | 0.2236 | 0.7620 | 0.2136 | | 6.397 | 9.0 | 657 | 8.2536 | 0.1113 | 0.8023 | 0.1884 | 0.3999 | 0.6139 | 0.2328 | 0.1077 | 0.7507 | 0.1883 | 0.2269 | 0.7507 | 0.2148 | | 6.4848 | 10.0 | 730 | 8.2442 | 0.1144 | 0.7997 | 0.1930 | 0.3896 | 0.6167 | 0.2236 | 0.1104 | 0.7507 | 0.1924 | 0.2237 | 0.7507 | 0.2130 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
devilteo911/whisper-small-ita-ct2
devilteo911
2024-10-28T14:35:05Z
20
0
ctranslate2
[ "ctranslate2", "audio", "automatic-speech-recognition", "hf-asr-leaderboard", "it", "en", "arxiv:2212.04356", "license:apache-2.0", "region:us" ]
automatic-speech-recognition
2024-10-28T14:21:50Z
--- license: apache-2.0 language: - it - en metrics: - wer pipeline_tag: automatic-speech-recognition tags: - audio - automatic-speech-recognition - hf-asr-leaderboard library_name: ctranslate2 --- # Litus whisper-small-ita for CTranslate2 La repo contiene la conversione di [litus-ai/whisper-small-ita](https://huggingface.co/litus-ai/whisper-small-ita/) al formato di [CTranslate2](https://github.com/OpenNMT/CTranslate2). Questo modello puΓ² essere usato su CTranslate2 o su progetti affini tipo:[faster-whisper](https://github.com/systran/faster-whisper). # Descrizione del Modello Questo modello Γ¨ una versione di [openai/whisper-small](https://huggingface.co/openai/whisper-small) ottimizzata per la lingua italiana, addestrata utilizzando una parte dei dati proprietari di [Litus AI](https://litus.ai/it/). `litus-ai/whisper-small-ita` rappresenta un ottimo compromesso value/cost ed Γ¨ ottimale per contesti in cui il budget computazionale Γ¨ limitato, ma Γ¨ comunque necessaria una trascrizione accurata del parlato. # ParticolaritΓ  del Modello La peculiaritΓ  principale del modello Γ¨ l'integrazione di token speciali che arricchiscono la trascrizione con meta-informazioni: - Elementi paralinguistici: `[LAUGH]`, `[MHMH]`, `[SIGH]`, `[UHM]` - QualitΓ  audio: `[NOISE]`, `[UNINT]` (non intelligibile) - Caratteristiche del parlato: `[AUTOCOR]` (autocorrezioni), `[L-EN]` (code-switching inglese) Questi token consentono una trascrizione piΓΉ ricca che cattura non solo il contenuto verbale ma anche elementi contestuali rilevanti. # Evaluation Nel seguente grafico puoi trovare l'Accuracy di `openai/whisper-small`, `openai/whisper-medium`, `litus-ai/whisper-small-ita` e il modello proprietario di Litus AI, `litus-proprietary`, su benchmark proprietari per meeting e chiamate vocali in lingua italiana. <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://huggingface.co/litus-ai/whisper-small-ita/resolve/main/Models%20Accuracy.png" alt="Litus AI eval"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Come usare il modello Puoi utlizzare devilteo911/whisper-small-ita-ct2 tramite faster-whisper: ```python from faster_whisper import WhisperModel model = WhisperModel("devilteo911/whisper-small-ita-ct2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Dettagli sulla conversione Il modello originale Γ¨ stato convertito usando questo comando: ``` ct2-transformers-converter --model litus-ai/whisper-small-ita --output_dir whisper-small-ita-ct2 \ --copy_files tokenizer_config.json preprocessor_config.json vocab.json normalizer.json merges.txt \ added_tokens.json generation_config.json special_tokens_map.json --quantization float16 ``` Nota che i pesi del modello sono salvati in FP16. Questo tipo puΓ² essere cambiato al momento del caricamento del modello usando il parametro [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). # Conclusions Per qualsiasi informazione sull'architettura sui dati utilizzati per il pretraining e l'intended use ti preghiamo di rivolgerti al [Paper](https://arxiv.org/abs/2212.04356), la [Model Card](https://huggingface.co/openai/whisper-small) e la [Repository](https://github.com/openai/whisper) originali.
aarontseng/fair-nmt-zh_hant-en
aarontseng
2024-10-28T14:28:52Z
111
0
transformers
[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "zh", "en", "base_model:Helsinki-NLP/opus-mt-zh-en", "base_model:finetune:Helsinki-NLP/opus-mt-zh-en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2024-10-25T03:39:43Z
--- license: mit language: - zh - en base_model: - Helsinki-NLP/opus-mt-zh-en pipeline_tag: translation library_name: transformers --- - ckp: 1995000 - bleu (flores200-dev): 56.84109 - bleu (flores200-devtest): 13.7635 - comet (flores200-dev): 0.853607586029181 - comet (flores200-devtest): 0.8553770352964816
RichardErkhov/bn999_-_mistral-4.2B-gguf
RichardErkhov
2024-10-28T14:23:46Z
40
0
null
[ "gguf", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T12:09:57Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-4.2B - GGUF - Model creator: https://huggingface.co/bn999/ - Original model: https://huggingface.co/bn999/mistral-4.2B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mistral-4.2B.Q2_K.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q2_K.gguf) | Q2_K | 1.58GB | | [mistral-4.2B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q3_K_S.gguf) | Q3_K_S | 1.82GB | | [mistral-4.2B.Q3_K.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q3_K.gguf) | Q3_K | 2.03GB | | [mistral-4.2B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q3_K_M.gguf) | Q3_K_M | 2.03GB | | [mistral-4.2B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q3_K_L.gguf) | Q3_K_L | 2.21GB | | [mistral-4.2B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.IQ4_XS.gguf) | IQ4_XS | 2.26GB | | [mistral-4.2B.Q4_0.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q4_0.gguf) | Q4_0 | 2.35GB | | [mistral-4.2B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.IQ4_NL.gguf) | IQ4_NL | 2.38GB | | [mistral-4.2B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q4_K_S.gguf) | Q4_K_S | 2.37GB | | [mistral-4.2B.Q4_K.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q4_K.gguf) | Q4_K | 2.48GB | | [mistral-4.2B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q4_K_M.gguf) | Q4_K_M | 2.48GB | | [mistral-4.2B.Q4_1.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q4_1.gguf) | Q4_1 | 2.6GB | | [mistral-4.2B.Q5_0.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q5_0.gguf) | Q5_0 | 2.85GB | | [mistral-4.2B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q5_K_S.gguf) | Q5_K_S | 2.85GB | | [mistral-4.2B.Q5_K.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q5_K.gguf) | Q5_K | 2.92GB | | [mistral-4.2B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q5_K_M.gguf) | Q5_K_M | 2.92GB | | [mistral-4.2B.Q5_1.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q5_1.gguf) | Q5_1 | 3.1GB | | [mistral-4.2B.Q6_K.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q6_K.gguf) | Q6_K | 3.38GB | | [mistral-4.2B.Q8_0.gguf](https://huggingface.co/RichardErkhov/bn999_-_mistral-4.2B-gguf/blob/main/mistral-4.2B.Q8_0.gguf) | Q8_0 | 4.38GB | Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 --- Selectively pruned and re-trained Mistral-7B for reduced size, targeting only MPT layers.
g-assismoraes/deberta-large-semeval25_EN08_fold2
g-assismoraes
2024-10-28T14:23:31Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T14:10:17Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer model-index: - name: deberta-large-semeval25_EN08_fold2 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. --> # deberta-large-semeval25_EN08_fold2 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.7224 - Precision Samples: 0.1123 - Recall Samples: 0.7856 - F1 Samples: 0.1886 - Precision Macro: 0.3681 - Recall Macro: 0.6639 - F1 Macro: 0.2792 - Precision Micro: 0.1054 - Recall Micro: 0.7394 - F1 Micro: 0.1844 - Precision Weighted: 0.1953 - Recall Weighted: 0.7394 - F1 Weighted: 0.2101 ## Model description More information needed ## Intended uses & 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.1108 | 1.0 | 73 | 9.1532 | 0.1078 | 0.4896 | 0.1658 | 0.8477 | 0.3196 | 0.2145 | 0.1041 | 0.3879 | 0.1641 | 0.6101 | 0.3879 | 0.0866 | | 8.9481 | 2.0 | 146 | 8.6922 | 0.1037 | 0.6457 | 0.1672 | 0.7149 | 0.4241 | 0.2235 | 0.0947 | 0.5515 | 0.1616 | 0.4524 | 0.5515 | 0.1227 | | 9.1563 | 3.0 | 219 | 8.6496 | 0.0968 | 0.7189 | 0.1614 | 0.5923 | 0.5060 | 0.2388 | 0.0875 | 0.6485 | 0.1542 | 0.3085 | 0.6485 | 0.1447 | | 8.7006 | 4.0 | 292 | 8.2522 | 0.1016 | 0.7955 | 0.1617 | 0.5424 | 0.5864 | 0.2606 | 0.0877 | 0.7333 | 0.1567 | 0.2756 | 0.7333 | 0.1672 | | 8.1242 | 5.0 | 365 | 7.9321 | 0.1011 | 0.7940 | 0.1721 | 0.4725 | 0.6190 | 0.2653 | 0.0945 | 0.7364 | 0.1675 | 0.2425 | 0.7364 | 0.1754 | | 7.4891 | 6.0 | 438 | 8.0728 | 0.1081 | 0.7863 | 0.1824 | 0.4759 | 0.6115 | 0.2650 | 0.0989 | 0.7303 | 0.1743 | 0.2454 | 0.7303 | 0.1816 | | 8.3973 | 7.0 | 511 | 7.8203 | 0.1074 | 0.7803 | 0.1817 | 0.3908 | 0.6341 | 0.2637 | 0.1002 | 0.7424 | 0.1765 | 0.1962 | 0.7424 | 0.1906 | | 7.0048 | 8.0 | 584 | 7.7429 | 0.1097 | 0.7953 | 0.1849 | 0.3862 | 0.6590 | 0.2731 | 0.1017 | 0.7515 | 0.1791 | 0.2017 | 0.7515 | 0.2014 | | 6.3856 | 9.0 | 657 | 7.7281 | 0.1081 | 0.7852 | 0.1823 | 0.3555 | 0.6382 | 0.2597 | 0.1016 | 0.7424 | 0.1788 | 0.1924 | 0.7424 | 0.2033 | | 5.8015 | 10.0 | 730 | 7.7224 | 0.1123 | 0.7856 | 0.1886 | 0.3681 | 0.6639 | 0.2792 | 0.1054 | 0.7394 | 0.1844 | 0.1953 | 0.7394 | 0.2101 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
Bonbone/ML5-fine-tuning-xsum
Bonbone
2024-10-28T14:20:50Z
105
0
transformers
[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2024-10-28T13:15:45Z
--- library_name: transformers license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: ML5-fine-tuning-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: test args: default metrics: - name: Rouge1 type: rouge value: 0.5714 --- <!-- 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. --> # ML5-fine-tuning-xsum This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 7.4333 - Rouge1: 0.5714 - Rouge2: 0.0 - Rougel: 0.5714 - Rougelsum: 0.5714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 18.7065 | 1.0 | 7 | 9.6966 | 0.0 | 0.0 | 0.0 | 0.0 | | 10.3198 | 2.0 | 14 | 7.4333 | 0.5714 | 0.0 | 0.5714 | 0.5714 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.3.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
joelem/autotrain-publicradio-3
joelem
2024-10-28T14:20:14Z
5
0
null
[ "tensorboard", "safetensors", "bert", "autotrain", "text-classification", "base_model:joelem/autotrain-publicradio-2", "base_model:finetune:joelem/autotrain-publicradio-2", "region:us" ]
text-classification
2024-10-25T22:26:42Z
--- tags: - autotrain - text-classification base_model: joelem/autotrain-publicradio-2 widget: - text: "I love AutoTrain" --- # Notes This is an intermittent fine tuning of the errors from autotrain-publicradio-2 based on Phil and Chris's corrections. Just to note, the accuracies here cant be compared with PR0 to PR2 bc it is different training data: - Specifically prediction data from PR2 that showed the lowest probability scores (.75 and below), so this is noisy data # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.6207979917526245 f1_macro: 0.659698275862069 f1_micro: 0.737410071942446 f1_weighted: 0.7260737099975193 precision_macro: 0.6876875812359683 precision_micro: 0.737410071942446 precision_weighted: 0.7253259852238735 recall_macro: 0.648967753098562 recall_micro: 0.737410071942446 recall_weighted: 0.737410071942446 accuracy: 0.737410071942446
Kartoshkina/laBSE-khakas
Kartoshkina
2024-10-28T14:20:07Z
5
0
sentence-transformers
[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-02-17T16:18:14Z
--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {Kartoshkina/laBSE-khakas} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 9980 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 100, "evaluator": "__main__.ChainScoreEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "warmupcosine", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
LightDestory/upernetconvnext-finetuned-segments-food-oct-28
LightDestory
2024-10-28T14:10:18Z
34
0
transformers
[ "transformers", "safetensors", "upernet", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-10-28T11:23:45Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jialei12138/task-13-Qwen-Qwen1.5-0.5B
jialei12138
2024-10-28T14:09:31Z
7
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "base_model:adapter:Qwen/Qwen1.5-0.5B", "region:us" ]
null
2024-10-06T08:26:34Z
--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **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] ### Framework versions - PEFT 0.13.2
bobox/DeBERTa3-s-CustomPoolin-toytest-step1
bobox
2024-10-28T14:01:20Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "deberta-v2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:32500", "loss:GISTEmbedLoss", "arxiv:1908.10084", "arxiv:2402.16829", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-10-28T14:00:56Z
--- base_model: microsoft/deberta-v3-small library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:32500 - loss:GISTEmbedLoss widget: - source_sentence: A picture of a white gas range with figurines above. sentences: - A nerdy woman brushing her teeth with a friend nearby. - a white stove turned off with a digital clock - The plasma membrane also contains other molecules, primarily other lipids and proteins. The green molecules in Figure above , for example, are the lipid cholesterol. Molecules of cholesterol help the plasma membrane keep its shape. Many of the proteins in the plasma membrane assist other substances in crossing the membrane. - source_sentence: who makes the kentucky derby garland of roses sentences: - Accrington strengthened their position in the play-off places with a hard-fought win over struggling Dagenham. - "tidal energy can be used to produce electricity. Ocean thermal is energy derived\ \ from waves and also from tidal waves. \n Ocean thermal energy can be used to\ \ produce electricity." - Kentucky Derby Trophy The Kroger Company has been the official florist of the Kentucky Derby since 1987. After taking over the duties from the Kingsley Walker florist, Kroger began constructing the prestigious garland in one of its local stores for the public to view on Derby Eve. The preservation of the garland and crowds of spectators watching its construction are a testament to the prestige and mystique of the Garland of Roses. - source_sentence: what is the difference between a general sense and a special sense? sentences: - 'Ian Curtis ( of Touching from a distance) Ian Kevin Curtis was an English musician and singer-songwriter. He is best known as the lead singer and lyricist of the post-punk band Joy Division. Joy Division released its debut album, Unknown Pleasures, in 1979 and recorded its follow-up, Closer, in 1980. Curtis, who suffered from epilepsy and depression, committed suicide on 18 May 1980, on the eve of Joy Division''s first North American tour, resulting in the band''s dissolution and the subsequent formation of New Order. Curtis was known for his baritone voice, dance style, and songwriting filled with imagery of desolation, emptiness and alienation. In 1995, Curtis''s widow Deborah published Touching from a Distance: Ian Curtis and Joy Division, a biography of the singer. His life and death Ian Kevin Curtis was an English musician and singer-songwriter. He is best known as the lead singer and lyricist of the post-punk band Joy Division. Joy Division released its debut album, Unknown Pleasures, in 1979 and recorded its follow-up, Closer, in 1980. Curtis, who suffered from epilepsy and depression, committed suicide on 18 May 1980, on the eve of Joy Division''s first North American tour, resulting in the band''s dissolution and the subsequent formation of New Order. Curtis was known for his baritone voice, dance style, and songwriting filled with imagery of desolation, emptiness and alienation. In 1995, Curtis''s widow Deborah published Touching from a Distance: Ian Curtis and Joy Division, a biography of the singer. His life and death have been dramatised in the films 24 Hour Party People (2002) and Control (2007). ...more' - The human body has two basic types of senses, called special senses and general senses. Special senses have specialized sense organs that gather sensory information and change it into nerve impulses. ... General senses, in contrast, are all associated with the sense of touch. They lack special sense organs. - Captain Hook Barrie states in the novel that "Hook was not his true name. To reveal who he really was would even at this date set the country in a blaze", and relates that Peter Pan began their rivalry by feeding the pirate's hand to the crocodile. He is said to be "Blackbeard's bo'sun" and "the only man of whom Barbecue was afraid".[5] (In Robert Louis Stevenson's Treasure Island, one of the names Long John Silver goes by is Barbecue.)[6] - source_sentence: Retzius was born in Stockholm , son of the anatomist Anders Jahan Retzius ( and grandson of the naturalist and chemist Anders Retzius ) . sentences: - Retzius was born in Stockholm , the son of anatomist Anders Jahan Retzius ( and grandson of the naturalist and chemist Anders Retzius ) . - As of 14 March , over 156,000 cases of COVID-19 have been reported in around 140 countries and territories ; more than 5,800 people have died from the disease and around 75,000 have recovered . - A person sitting on a stool on the street. - source_sentence: who was the first person who made the violin sentences: - Alice in Chains Alice in Chains is an American rock band from Seattle, Washington, formed in 1987 by guitarist and vocalist Jerry Cantrell and drummer Sean Kinney,[1] who recruited bassist Mike Starr[1] and lead vocalist Layne Staley.[1][2][3] Starr was replaced by Mike Inez in 1993.[4] After Staley's death in 2002, William DuVall joined in 2006 as co-lead vocalist and rhythm guitarist. The band took its name from Staley's previous group, the glam metal band Alice N' Chains.[5][2] - as distance from an object decreases , that object will appear larger - Violin The first makers of violins probably borrowed from various developments of the Byzantine lira. These included the rebec;[13] the Arabic rebab; the vielle (also known as the fidel or viuola); and the lira da braccio[11][14] The violin in its present form emerged in early 16th-century northern Italy. The earliest pictures of violins, albeit with three strings, are seen in northern Italy around 1530, at around the same time as the words "violino" and "vyollon" are seen in Italian and French documents. One of the earliest explicit descriptions of the instrument, including its tuning, is from the Epitome musical by Jambe de Fer, published in Lyon in 1556.[15] By this time, the violin had already begun to spread throughout Europe. model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.6699634563461265 name: Pearson Cosine - type: spearman_cosine value: 0.6740052367487698 name: Spearman Cosine - type: pearson_manhattan value: 0.6846904230572102 name: Pearson Manhattan - type: spearman_manhattan value: 0.676461767740328 name: Spearman Manhattan - type: pearson_euclidean value: 0.6819532604363933 name: Pearson Euclidean - type: spearman_euclidean value: 0.6744353858732639 name: Spearman Euclidean - type: pearson_dot value: 0.6677964772074442 name: Pearson Dot - type: spearman_dot value: 0.6714885153106404 name: Spearman Dot - type: pearson_max value: 0.6846904230572102 name: Pearson Max - type: spearman_max value: 0.676461767740328 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.697265625 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9149889349937439 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5579399141630902 name: Cosine F1 - type: cosine_f1_threshold value: 0.8168730735778809 name: Cosine F1 Threshold - type: cosine_precision value: 0.44368600682593856 name: Cosine Precision - type: cosine_recall value: 0.7514450867052023 name: Cosine Recall - type: cosine_ap value: 0.5242647012381595 name: Cosine Ap - type: dot_accuracy value: 0.6953125 name: Dot Accuracy - type: dot_accuracy_threshold value: 700.5377197265625 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5545851528384279 name: Dot F1 - type: dot_f1_threshold value: 623.9097900390625 name: Dot F1 Threshold - type: dot_precision value: 0.4456140350877193 name: Dot Precision - type: dot_recall value: 0.7341040462427746 name: Dot Recall - type: dot_ap value: 0.5241554075174903 name: Dot Ap - type: manhattan_accuracy value: 0.6953125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 235.2859344482422 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5517241379310345 name: Manhattan F1 - type: manhattan_f1_threshold value: 347.6478271484375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.4580152671755725 name: Manhattan Precision - type: manhattan_recall value: 0.6936416184971098 name: Manhattan Recall - type: manhattan_ap value: 0.5239028585462809 name: Manhattan Ap - type: euclidean_accuracy value: 0.697265625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 11.389955520629883 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5567451820128478 name: Euclidean F1 - type: euclidean_f1_threshold value: 16.685447692871094 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4421768707482993 name: Euclidean Precision - type: euclidean_recall value: 0.7514450867052023 name: Euclidean Recall - type: euclidean_ap value: 0.5247420500207234 name: Euclidean Ap - type: max_accuracy value: 0.697265625 name: Max Accuracy - type: max_accuracy_threshold value: 700.5377197265625 name: Max Accuracy Threshold - type: max_f1 value: 0.5579399141630902 name: Max F1 - type: max_f1_threshold value: 623.9097900390625 name: Max F1 Threshold - type: max_precision value: 0.4580152671755725 name: Max Precision - type: max_recall value: 0.7514450867052023 name: Max Recall - type: max_ap value: 0.5247420500207234 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.66796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.804556131362915 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.684297520661157 name: Cosine F1 - type: cosine_f1_threshold value: 0.7130892276763916 name: Cosine F1 Threshold - type: cosine_precision value: 0.5609756097560976 name: Cosine Precision - type: cosine_recall value: 0.8771186440677966 name: Cosine Recall - type: cosine_ap value: 0.6982323361009166 name: Cosine Ap - type: dot_accuracy value: 0.669921875 name: Dot Accuracy - type: dot_accuracy_threshold value: 609.73779296875 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6845637583892616 name: Dot F1 - type: dot_f1_threshold value: 546.085205078125 name: Dot F1 Threshold - type: dot_precision value: 0.5666666666666667 name: Dot Precision - type: dot_recall value: 0.864406779661017 name: Dot Recall - type: dot_ap value: 0.6969471595240038 name: Dot Ap - type: manhattan_accuracy value: 0.67578125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 363.409423828125 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.687392055267703 name: Manhattan F1 - type: manhattan_f1_threshold value: 430.9031982421875 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5801749271137027 name: Manhattan Precision - type: manhattan_recall value: 0.8432203389830508 name: Manhattan Recall - type: manhattan_ap value: 0.7021641064533223 name: Manhattan Ap - type: euclidean_accuracy value: 0.666015625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 17.237049102783203 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6844741235392321 name: Euclidean F1 - type: euclidean_f1_threshold value: 20.860803604125977 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5647382920110193 name: Euclidean Precision - type: euclidean_recall value: 0.8686440677966102 name: Euclidean Recall - type: euclidean_ap value: 0.6983440307123455 name: Euclidean Ap - type: max_accuracy value: 0.67578125 name: Max Accuracy - type: max_accuracy_threshold value: 609.73779296875 name: Max Accuracy Threshold - type: max_f1 value: 0.687392055267703 name: Max F1 - type: max_f1_threshold value: 546.085205078125 name: Max F1 Threshold - type: max_precision value: 0.5801749271137027 name: Max Precision - type: max_recall value: 0.8771186440677966 name: Max Recall - type: max_ap value: 0.7021641064533223 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( (linear_cls_pj): Linear(in_features=768, out_features=768, bias=True) (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True) (linear_mean_pj): Linear(in_features=768, out_features=768, bias=True) (linear_attnOut): Linear(in_features=768, out_features=768, bias=True) (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) ) (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_pjCls): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_pjMean): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the πŸ€— Hub model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest-step1") # Run inference sentences = [ 'who was the first person who made the violin', 'Violin The first makers of violins probably borrowed from various developments of the Byzantine lira. These included the rebec;[13] the Arabic rebab; the vielle (also known as the fidel or viuola); and the lira da braccio[11][14] The violin in its present form emerged in early 16th-century northern Italy. The earliest pictures of violins, albeit with three strings, are seen in northern Italy around 1530, at around the same time as the words "violino" and "vyollon" are seen in Italian and French documents. One of the earliest explicit descriptions of the instrument, including its tuning, is from the Epitome musical by Jambe de Fer, published in Lyon in 1556.[15] By this time, the violin had already begun to spread throughout Europe.', "Alice in Chains Alice in Chains is an American rock band from Seattle, Washington, formed in 1987 by guitarist and vocalist Jerry Cantrell and drummer Sean Kinney,[1] who recruited bassist Mike Starr[1] and lead vocalist Layne Staley.[1][2][3] Starr was replaced by Mike Inez in 1993.[4] After Staley's death in 2002, William DuVall joined in 2006 as co-lead vocalist and rhythm guitarist. The band took its name from Staley's previous group, the glam metal band Alice N' Chains.[5][2]", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:----------| | pearson_cosine | 0.67 | | **spearman_cosine** | **0.674** | | pearson_manhattan | 0.6847 | | spearman_manhattan | 0.6765 | | pearson_euclidean | 0.682 | | spearman_euclidean | 0.6744 | | pearson_dot | 0.6678 | | spearman_dot | 0.6715 | | pearson_max | 0.6847 | | spearman_max | 0.6765 | #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6973 | | cosine_accuracy_threshold | 0.915 | | cosine_f1 | 0.5579 | | cosine_f1_threshold | 0.8169 | | cosine_precision | 0.4437 | | cosine_recall | 0.7514 | | cosine_ap | 0.5243 | | dot_accuracy | 0.6953 | | dot_accuracy_threshold | 700.5377 | | dot_f1 | 0.5546 | | dot_f1_threshold | 623.9098 | | dot_precision | 0.4456 | | dot_recall | 0.7341 | | dot_ap | 0.5242 | | manhattan_accuracy | 0.6953 | | manhattan_accuracy_threshold | 235.2859 | | manhattan_f1 | 0.5517 | | manhattan_f1_threshold | 347.6478 | | manhattan_precision | 0.458 | | manhattan_recall | 0.6936 | | manhattan_ap | 0.5239 | | euclidean_accuracy | 0.6973 | | euclidean_accuracy_threshold | 11.39 | | euclidean_f1 | 0.5567 | | euclidean_f1_threshold | 16.6854 | | euclidean_precision | 0.4422 | | euclidean_recall | 0.7514 | | euclidean_ap | 0.5247 | | max_accuracy | 0.6973 | | max_accuracy_threshold | 700.5377 | | max_f1 | 0.5579 | | max_f1_threshold | 623.9098 | | max_precision | 0.458 | | max_recall | 0.7514 | | **max_ap** | **0.5247** | #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.668 | | cosine_accuracy_threshold | 0.8046 | | cosine_f1 | 0.6843 | | cosine_f1_threshold | 0.7131 | | cosine_precision | 0.561 | | cosine_recall | 0.8771 | | cosine_ap | 0.6982 | | dot_accuracy | 0.6699 | | dot_accuracy_threshold | 609.7378 | | dot_f1 | 0.6846 | | dot_f1_threshold | 546.0852 | | dot_precision | 0.5667 | | dot_recall | 0.8644 | | dot_ap | 0.6969 | | manhattan_accuracy | 0.6758 | | manhattan_accuracy_threshold | 363.4094 | | manhattan_f1 | 0.6874 | | manhattan_f1_threshold | 430.9032 | | manhattan_precision | 0.5802 | | manhattan_recall | 0.8432 | | manhattan_ap | 0.7022 | | euclidean_accuracy | 0.666 | | euclidean_accuracy_threshold | 17.237 | | euclidean_f1 | 0.6845 | | euclidean_f1_threshold | 20.8608 | | euclidean_precision | 0.5647 | | euclidean_recall | 0.8686 | | euclidean_ap | 0.6983 | | max_accuracy | 0.6758 | | max_accuracy_threshold | 609.7378 | | max_f1 | 0.6874 | | max_f1_threshold | 546.0852 | | max_precision | 0.5802 | | max_recall | 0.8771 | | **max_ap** | **0.7022** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 32,500 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 29.3 tokens</li><li>max: 343 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.53 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>A Slippery Dick is what type of creature?</code> | <code>The Slippery Dick (Juvenile) - Whats That Fish! Description Also known as Sand-reef Wrasses and Slippery Dick Wrasse. Found singly or in pairs or in groups constantly circling around reefs, sea grass beds and sandy areas. Colours highly variable especially between juvenile to adult. They feed on hard shell invertebrates. Length - 18cm Depth - 2-12m Widespread Western Atlantic & Caribbean Most reef fish seen by divers during the day are grazers, that cruise around just above the surface of the coral or snoop into crevices looking for algae, worms and small crustaceans. Wrasses have small protruding teeth and graze the bottom taking in a variety of snails, worms, crabs, shrimps and eggs. Any hard coats or thick shells are then ground down by their pharyngeal jaws and the delicacies inside digested. From juvenile to adult wrasses dramatically alter their colour and body shapes. Wrasses are always on the go during the day, but are the first to go to bed and the last to rise. Small wrasses dive below the sand to sleep and larger wrasses wedge themselves in crevasses. Related creatures Heads up! Many creatures change during their life. Juvenile fish become adults and some change shape or their colour. Some species change sex and others just get older. The following creature(s) are known relatives of the Slippery Dick (Juvenile). Click the image(s) to explore further or hover over to get a better view! Slippery Dick</code> | | <code>e.&#9;in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> | <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code> | | <code>In December 2015 , the film was ranked # 192 on IMDb .</code> | <code>As of December 2015 , it is the # 192 highest rated film on IMDb.</code> | * Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.025} ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,664 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 28.74 tokens</li><li>max: 330 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 56.55 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What component of an organism, made up of many cells, in turn makes up an organ?</code> | <code></code> | | <code>Diffusion Diffusion is a process where atoms or molecules move from areas of high concentration to areas of low concentration.</code> | <code>Diffusion is the process in which a substance naturally moves from an area of higher to lower concentration.</code> | | <code>In the 1966 movie The Good, The Bad And The Ugly, Clint Eastwood played the Good" and Lee van Cleef played "the Bad", but who played "the Ugly"?</code> | <code>View All Photos (10) Movie Info In the last and the best installment of his so-called "Dollars" trilogy of Sergio Leone-directed "spaghetti westerns," Clint Eastwood reprised the role of a taciturn, enigmatic loner. Here he searches for a cache of stolen gold against rivals the Bad (Lee Van Cleef), a ruthless bounty hunter, and the Ugly (Eli Wallach), a Mexican bandit. Though dubbed "the Good," Eastwood's character is not much better than his opponents -- he is just smarter and shoots faster. The film's title reveals its ironic attitude toward the canonized heroes of the classical western. "The real West was the world of violence, fear, and brutal instincts," claimed Leone. "In pursuit of profit there is no such thing as good and evil, generosity or deviousness; everything depends on chance, and not the best wins but the luckiest." Immensely entertaining and beautifully shot in Techniscope by Tonino Delli Colli, the movie is a virtually definitive "spaghetti western," rivaled only by Leone's own Once Upon a Time in the West (1968). The main musical theme by Ennio Morricone hit #1 on the British pop charts. Originally released in Italy at 177 minutes, the movie was later cut for its international release. ~ Yuri German, Rovi Rating:</code> | * Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.025} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 256 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} - `warmup_ratio`: 0.33 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} - `warmup_ratio`: 0.33 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | Validation Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap | |:------:|:----:|:-------------:|:---------------:|:------------------------:|:-----------------:|:---------------:| | 0.0010 | 1 | 4.9603 | - | - | - | - | | 0.0020 | 2 | 28.2529 | - | - | - | - | | 0.0030 | 3 | 27.6365 | - | - | - | - | | 0.0039 | 4 | 6.1387 | - | - | - | - | | 0.0049 | 5 | 5.5753 | - | - | - | - | | 0.0059 | 6 | 5.6951 | - | - | - | - | | 0.0069 | 7 | 6.3533 | - | - | - | - | | 0.0079 | 8 | 27.3848 | - | - | - | - | | 0.0089 | 9 | 3.8501 | - | - | - | - | | 0.0098 | 10 | 27.911 | - | - | - | - | | 0.0108 | 11 | 4.9042 | - | - | - | - | | 0.0118 | 12 | 6.8003 | - | - | - | - | | 0.0128 | 13 | 5.7317 | - | - | - | - | | 0.0138 | 14 | 20.261 | - | - | - | - | | 0.0148 | 15 | 27.9051 | - | - | - | - | | 0.0157 | 16 | 5.5959 | - | - | - | - | | 0.0167 | 17 | 5.8052 | - | - | - | - | | 0.0177 | 18 | 4.5088 | - | - | - | - | | 0.0187 | 19 | 7.3472 | - | - | - | - | | 0.0197 | 20 | 5.8668 | - | - | - | - | | 0.0207 | 21 | 6.4083 | - | - | - | - | | 0.0217 | 22 | 6.011 | - | - | - | - | | 0.0226 | 23 | 5.2394 | - | - | - | - | | 0.0236 | 24 | 4.2966 | - | - | - | - | | 0.0246 | 25 | 26.605 | - | - | - | - | | 0.0256 | 26 | 6.2067 | - | - | - | - | | 0.0266 | 27 | 6.0346 | - | - | - | - | | 0.0276 | 28 | 5.4676 | - | - | - | - | | 0.0285 | 29 | 6.4292 | - | - | - | - | | 0.0295 | 30 | 26.6452 | - | - | - | - | | 0.0305 | 31 | 18.8401 | - | - | - | - | | 0.0315 | 32 | 7.4531 | - | - | - | - | | 0.0325 | 33 | 4.8286 | - | - | - | - | | 0.0335 | 34 | 5.0078 | - | - | - | - | | 0.0344 | 35 | 5.4115 | - | - | - | - | | 0.0354 | 36 | 5.4196 | - | - | - | - | | 0.0364 | 37 | 4.5023 | - | - | - | - | | 0.0374 | 38 | 5.376 | - | - | - | - | | 0.0384 | 39 | 5.2303 | - | - | - | - | | 0.0394 | 40 | 5.6694 | - | - | - | - | | 0.0404 | 41 | 4.7825 | - | - | - | - | | 0.0413 | 42 | 4.6507 | - | - | - | - | | 0.0423 | 43 | 24.2072 | - | - | - | - | | 0.0433 | 44 | 4.9285 | - | - | - | - | | 0.0443 | 45 | 6.326 | - | - | - | - | | 0.0453 | 46 | 4.5724 | - | - | - | - | | 0.0463 | 47 | 4.754 | - | - | - | - | | 0.0472 | 48 | 5.5443 | - | - | - | - | | 0.0482 | 49 | 4.5764 | - | - | - | - | | 0.0492 | 50 | 5.1434 | - | - | - | - | | 0.0502 | 51 | 22.6991 | - | - | - | - | | 0.0512 | 52 | 5.4277 | - | - | - | - | | 0.0522 | 53 | 5.0178 | - | - | - | - | | 0.0531 | 54 | 4.8779 | - | - | - | - | | 0.0541 | 55 | 4.2884 | - | - | - | - | | 0.0551 | 56 | 16.0994 | - | - | - | - | | 0.0561 | 57 | 21.31 | - | - | - | - | | 0.0571 | 58 | 4.9721 | - | - | - | - | | 0.0581 | 59 | 5.143 | - | - | - | - | | 0.0591 | 60 | 3.5933 | - | - | - | - | | 0.0600 | 61 | 5.2559 | - | - | - | - | | 0.0610 | 62 | 4.0757 | - | - | - | - | | 0.0620 | 63 | 3.6612 | - | - | - | - | | 0.0630 | 64 | 4.7505 | - | - | - | - | | 0.0640 | 65 | 4.1979 | - | - | - | - | | 0.0650 | 66 | 3.9982 | - | - | - | - | | 0.0659 | 67 | 4.7065 | - | - | - | - | | 0.0669 | 68 | 5.3413 | - | - | - | - | | 0.0679 | 69 | 3.6964 | - | - | - | - | | 0.0689 | 70 | 17.8774 | - | - | - | - | | 0.0699 | 71 | 4.8154 | - | - | - | - | | 0.0709 | 72 | 4.8356 | - | - | - | - | | 0.0719 | 73 | 4.568 | - | - | - | - | | 0.0728 | 74 | 4.0898 | - | - | - | - | | 0.0738 | 75 | 3.4502 | - | - | - | - | | 0.0748 | 76 | 3.7733 | - | - | - | - | | 0.0758 | 77 | 4.5204 | - | - | - | - | | 0.0768 | 78 | 4.2526 | - | - | - | - | | 0.0778 | 79 | 4.4398 | - | - | - | - | | 0.0787 | 80 | 4.0988 | - | - | - | - | | 0.0797 | 81 | 3.9704 | - | - | - | - | | 0.0807 | 82 | 4.3343 | - | - | - | - | | 0.0817 | 83 | 4.2587 | - | - | - | - | | 0.0827 | 84 | 15.0149 | - | - | - | - | | 0.0837 | 85 | 14.6599 | - | - | - | - | | 0.0846 | 86 | 4.0623 | - | - | - | - | | 0.0856 | 87 | 3.7597 | - | - | - | - | | 0.0866 | 88 | 4.3433 | - | - | - | - | | 0.0876 | 89 | 4.0287 | - | - | - | - | | 0.0886 | 90 | 4.6257 | - | - | - | - | | 0.0896 | 91 | 13.4689 | - | - | - | - | | 0.0906 | 92 | 4.6583 | - | - | - | - | | 0.0915 | 93 | 4.2682 | - | - | - | - | | 0.0925 | 94 | 4.468 | - | - | - | - | | 0.0935 | 95 | 3.4333 | - | - | - | - | | 0.0945 | 96 | 12.7654 | - | - | - | - | | 0.0955 | 97 | 3.5577 | - | - | - | - | | 0.0965 | 98 | 12.5875 | - | - | - | - | | 0.0974 | 99 | 4.2206 | - | - | - | - | | 0.0984 | 100 | 3.5981 | - | - | - | - | | 0.0994 | 101 | 3.5575 | - | - | - | - | | 0.1004 | 102 | 4.0271 | - | - | - | - | | 0.1014 | 103 | 4.0803 | - | - | - | - | | 0.1024 | 104 | 4.0886 | - | - | - | - | | 0.1033 | 105 | 4.176 | - | - | - | - | | 0.1043 | 106 | 4.6653 | - | - | - | - | | 0.1053 | 107 | 4.3076 | - | - | - | - | | 0.1063 | 108 | 8.7282 | - | - | - | - | | 0.1073 | 109 | 3.4192 | - | - | - | - | | 0.1083 | 110 | 10.6027 | - | - | - | - | | 0.1093 | 111 | 4.0959 | - | - | - | - | | 0.1102 | 112 | 4.2785 | - | - | - | - | | 0.1112 | 113 | 3.9945 | - | - | - | - | | 0.1122 | 114 | 10.0652 | - | - | - | - | | 0.1132 | 115 | 3.8621 | - | - | - | - | | 0.1142 | 116 | 4.3975 | - | - | - | - | | 0.1152 | 117 | 9.7899 | - | - | - | - | | 0.1161 | 118 | 4.3812 | - | - | - | - | | 0.1171 | 119 | 3.8715 | - | - | - | - | | 0.1181 | 120 | 3.8327 | - | - | - | - | | 0.1191 | 121 | 3.5103 | - | - | - | - | | 0.1201 | 122 | 9.3158 | - | - | - | - | | 0.1211 | 123 | 3.7201 | - | - | - | - | | 0.1220 | 124 | 3.4311 | - | - | - | - | | 0.1230 | 125 | 3.7946 | - | - | - | - | | 0.1240 | 126 | 4.0456 | - | - | - | - | | 0.125 | 127 | 3.482 | - | - | - | - | | 0.1260 | 128 | 3.1901 | - | - | - | - | | 0.1270 | 129 | 3.414 | - | - | - | - | | 0.1280 | 130 | 3.4967 | - | - | - | - | | 0.1289 | 131 | 3.6594 | - | - | - | - | | 0.1299 | 132 | 8.066 | - | - | - | - | | 0.1309 | 133 | 3.7872 | - | - | - | - | | 0.1319 | 134 | 4.0023 | - | - | - | - | | 0.1329 | 135 | 3.7728 | - | - | - | - | | 0.1339 | 136 | 3.1893 | - | - | - | - | | 0.1348 | 137 | 3.3635 | - | - | - | - | | 0.1358 | 138 | 4.0195 | - | - | - | - | | 0.1368 | 139 | 4.1097 | - | - | - | - | | 0.1378 | 140 | 3.7903 | - | - | - | - | | 0.1388 | 141 | 3.5748 | - | - | - | - | | 0.1398 | 142 | 3.8104 | - | - | - | - | | 0.1407 | 143 | 8.0411 | - | - | - | - | | 0.1417 | 144 | 3.4819 | - | - | - | - | | 0.1427 | 145 | 3.452 | - | - | - | - | | 0.1437 | 146 | 3.5861 | - | - | - | - | | 0.1447 | 147 | 3.4324 | - | - | - | - | | 0.1457 | 148 | 3.521 | - | - | - | - | | 0.1467 | 149 | 3.8868 | - | - | - | - | | 0.1476 | 150 | 8.1191 | - | - | - | - | | 0.1486 | 151 | 3.6447 | - | - | - | - | | 0.1496 | 152 | 2.9436 | - | - | - | - | | 0.1506 | 153 | 8.1535 | 2.2032 | 0.2236 | 0.4009 | 0.5892 | | 0.1516 | 154 | 3.9619 | - | - | - | - | | 0.1526 | 155 | 3.1301 | - | - | - | - | | 0.1535 | 156 | 3.0478 | - | - | - | - | | 0.1545 | 157 | 3.2986 | - | - | - | - | | 0.1555 | 158 | 3.2847 | - | - | - | - | | 0.1565 | 159 | 3.6599 | - | - | - | - | | 0.1575 | 160 | 3.2238 | - | - | - | - | | 0.1585 | 161 | 2.8897 | - | - | - | - | | 0.1594 | 162 | 3.9443 | - | - | - | - | | 0.1604 | 163 | 3.3733 | - | - | - | - | | 0.1614 | 164 | 3.7444 | - | - | - | - | | 0.1624 | 165 | 3.4813 | - | - | - | - | | 0.1634 | 166 | 2.6865 | - | - | - | - | | 0.1644 | 167 | 2.7587 | - | - | - | - | | 0.1654 | 168 | 3.3628 | - | - | - | - | | 0.1663 | 169 | 3.0035 | - | - | - | - | | 0.1673 | 170 | 10.1591 | - | - | - | - | | 0.1683 | 171 | 3.5366 | - | - | - | - | | 0.1693 | 172 | 8.4047 | - | - | - | - | | 0.1703 | 173 | 3.8643 | - | - | - | - | | 0.1713 | 174 | 3.3529 | - | - | - | - | | 0.1722 | 175 | 3.7143 | - | - | - | - | | 0.1732 | 176 | 3.3323 | - | - | - | - | | 0.1742 | 177 | 3.1206 | - | - | - | - | | 0.1752 | 178 | 3.1348 | - | - | - | - | | 0.1762 | 179 | 7.6011 | - | - | - | - | | 0.1772 | 180 | 3.7025 | - | - | - | - | | 0.1781 | 181 | 10.5662 | - | - | - | - | | 0.1791 | 182 | 8.966 | - | - | - | - | | 0.1801 | 183 | 9.426 | - | - | - | - | | 0.1811 | 184 | 3.0025 | - | - | - | - | | 0.1821 | 185 | 7.0984 | - | - | - | - | | 0.1831 | 186 | 7.3808 | - | - | - | - | | 0.1841 | 187 | 2.8657 | - | - | - | - | | 0.1850 | 188 | 6.5636 | - | - | - | - | | 0.1860 | 189 | 3.4702 | - | - | - | - | | 0.1870 | 190 | 5.9302 | - | - | - | - | | 0.1880 | 191 | 3.2406 | - | - | - | - | | 0.1890 | 192 | 3.4459 | - | - | - | - | | 0.1900 | 193 | 5.269 | - | - | - | - | | 0.1909 | 194 | 4.8605 | - | - | - | - | | 0.1919 | 195 | 2.9891 | - | - | - | - | | 0.1929 | 196 | 3.6681 | - | - | - | - | | 0.1939 | 197 | 3.1589 | - | - | - | - | | 0.1949 | 198 | 3.1835 | - | - | - | - | | 0.1959 | 199 | 3.7561 | - | - | - | - | | 0.1969 | 200 | 4.0891 | - | - | - | - | | 0.1978 | 201 | 3.563 | - | - | - | - | | 0.1988 | 202 | 3.7433 | - | - | - | - | | 0.1998 | 203 | 3.3813 | - | - | - | - | | 0.2008 | 204 | 5.2311 | - | - | - | - | | 0.2018 | 205 | 3.3494 | - | - | - | - | | 0.2028 | 206 | 3.3533 | - | - | - | - | | 0.2037 | 207 | 3.688 | - | - | - | - | | 0.2047 | 208 | 3.5342 | - | - | - | - | | 0.2057 | 209 | 4.9381 | - | - | - | - | | 0.2067 | 210 | 3.1839 | - | - | - | - | | 0.2077 | 211 | 3.0465 | - | - | - | - | | 0.2087 | 212 | 3.1232 | - | - | - | - | | 0.2096 | 213 | 4.6297 | - | - | - | - | | 0.2106 | 214 | 2.9834 | - | - | - | - | | 0.2116 | 215 | 4.2231 | - | - | - | - | | 0.2126 | 216 | 3.1458 | - | - | - | - | | 0.2136 | 217 | 3.2525 | - | - | - | - | | 0.2146 | 218 | 3.5971 | - | - | - | - | | 0.2156 | 219 | 3.5616 | - | - | - | - | | 0.2165 | 220 | 3.2378 | - | - | - | - | | 0.2175 | 221 | 2.9075 | - | - | - | - | | 0.2185 | 222 | 3.0391 | - | - | - | - | | 0.2195 | 223 | 3.5573 | - | - | - | - | | 0.2205 | 224 | 3.2092 | - | - | - | - | | 0.2215 | 225 | 3.2646 | - | - | - | - | | 0.2224 | 226 | 3.0886 | - | - | - | - | | 0.2234 | 227 | 3.5241 | - | - | - | - | | 0.2244 | 228 | 3.0111 | - | - | - | - | | 0.2254 | 229 | 3.707 | - | - | - | - | | 0.2264 | 230 | 5.3822 | - | - | - | - | | 0.2274 | 231 | 3.2646 | - | - | - | - | | 0.2283 | 232 | 2.7021 | - | - | - | - | | 0.2293 | 233 | 3.5131 | - | - | - | - | | 0.2303 | 234 | 3.103 | - | - | - | - | | 0.2313 | 235 | 2.9535 | - | - | - | - | | 0.2323 | 236 | 2.9631 | - | - | - | - | | 0.2333 | 237 | 2.8068 | - | - | - | - | | 0.2343 | 238 | 3.4251 | - | - | - | - | | 0.2352 | 239 | 2.8495 | - | - | - | - | | 0.2362 | 240 | 2.9972 | - | - | - | - | | 0.2372 | 241 | 3.3509 | - | - | - | - | | 0.2382 | 242 | 2.9234 | - | - | - | - | | 0.2392 | 243 | 2.4086 | - | - | - | - | | 0.2402 | 244 | 3.1282 | - | - | - | - | | 0.2411 | 245 | 2.3352 | - | - | - | - | | 0.2421 | 246 | 2.4706 | - | - | - | - | | 0.2431 | 247 | 3.5449 | - | - | - | - | | 0.2441 | 248 | 2.8963 | - | - | - | - | | 0.2451 | 249 | 2.773 | - | - | - | - | | 0.2461 | 250 | 2.355 | - | - | - | - | | 0.2470 | 251 | 2.656 | - | - | - | - | | 0.2480 | 252 | 2.6221 | - | - | - | - | | 0.2490 | 253 | 8.6739 | - | - | - | - | | 0.25 | 254 | 10.8242 | - | - | - | - | | 0.2510 | 255 | 2.3408 | - | - | - | - | | 0.2520 | 256 | 2.1221 | - | - | - | - | | 0.2530 | 257 | 3.295 | - | - | - | - | | 0.2539 | 258 | 2.5896 | - | - | - | - | | 0.2549 | 259 | 2.1215 | - | - | - | - | | 0.2559 | 260 | 9.4851 | - | - | - | - | | 0.2569 | 261 | 2.1982 | - | - | - | - | | 0.2579 | 262 | 3.0568 | - | - | - | - | | 0.2589 | 263 | 2.6269 | - | - | - | - | | 0.2598 | 264 | 2.4792 | - | - | - | - | | 0.2608 | 265 | 1.9445 | - | - | - | - | | 0.2618 | 266 | 2.4061 | - | - | - | - | | 0.2628 | 267 | 8.3116 | - | - | - | - | | 0.2638 | 268 | 8.0804 | - | - | - | - | | 0.2648 | 269 | 2.1674 | - | - | - | - | | 0.2657 | 270 | 7.1975 | - | - | - | - | | 0.2667 | 271 | 5.9104 | - | - | - | - | | 0.2677 | 272 | 2.498 | - | - | - | - | | 0.2687 | 273 | 2.5249 | - | - | - | - | | 0.2697 | 274 | 2.7152 | - | - | - | - | | 0.2707 | 275 | 2.7904 | - | - | - | - | | 0.2717 | 276 | 2.7745 | - | - | - | - | | 0.2726 | 277 | 2.9741 | - | - | - | - | | 0.2736 | 278 | 1.8215 | - | - | - | - | | 0.2746 | 279 | 4.6844 | - | - | - | - | | 0.2756 | 280 | 2.8613 | - | - | - | - | | 0.2766 | 281 | 2.7147 | - | - | - | - | | 0.2776 | 282 | 2.814 | - | - | - | - | | 0.2785 | 283 | 2.3569 | - | - | - | - | | 0.2795 | 284 | 2.672 | - | - | - | - | | 0.2805 | 285 | 3.2052 | - | - | - | - | | 0.2815 | 286 | 2.8056 | - | - | - | - | | 0.2825 | 287 | 2.6268 | - | - | - | - | | 0.2835 | 288 | 2.5641 | - | - | - | - | | 0.2844 | 289 | 2.4475 | - | - | - | - | | 0.2854 | 290 | 2.7377 | - | - | - | - | | 0.2864 | 291 | 2.3831 | - | - | - | - | | 0.2874 | 292 | 8.8069 | - | - | - | - | | 0.2884 | 293 | 2.186 | - | - | - | - | | 0.2894 | 294 | 2.3389 | - | - | - | - | | 0.2904 | 295 | 1.9744 | - | - | - | - | | 0.2913 | 296 | 2.4491 | - | - | - | - | | 0.2923 | 297 | 2.5668 | - | - | - | - | | 0.2933 | 298 | 2.1939 | - | - | - | - | | 0.2943 | 299 | 2.2832 | - | - | - | - | | 0.2953 | 300 | 2.7508 | - | - | - | - | | 0.2963 | 301 | 2.5206 | - | - | - | - | | 0.2972 | 302 | 2.3522 | - | - | - | - | | 0.2982 | 303 | 2.7186 | - | - | - | - | | 0.2992 | 304 | 2.1369 | - | - | - | - | | 0.3002 | 305 | 9.7972 | - | - | - | - | | 0.3012 | 306 | 1.9378 | 1.5786 | 0.2924 | 0.4272 | 0.6159 | | 0.3022 | 307 | 2.5365 | - | - | - | - | | 0.3031 | 308 | 2.0346 | - | - | - | - | | 0.3041 | 309 | 2.0721 | - | - | - | - | | 0.3051 | 310 | 2.6966 | - | - | - | - | | 0.3061 | 311 | 2.6757 | - | - | - | - | | 0.3071 | 312 | 10.6395 | - | - | - | - | | 0.3081 | 313 | 2.8671 | - | - | - | - | | 0.3091 | 314 | 2.0144 | - | - | - | - | | 0.3100 | 315 | 9.9338 | - | - | - | - | | 0.3110 | 316 | 2.6167 | - | - | - | - | | 0.3120 | 317 | 2.1342 | - | - | - | - | | 0.3130 | 318 | 9.0369 | - | - | - | - | | 0.3140 | 319 | 2.0182 | - | - | - | - | | 0.3150 | 320 | 2.2189 | - | - | - | - | | 0.3159 | 321 | 1.9667 | - | - | - | - | | 0.3169 | 322 | 2.3371 | - | - | - | - | | 0.3179 | 323 | 6.9866 | - | - | - | - | | 0.3189 | 324 | 1.6119 | - | - | - | - | | 0.3199 | 325 | 1.8615 | - | - | - | - | | 0.3209 | 326 | 2.1708 | - | - | - | - | | 0.3219 | 327 | 2.0174 | - | - | - | - | | 0.3228 | 328 | 6.7891 | - | - | - | - | | 0.3238 | 329 | 2.155 | - | - | - | - | | 0.3248 | 330 | 2.4636 | - | - | - | - | | 0.3258 | 331 | 1.9844 | - | - | - | - | | 0.3268 | 332 | 1.9035 | - | - | - | - | | 0.3278 | 333 | 2.0729 | - | - | - | - | | 0.3287 | 334 | 1.5715 | - | - | - | - | | 0.3297 | 335 | 2.7211 | - | - | - | - | | 0.3307 | 336 | 2.0351 | - | - | - | - | | 0.3317 | 337 | 2.4049 | - | - | - | - | | 0.3327 | 338 | 2.3939 | - | - | - | - | | 0.3337 | 339 | 1.7353 | - | - | - | - | | 0.3346 | 340 | 1.8393 | - | - | - | - | | 0.3356 | 341 | 2.2874 | - | - | - | - | | 0.3366 | 342 | 1.8566 | - | - | - | - | | 0.3376 | 343 | 2.2676 | - | - | - | - | | 0.3386 | 344 | 1.7895 | - | - | - | - | | 0.3396 | 345 | 2.2506 | - | - | - | - | | 0.3406 | 346 | 1.5613 | - | - | - | - | | 0.3415 | 347 | 2.3531 | - | - | - | - | | 0.3425 | 348 | 1.99 | - | - | - | - | | 0.3435 | 349 | 12.0831 | - | - | - | - | | 0.3445 | 350 | 2.0959 | - | - | - | - | | 0.3455 | 351 | 2.0641 | - | - | - | - | | 0.3465 | 352 | 1.9197 | - | - | - | - | | 0.3474 | 353 | 1.9382 | - | - | - | - | | 0.3484 | 354 | 2.3819 | - | - | - | - | | 0.3494 | 355 | 1.6053 | - | - | - | - | | 0.3504 | 356 | 2.4719 | - | - | - | - | | 0.3514 | 357 | 1.5602 | - | - | - | - | | 0.3524 | 358 | 2.1675 | - | - | - | - | | 0.3533 | 359 | 11.5856 | - | - | - | - | | 0.3543 | 360 | 9.3718 | - | - | - | - | | 0.3553 | 361 | 1.8952 | - | - | - | - | | 0.3563 | 362 | 1.701 | - | - | - | - | | 0.3573 | 363 | 1.46 | - | - | - | - | | 0.3583 | 364 | 1.7913 | - | - | - | - | | 0.3593 | 365 | 9.1152 | - | - | - | - | | 0.3602 | 366 | 9.2681 | - | - | - | - | | 0.3612 | 367 | 2.2932 | - | - | - | - | | 0.3622 | 368 | 1.7176 | - | - | - | - | | 0.3632 | 369 | 2.2559 | - | - | - | - | | 0.3642 | 370 | 1.9846 | - | - | - | - | | 0.3652 | 371 | 1.8022 | - | - | - | - | | 0.3661 | 372 | 8.1128 | - | - | - | - | | 0.3671 | 373 | 6.929 | - | - | - | - | | 0.3681 | 374 | 1.9038 | - | - | - | - | | 0.3691 | 375 | 1.3899 | - | - | - | - | | 0.3701 | 376 | 1.5677 | - | - | - | - | | 0.3711 | 377 | 5.2357 | - | - | - | - | | 0.3720 | 378 | 2.2304 | - | - | - | - | | 0.3730 | 379 | 2.1727 | - | - | - | - | | 0.3740 | 380 | 2.2941 | - | - | - | - | | 0.375 | 381 | 2.2257 | - | - | - | - | | 0.3760 | 382 | 1.7489 | - | - | - | - | | 0.3770 | 383 | 1.5027 | - | - | - | - | | 0.3780 | 384 | 1.6917 | - | - | - | - | | 0.3789 | 385 | 5.7867 | - | - | - | - | | 0.3799 | 386 | 1.6871 | - | - | - | - | | 0.3809 | 387 | 1.5652 | - | - | - | - | | 0.3819 | 388 | 2.1691 | - | - | - | - | | 0.3829 | 389 | 1.869 | - | - | - | - | | 0.3839 | 390 | 2.1934 | - | - | - | - | | 0.3848 | 391 | 7.0152 | - | - | - | - | | 0.3858 | 392 | 2.0454 | - | - | - | - | | 0.3868 | 393 | 1.8098 | - | - | - | - | | 0.3878 | 394 | 5.7529 | - | - | - | - | | 0.3888 | 395 | 1.3949 | - | - | - | - | | 0.3898 | 396 | 1.5962 | - | - | - | - | | 0.3907 | 397 | 6.1436 | - | - | - | - | | 0.3917 | 398 | 5.2979 | - | - | - | - | | 0.3927 | 399 | 1.2422 | - | - | - | - | | 0.3937 | 400 | 2.1152 | - | - | - | - | | 0.3947 | 401 | 1.6679 | - | - | - | - | | 0.3957 | 402 | 4.2978 | - | - | - | - | | 0.3967 | 403 | 1.624 | - | - | - | - | | 0.3976 | 404 | 2.0267 | - | - | - | - | | 0.3986 | 405 | 1.3975 | - | - | - | - | | 0.3996 | 406 | 1.905 | - | - | - | - | | 0.4006 | 407 | 5.4419 | - | - | - | - | | 0.4016 | 408 | 2.0008 | - | - | - | - | | 0.4026 | 409 | 1.8387 | - | - | - | - | | 0.4035 | 410 | 2.2391 | - | - | - | - | | 0.4045 | 411 | 1.7153 | - | - | - | - | | 0.4055 | 412 | 2.1533 | - | - | - | - | | 0.4065 | 413 | 1.788 | - | - | - | - | | 0.4075 | 414 | 3.482 | - | - | - | - | | 0.4085 | 415 | 1.8376 | - | - | - | - | | 0.4094 | 416 | 4.8811 | - | - | - | - | | 0.4104 | 417 | 1.9421 | - | - | - | - | | 0.4114 | 418 | 1.4796 | - | - | - | - | | 0.4124 | 419 | 1.6209 | - | - | - | - | | 0.4134 | 420 | 1.8734 | - | - | - | - | | 0.4144 | 421 | 1.9444 | - | - | - | - | | 0.4154 | 422 | 1.9581 | - | - | - | - | | 0.4163 | 423 | 1.5175 | - | - | - | - | | 0.4173 | 424 | 1.2831 | - | - | - | - | | 0.4183 | 425 | 1.1355 | - | - | - | - | | 0.4193 | 426 | 1.864 | - | - | - | - | | 0.4203 | 427 | 5.1574 | - | - | - | - | | 0.4213 | 428 | 5.323 | - | - | - | - | | 0.4222 | 429 | 1.385 | - | - | - | - | | 0.4232 | 430 | 1.1691 | - | - | - | - | | 0.4242 | 431 | 1.8994 | - | - | - | - | | 0.4252 | 432 | 5.4254 | - | - | - | - | | 0.4262 | 433 | 1.9113 | - | - | - | - | | 0.4272 | 434 | 2.1108 | - | - | - | - | | 0.4281 | 435 | 1.7012 | - | - | - | - | | 0.4291 | 436 | 1.5722 | - | - | - | - | | 0.4301 | 437 | 1.5967 | - | - | - | - | | 0.4311 | 438 | 5.609 | - | - | - | - | | 0.4321 | 439 | 1.4444 | - | - | - | - | | 0.4331 | 440 | 5.3153 | - | - | - | - | | 0.4341 | 441 | 5.0934 | - | - | - | - | | 0.4350 | 442 | 1.3028 | - | - | - | - | | 0.4360 | 443 | 1.263 | - | - | - | - | | 0.4370 | 444 | 1.8462 | - | - | - | - | | 0.4380 | 445 | 2.1533 | - | - | - | - | | 0.4390 | 446 | 1.5467 | - | - | - | - | | 0.4400 | 447 | 1.4331 | - | - | - | - | | 0.4409 | 448 | 1.4416 | - | - | - | - | | 0.4419 | 449 | 1.5976 | - | - | - | - | | 0.4429 | 450 | 1.8723 | - | - | - | - | | 0.4439 | 451 | 1.1753 | - | - | - | - | | 0.4449 | 452 | 2.3205 | - | - | - | - | | 0.4459 | 453 | 1.6467 | - | - | - | - | | 0.4469 | 454 | 0.9322 | - | - | - | - | | 0.4478 | 455 | 1.958 | - | - | - | - | | 0.4488 | 456 | 1.8746 | - | - | - | - | | 0.4498 | 457 | 1.4546 | - | - | - | - | | 0.4508 | 458 | 0.9795 | - | - | - | - | | 0.4518 | 459 | 1.5458 | 1.2676 | 0.2751 | 0.4485 | 0.6433 | | 0.4528 | 460 | 1.6558 | - | - | - | - | | 0.4537 | 461 | 1.389 | - | - | - | - | | 0.4547 | 462 | 1.5608 | - | - | - | - | | 0.4557 | 463 | 1.6618 | - | - | - | - | | 0.4567 | 464 | 1.5122 | - | - | - | - | | 0.4577 | 465 | 1.3602 | - | - | - | - | | 0.4587 | 466 | 1.6714 | - | - | - | - | | 0.4596 | 467 | 1.0644 | - | - | - | - | | 0.4606 | 468 | 7.6421 | - | - | - | - | | 0.4616 | 469 | 1.2987 | - | - | - | - | | 0.4626 | 470 | 1.4231 | - | - | - | - | | 0.4636 | 471 | 7.7424 | - | - | - | - | | 0.4646 | 472 | 1.6811 | - | - | - | - | | 0.4656 | 473 | 1.1814 | - | - | - | - | | 0.4665 | 474 | 1.4486 | - | - | - | - | | 0.4675 | 475 | 1.3892 | - | - | - | - | | 0.4685 | 476 | 1.3681 | - | - | - | - | | 0.4695 | 477 | 1.3081 | - | - | - | - | | 0.4705 | 478 | 0.9102 | - | - | - | - | | 0.4715 | 479 | 1.0992 | - | - | - | - | | 0.4724 | 480 | 6.018 | - | - | - | - | | 0.4734 | 481 | 6.0908 | - | - | - | - | | 0.4744 | 482 | 1.2245 | - | - | - | - | | 0.4754 | 483 | 1.4825 | - | - | - | - | | 0.4764 | 484 | 1.8037 | - | - | - | - | | 0.4774 | 485 | 1.3611 | - | - | - | - | | 0.4783 | 486 | 1.7482 | - | - | - | - | | 0.4793 | 487 | 1.6385 | - | - | - | - | | 0.4803 | 488 | 1.3245 | - | - | - | - | | 0.4813 | 489 | 1.5638 | - | - | - | - | | 0.4823 | 490 | 1.566 | - | - | - | - | | 0.4833 | 491 | 1.9482 | - | - | - | - | | 0.4843 | 492 | 6.0859 | - | - | - | - | | 0.4852 | 493 | 5.8754 | - | - | - | - | | 0.4862 | 494 | 0.9964 | - | - | - | - | | 0.4872 | 495 | 1.5949 | - | - | - | - | | 0.4882 | 496 | 1.3167 | - | - | - | - | | 0.4892 | 497 | 3.9345 | - | - | - | - | | 0.4902 | 498 | 4.3886 | - | - | - | - | | 0.4911 | 499 | 1.6124 | - | - | - | - | | 0.4921 | 500 | 1.2145 | - | - | - | - | | 0.4931 | 501 | 3.5499 | - | - | - | - | | 0.4941 | 502 | 1.2999 | - | - | - | - | | 0.4951 | 503 | 1.2375 | - | - | - | - | | 0.4961 | 504 | 1.1606 | - | - | - | - | | 0.4970 | 505 | 1.4634 | - | - | - | - | | 0.4980 | 506 | 1.35 | - | - | - | - | | 0.4990 | 507 | 1.7187 | - | - | - | - | | 0.5 | 508 | 1.5915 | - | - | - | - | | 0.5010 | 509 | 1.2357 | - | - | - | - | | 0.5020 | 510 | 3.4122 | - | - | - | - | | 0.5030 | 511 | 4.244 | - | - | - | - | | 0.5039 | 512 | 0.9151 | - | - | - | - | | 0.5049 | 513 | 1.4323 | - | - | - | - | | 0.5059 | 514 | 1.4824 | - | - | - | - | | 0.5069 | 515 | 1.339 | - | - | - | - | | 0.5079 | 516 | 4.1658 | - | - | - | - | | 0.5089 | 517 | 1.3062 | - | - | - | - | | 0.5098 | 518 | 1.2905 | - | - | - | - | | 0.5108 | 519 | 1.1487 | - | - | - | - | | 0.5118 | 520 | 2.8652 | - | - | - | - | | 0.5128 | 521 | 1.2634 | - | - | - | - | | 0.5138 | 522 | 1.6745 | - | - | - | - | | 0.5148 | 523 | 1.6548 | - | - | - | - | | 0.5157 | 524 | 2.4204 | - | - | - | - | | 0.5167 | 525 | 1.7201 | - | - | - | - | | 0.5177 | 526 | 1.761 | - | - | - | - | | 0.5187 | 527 | 2.7098 | - | - | - | - | | 0.5197 | 528 | 1.6425 | - | - | - | - | | 0.5207 | 529 | 1.2466 | - | - | - | - | | 0.5217 | 530 | 1.3339 | - | - | - | - | | 0.5226 | 531 | 1.2398 | - | - | - | - | | 0.5236 | 532 | 3.5325 | - | - | - | - | | 0.5246 | 533 | 1.1303 | - | - | - | - | | 0.5256 | 534 | 1.2601 | - | - | - | - | | 0.5266 | 535 | 1.5762 | - | - | - | - | | 0.5276 | 536 | 1.3992 | - | - | - | - | | 0.5285 | 537 | 1.7125 | - | - | - | - | | 0.5295 | 538 | 3.6759 | - | - | - | - | | 0.5305 | 539 | 1.5468 | - | - | - | - | | 0.5315 | 540 | 1.4316 | - | - | - | - | | 0.5325 | 541 | 1.2797 | - | - | - | - | | 0.5335 | 542 | 1.9122 | - | - | - | - | | 0.5344 | 543 | 2.0367 | - | - | - | - | | 0.5354 | 544 | 3.3029 | - | - | - | - | | 0.5364 | 545 | 3.9263 | - | - | - | - | | 0.5374 | 546 | 3.0101 | - | - | - | - | | 0.5384 | 547 | 3.3555 | - | - | - | - | | 0.5394 | 548 | 1.2068 | - | - | - | - | | 0.5404 | 549 | 1.1566 | - | - | - | - | | 0.5413 | 550 | 1.2773 | - | - | - | - | | 0.5423 | 551 | 1.4047 | - | - | - | - | | 0.5433 | 552 | 1.6048 | - | - | - | - | | 0.5443 | 553 | 1.217 | - | - | - | - | | 0.5453 | 554 | 1.8104 | - | - | - | - | | 0.5463 | 555 | 1.687 | - | - | - | - | | 0.5472 | 556 | 1.6702 | - | - | - | - | | 0.5482 | 557 | 1.7011 | - | - | - | - | | 0.5492 | 558 | 1.7341 | - | - | - | - | | 0.5502 | 559 | 1.5006 | - | - | - | - | | 0.5512 | 560 | 1.2778 | - | - | - | - | | 0.5522 | 561 | 1.5081 | - | - | - | - | | 0.5531 | 562 | 1.2398 | - | - | - | - | | 0.5541 | 563 | 1.1054 | - | - | - | - | | 0.5551 | 564 | 4.0185 | - | - | - | - | | 0.5561 | 565 | 1.0427 | - | - | - | - | | 0.5571 | 566 | 1.3934 | - | - | - | - | | 0.5581 | 567 | 1.2378 | - | - | - | - | | 0.5591 | 568 | 1.022 | - | - | - | - | | 0.5600 | 569 | 0.9001 | - | - | - | - | | 0.5610 | 570 | 1.3279 | - | - | - | - | | 0.5620 | 571 | 1.2889 | - | - | - | - | | 0.5630 | 572 | 0.9383 | - | - | - | - | | 0.5640 | 573 | 1.749 | - | - | - | - | | 0.5650 | 574 | 0.7669 | - | - | - | - | | 0.5659 | 575 | 0.9355 | - | - | - | - | | 0.5669 | 576 | 1.3596 | - | - | - | - | | 0.5679 | 577 | 5.5102 | - | - | - | - | | 0.5689 | 578 | 0.7984 | - | - | - | - | | 0.5699 | 579 | 0.8871 | - | - | - | - | | 0.5709 | 580 | 1.1151 | - | - | - | - | | 0.5719 | 581 | 0.9502 | - | - | - | - | | 0.5728 | 582 | 3.6492 | - | - | - | - | | 0.5738 | 583 | 3.4262 | - | - | - | - | | 0.5748 | 584 | 1.3362 | - | - | - | - | | 0.5758 | 585 | 0.9015 | - | - | - | - | | 0.5768 | 586 | 1.5884 | - | - | - | - | | 0.5778 | 587 | 1.109 | - | - | - | - | | 0.5787 | 588 | 1.041 | - | - | - | - | | 0.5797 | 589 | 1.4892 | - | - | - | - | | 0.5807 | 590 | 1.2623 | - | - | - | - | | 0.5817 | 591 | 1.5302 | - | - | - | - | | 0.5827 | 592 | 1.3517 | - | - | - | - | | 0.5837 | 593 | 0.6166 | - | - | - | - | | 0.5846 | 594 | 1.6761 | - | - | - | - | | 0.5856 | 595 | 1.1115 | - | - | - | - | | 0.5866 | 596 | 1.2945 | - | - | - | - | | 0.5876 | 597 | 1.4378 | - | - | - | - | | 0.5886 | 598 | 0.9928 | - | - | - | - | | 0.5896 | 599 | 0.9898 | - | - | - | - | | 0.5906 | 600 | 4.6887 | - | - | - | - | | 0.5915 | 601 | 1.2254 | - | - | - | - | | 0.5925 | 602 | 1.2707 | - | - | - | - | | 0.5935 | 603 | 1.8289 | - | - | - | - | | 0.5945 | 604 | 0.7801 | - | - | - | - | | 0.5955 | 605 | 0.9111 | - | - | - | - | | 0.5965 | 606 | 1.1405 | - | - | - | - | | 0.5974 | 607 | 1.0497 | - | - | - | - | | 0.5984 | 608 | 1.0792 | - | - | - | - | | 0.5994 | 609 | 0.9699 | - | - | - | - | | 0.6004 | 610 | 0.9398 | - | - | - | - | | 0.6014 | 611 | 1.5483 | - | - | - | - | | 0.6024 | 612 | 0.997 | 1.0047 | 0.3980 | 0.4554 | 0.6701 | | 0.6033 | 613 | 0.8358 | - | - | - | - | | 0.6043 | 614 | 1.211 | - | - | - | - | | 0.6053 | 615 | 6.7813 | - | - | - | - | | 0.6063 | 616 | 1.1229 | - | - | - | - | | 0.6073 | 617 | 1.0317 | - | - | - | - | | 0.6083 | 618 | 1.2123 | - | - | - | - | | 0.6093 | 619 | 1.4073 | - | - | - | - | | 0.6102 | 620 | 0.9951 | - | - | - | - | | 0.6112 | 621 | 1.3166 | - | - | - | - | | 0.6122 | 622 | 4.5204 | - | - | - | - | | 0.6132 | 623 | 0.6539 | - | - | - | - | | 0.6142 | 624 | 1.1959 | - | - | - | - | | 0.6152 | 625 | 4.2551 | - | - | - | - | | 0.6161 | 626 | 1.2459 | - | - | - | - | | 0.6171 | 627 | 1.3758 | - | - | - | - | | 0.6181 | 628 | 1.0524 | - | - | - | - | | 0.6191 | 629 | 1.5197 | - | - | - | - | | 0.6201 | 630 | 1.0201 | - | - | - | - | | 0.6211 | 631 | 0.9007 | - | - | - | - | | 0.6220 | 632 | 0.8418 | - | - | - | - | | 0.6230 | 633 | 1.4343 | - | - | - | - | | 0.6240 | 634 | 0.5292 | - | - | - | - | | 0.625 | 635 | 0.8549 | - | - | - | - | | 0.6260 | 636 | 0.8703 | - | - | - | - | | 0.6270 | 637 | 0.9911 | - | - | - | - | | 0.6280 | 638 | 1.3342 | - | - | - | - | | 0.6289 | 639 | 1.1332 | - | - | - | - | | 0.6299 | 640 | 3.9965 | - | - | - | - | | 0.6309 | 641 | 0.7236 | - | - | - | - | | 0.6319 | 642 | 0.9079 | - | - | - | - | | 0.6329 | 643 | 1.0967 | - | - | - | - | | 0.6339 | 644 | 1.4183 | - | - | - | - | | 0.6348 | 645 | 1.3841 | - | - | - | - | | 0.6358 | 646 | 1.2982 | - | - | - | - | | 0.6368 | 647 | 0.9048 | - | - | - | - | | 0.6378 | 648 | 0.7918 | - | - | - | - | | 0.6388 | 649 | 0.3685 | - | - | - | - | | 0.6398 | 650 | 0.6949 | - | - | - | - | | 0.6407 | 651 | 5.1568 | - | - | - | - | | 0.6417 | 652 | 1.3943 | - | - | - | - | | 0.6427 | 653 | 0.8608 | - | - | - | - | | 0.6437 | 654 | 0.8197 | - | - | - | - | | 0.6447 | 655 | 0.822 | - | - | - | - | | 0.6457 | 656 | 3.2918 | - | - | - | - | | 0.6467 | 657 | 0.5596 | - | - | - | - | | 0.6476 | 658 | 4.1499 | - | - | - | - | | 0.6486 | 659 | 1.0279 | - | - | - | - | | 0.6496 | 660 | 1.1506 | - | - | - | - | | 0.6506 | 661 | 1.1673 | - | - | - | - | | 0.6516 | 662 | 0.96 | - | - | - | - | | 0.6526 | 663 | 3.5414 | - | - | - | - | | 0.6535 | 664 | 0.6599 | - | - | - | - | | 0.6545 | 665 | 3.5518 | - | - | - | - | | 0.6555 | 666 | 1.1906 | - | - | - | - | | 0.6565 | 667 | 2.1353 | - | - | - | - | | 0.6575 | 668 | 0.7083 | - | - | - | - | | 0.6585 | 669 | 2.9425 | - | - | - | - | | 0.6594 | 670 | 0.9433 | - | - | - | - | | 0.6604 | 671 | 1.8499 | - | - | - | - | | 0.6614 | 672 | 1.1614 | - | - | - | - | | 0.6624 | 673 | 1.0474 | - | - | - | - | | 0.6634 | 674 | 1.2895 | - | - | - | - | | 0.6644 | 675 | 0.9789 | - | - | - | - | | 0.6654 | 676 | 0.7719 | - | - | - | - | | 0.6663 | 677 | 1.2203 | - | - | - | - | | 0.6673 | 678 | 1.0516 | - | - | - | - | | 0.6683 | 679 | 2.5514 | - | - | - | - | | 0.6693 | 680 | 0.7346 | - | - | - | - | | 0.6703 | 681 | 1.0245 | - | - | - | - | | 0.6713 | 682 | 2.8005 | - | - | - | - | | 0.6722 | 683 | 1.3212 | - | - | - | - | | 0.6732 | 684 | 0.95 | - | - | - | - | | 0.6742 | 685 | 1.0483 | - | - | - | - | | 0.6752 | 686 | 0.8504 | - | - | - | - | | 0.6762 | 687 | 2.281 | - | - | - | - | | 0.6772 | 688 | 1.8153 | - | - | - | - | | 0.6781 | 689 | 1.3652 | - | - | - | - | | 0.6791 | 690 | 1.0949 | - | - | - | - | | 0.6801 | 691 | 1.2196 | - | - | - | - | | 0.6811 | 692 | 0.7995 | - | - | - | - | | 0.6821 | 693 | 1.5108 | - | - | - | - | | 0.6831 | 694 | 0.7933 | - | - | - | - | | 0.6841 | 695 | 1.2367 | - | - | - | - | | 0.6850 | 696 | 1.0352 | - | - | - | - | | 0.6860 | 697 | 1.1709 | - | - | - | - | | 0.6870 | 698 | 1.452 | - | - | - | - | | 0.6880 | 699 | 0.8497 | - | - | - | - | | 0.6890 | 700 | 2.8109 | - | - | - | - | | 0.6900 | 701 | 2.6196 | - | - | - | - | | 0.6909 | 702 | 1.4556 | - | - | - | - | | 0.6919 | 703 | 1.3494 | - | - | - | - | | 0.6929 | 704 | 1.6624 | - | - | - | - | | 0.6939 | 705 | 1.6169 | - | - | - | - | | 0.6949 | 706 | 0.5565 | - | - | - | - | | 0.6959 | 707 | 0.8594 | - | - | - | - | | 0.6969 | 708 | 0.8551 | - | - | - | - | | 0.6978 | 709 | 1.1693 | - | - | - | - | | 0.6988 | 710 | 1.0514 | - | - | - | - | | 0.6998 | 711 | 1.1862 | - | - | - | - | | 0.7008 | 712 | 0.8359 | - | - | - | - | | 0.7018 | 713 | 0.7692 | - | - | - | - | | 0.7028 | 714 | 1.815 | - | - | - | - | | 0.7037 | 715 | 1.0751 | - | - | - | - | | 0.7047 | 716 | 0.6526 | - | - | - | - | | 0.7057 | 717 | 1.1617 | - | - | - | - | | 0.7067 | 718 | 1.0783 | - | - | - | - | | 0.7077 | 719 | 0.7916 | - | - | - | - | | 0.7087 | 720 | 1.3039 | - | - | - | - | | 0.7096 | 721 | 1.1156 | - | - | - | - | | 0.7106 | 722 | 1.0529 | - | - | - | - | | 0.7116 | 723 | 0.8265 | - | - | - | - | | 0.7126 | 724 | 0.8019 | - | - | - | - | | 0.7136 | 725 | 0.6116 | - | - | - | - | | 0.7146 | 726 | 1.135 | - | - | - | - | | 0.7156 | 727 | 0.7692 | - | - | - | - | | 0.7165 | 728 | 2.3559 | - | - | - | - | | 0.7175 | 729 | 1.352 | - | - | - | - | | 0.7185 | 730 | 2.823 | - | - | - | - | | 0.7195 | 731 | 1.0067 | - | - | - | - | | 0.7205 | 732 | 0.9077 | - | - | - | - | | 0.7215 | 733 | 1.0933 | - | - | - | - | | 0.7224 | 734 | 0.8174 | - | - | - | - | | 0.7234 | 735 | 1.2212 | - | - | - | - | | 0.7244 | 736 | 1.1557 | - | - | - | - | | 0.7254 | 737 | 0.6191 | - | - | - | - | | 0.7264 | 738 | 1.7437 | - | - | - | - | | 0.7274 | 739 | 0.8977 | - | - | - | - | | 0.7283 | 740 | 1.0782 | - | - | - | - | | 0.7293 | 741 | 0.8985 | - | - | - | - | | 0.7303 | 742 | 1.4867 | - | - | - | - | | 0.7313 | 743 | 0.7497 | - | - | - | - | | 0.7323 | 744 | 0.6433 | - | - | - | - | | 0.7333 | 745 | 1.4175 | - | - | - | - | | 0.7343 | 746 | 1.1896 | - | - | - | - | | 0.7352 | 747 | 1.9867 | - | - | - | - | | 0.7362 | 748 | 0.8968 | - | - | - | - | | 0.7372 | 749 | 0.7265 | - | - | - | - | | 0.7382 | 750 | 0.9418 | - | - | - | - | | 0.7392 | 751 | 1.3717 | - | - | - | - | | 0.7402 | 752 | 2.1774 | - | - | - | - | | 0.7411 | 753 | 1.0854 | - | - | - | - | | 0.7421 | 754 | 0.9777 | - | - | - | - | | 0.7431 | 755 | 1.2721 | - | - | - | - | | 0.7441 | 756 | 0.7114 | - | - | - | - | | 0.7451 | 757 | 1.4036 | - | - | - | - | | 0.7461 | 758 | 1.1742 | - | - | - | - | | 0.7470 | 759 | 0.9351 | - | - | - | - | | 0.7480 | 760 | 0.5537 | - | - | - | - | | 0.7490 | 761 | 0.8688 | - | - | - | - | | 0.75 | 762 | 3.0053 | - | - | - | - | | 0.7510 | 763 | 3.3743 | - | - | - | - | | 0.7520 | 764 | 1.9928 | - | - | - | - | | 0.7530 | 765 | 1.5118 | 0.9342 | 0.4514 | 0.4792 | 0.6782 | | 0.7539 | 766 | 1.1213 | - | - | - | - | | 0.7549 | 767 | 2.1312 | - | - | - | - | | 0.7559 | 768 | 1.3739 | - | - | - | - | | 0.7569 | 769 | 0.8819 | - | - | - | - | | 0.7579 | 770 | 0.9069 | - | - | - | - | | 0.7589 | 771 | 0.935 | - | - | - | - | | 0.7598 | 772 | 0.7874 | - | - | - | - | | 0.7608 | 773 | 1.9942 | - | - | - | - | | 0.7618 | 774 | 1.1711 | - | - | - | - | | 0.7628 | 775 | 0.8407 | - | - | - | - | | 0.7638 | 776 | 1.5171 | - | - | - | - | | 0.7648 | 777 | 0.5308 | - | - | - | - | | 0.7657 | 778 | 1.4107 | - | - | - | - | | 0.7667 | 779 | 1.1766 | - | - | - | - | | 0.7677 | 780 | 1.326 | - | - | - | - | | 0.7687 | 781 | 0.7371 | - | - | - | - | | 0.7697 | 782 | 1.0504 | - | - | - | - | | 0.7707 | 783 | 1.1458 | - | - | - | - | | 0.7717 | 784 | 0.7242 | - | - | - | - | | 0.7726 | 785 | 0.8113 | - | - | - | - | | 0.7736 | 786 | 1.3808 | - | - | - | - | | 0.7746 | 787 | 0.7584 | - | - | - | - | | 0.7756 | 788 | 1.226 | - | - | - | - | | 0.7766 | 789 | 1.0599 | - | - | - | - | | 0.7776 | 790 | 2.9348 | - | - | - | - | | 0.7785 | 791 | 1.0849 | - | - | - | - | | 0.7795 | 792 | 0.5362 | - | - | - | - | | 0.7805 | 793 | 1.3765 | - | - | - | - | | 0.7815 | 794 | 0.6824 | - | - | - | - | | 0.7825 | 795 | 0.6009 | - | - | - | - | | 0.7835 | 796 | 2.3853 | - | - | - | - | | 0.7844 | 797 | 1.0571 | - | - | - | - | | 0.7854 | 798 | 0.9172 | - | - | - | - | | 0.7864 | 799 | 0.7915 | - | - | - | - | | 0.7874 | 800 | 0.827 | - | - | - | - | | 0.7884 | 801 | 0.8465 | - | - | - | - | | 0.7894 | 802 | 2.3489 | - | - | - | - | | 0.7904 | 803 | 0.6506 | - | - | - | - | | 0.7913 | 804 | 0.8346 | - | - | - | - | | 0.7923 | 805 | 0.6249 | - | - | - | - | | 0.7933 | 806 | 1.0557 | - | - | - | - | | 0.7943 | 807 | 0.7552 | - | - | - | - | | 0.7953 | 808 | 1.281 | - | - | - | - | | 0.7963 | 809 | 0.7846 | - | - | - | - | | 0.7972 | 810 | 2.6403 | - | - | - | - | | 0.7982 | 811 | 0.3679 | - | - | - | - | | 0.7992 | 812 | 1.9118 | - | - | - | - | | 0.8002 | 813 | 2.5911 | - | - | - | - | | 0.8012 | 814 | 1.1783 | - | - | - | - | | 0.8022 | 815 | 0.9347 | - | - | - | - | | 0.8031 | 816 | 0.5311 | - | - | - | - | | 0.8041 | 817 | 0.7092 | - | - | - | - | | 0.8051 | 818 | 0.8384 | - | - | - | - | | 0.8061 | 819 | 0.514 | - | - | - | - | | 0.8071 | 820 | 0.3638 | - | - | - | - | | 0.8081 | 821 | 1.9376 | - | - | - | - | | 0.8091 | 822 | 0.9177 | - | - | - | - | | 0.8100 | 823 | 0.8293 | - | - | - | - | | 0.8110 | 824 | 0.7269 | - | - | - | - | | 0.8120 | 825 | 0.664 | - | - | - | - | | 0.8130 | 826 | 0.6205 | - | - | - | - | | 0.8140 | 827 | 0.6562 | - | - | - | - | | 0.8150 | 828 | 0.6576 | - | - | - | - | | 0.8159 | 829 | 0.9931 | - | - | - | - | | 0.8169 | 830 | 1.1707 | - | - | - | - | | 0.8179 | 831 | 0.8635 | - | - | - | - | | 0.8189 | 832 | 0.7274 | - | - | - | - | | 0.8199 | 833 | 1.6808 | - | - | - | - | | 0.8209 | 834 | 1.8309 | - | - | - | - | | 0.8219 | 835 | 0.6191 | - | - | - | - | | 0.8228 | 836 | 1.0789 | - | - | - | - | | 0.8238 | 837 | 1.1637 | - | - | - | - | | 0.8248 | 838 | 0.7813 | - | - | - | - | | 0.8258 | 839 | 1.0403 | - | - | - | - | | 0.8268 | 840 | 0.7656 | - | - | - | - | | 0.8278 | 841 | 0.9994 | - | - | - | - | | 0.8287 | 842 | 1.009 | - | - | - | - | | 0.8297 | 843 | 0.8585 | - | - | - | - | | 0.8307 | 844 | 0.8847 | - | - | - | - | | 0.8317 | 845 | 0.8321 | - | - | - | - | | 0.8327 | 846 | 1.2605 | - | - | - | - | | 0.8337 | 847 | 1.0609 | - | - | - | - | | 0.8346 | 848 | 2.0115 | - | - | - | - | | 0.8356 | 849 | 1.2952 | - | - | - | - | | 0.8366 | 850 | 0.6999 | - | - | - | - | | 0.8376 | 851 | 0.7006 | - | - | - | - | | 0.8386 | 852 | 0.927 | - | - | - | - | | 0.8396 | 853 | 1.2083 | - | - | - | - | | 0.8406 | 854 | 0.608 | - | - | - | - | | 0.8415 | 855 | 0.8478 | - | - | - | - | | 0.8425 | 856 | 1.5731 | - | - | - | - | | 0.8435 | 857 | 1.6353 | - | - | - | - | | 0.8445 | 858 | 0.7862 | - | - | - | - | | 0.8455 | 859 | 0.8909 | - | - | - | - | | 0.8465 | 860 | 1.1719 | - | - | - | - | | 0.8474 | 861 | 1.2722 | - | - | - | - | | 0.8484 | 862 | 1.0022 | - | - | - | - | | 0.8494 | 863 | 1.5307 | - | - | - | - | | 0.8504 | 864 | 1.0162 | - | - | - | - | | 0.8514 | 865 | 0.6827 | - | - | - | - | | 0.8524 | 866 | 0.7744 | - | - | - | - | | 0.8533 | 867 | 1.2011 | - | - | - | - | | 0.8543 | 868 | 0.9219 | - | - | - | - | | 0.8553 | 869 | 0.7636 | - | - | - | - | | 0.8563 | 870 | 1.5061 | - | - | - | - | | 0.8573 | 871 | 1.5569 | - | - | - | - | | 0.8583 | 872 | 0.5896 | - | - | - | - | | 0.8593 | 873 | 1.1918 | - | - | - | - | | 0.8602 | 874 | 0.8572 | - | - | - | - | | 0.8612 | 875 | 1.0421 | - | - | - | - | | 0.8622 | 876 | 2.4599 | - | - | - | - | | 0.8632 | 877 | 0.55 | - | - | - | - | | 0.8642 | 878 | 1.2829 | - | - | - | - | | 0.8652 | 879 | 0.7808 | - | - | - | - | | 0.8661 | 880 | 1.7712 | - | - | - | - | | 0.8671 | 881 | 0.7456 | - | - | - | - | | 0.8681 | 882 | 1.2805 | - | - | - | - | | 0.8691 | 883 | 2.1927 | - | - | - | - | | 0.8701 | 884 | 0.855 | - | - | - | - | | 0.8711 | 885 | 0.667 | - | - | - | - | | 0.8720 | 886 | 1.1097 | - | - | - | - | | 0.8730 | 887 | 1.8795 | - | - | - | - | | 0.8740 | 888 | 0.6767 | - | - | - | - | | 0.875 | 889 | 0.7549 | - | - | - | - | | 0.8760 | 890 | 0.8616 | - | - | - | - | | 0.8770 | 891 | 1.9461 | - | - | - | - | | 0.8780 | 892 | 1.2694 | - | - | - | - | | 0.8789 | 893 | 1.825 | - | - | - | - | | 0.8799 | 894 | 0.9218 | - | - | - | - | | 0.8809 | 895 | 1.0297 | - | - | - | - | | 0.8819 | 896 | 0.609 | - | - | - | - | | 0.8829 | 897 | 0.9638 | - | - | - | - | | 0.8839 | 898 | 0.5521 | - | - | - | - | | 0.8848 | 899 | 1.3365 | - | - | - | - | | 0.8858 | 900 | 0.8443 | - | - | - | - | | 0.8868 | 901 | 0.7848 | - | - | - | - | | 0.8878 | 902 | 1.0733 | - | - | - | - | | 0.8888 | 903 | 0.5657 | - | - | - | - | | 0.8898 | 904 | 1.8081 | - | - | - | - | | 0.8907 | 905 | 0.8232 | - | - | - | - | | 0.8917 | 906 | 0.6159 | - | - | - | - | | 0.8927 | 907 | 0.9832 | - | - | - | - | | 0.8937 | 908 | 1.1375 | - | - | - | - | | 0.8947 | 909 | 1.4182 | - | - | - | - | | 0.8957 | 910 | 1.2287 | - | - | - | - | | 0.8967 | 911 | 1.0915 | - | - | - | - | | 0.8976 | 912 | 0.8116 | - | - | - | - | | 0.8986 | 913 | 0.6824 | - | - | - | - | | 0.8996 | 914 | 0.8888 | - | - | - | - | | 0.9006 | 915 | 0.5974 | - | - | - | - | | 0.9016 | 916 | 1.1766 | - | - | - | - | | 0.9026 | 917 | 0.9415 | - | - | - | - | | 0.9035 | 918 | 0.6387 | 0.7856 | 0.5147 | 0.4835 | 0.6934 | | 0.9045 | 919 | 0.7342 | - | - | - | - | | 0.9055 | 920 | 1.2232 | - | - | - | - | | 0.9065 | 921 | 1.4883 | - | - | - | - | | 0.9075 | 922 | 1.4453 | - | - | - | - | | 0.9085 | 923 | 0.665 | - | - | - | - | | 0.9094 | 924 | 0.8973 | - | - | - | - | | 0.9104 | 925 | 0.7578 | - | - | - | - | | 0.9114 | 926 | 0.8693 | - | - | - | - | | 0.9124 | 927 | 1.0055 | - | - | - | - | | 0.9134 | 928 | 0.4451 | - | - | - | - | | 0.9144 | 929 | 1.3435 | - | - | - | - | | 0.9154 | 930 | 1.0979 | - | - | - | - | | 0.9163 | 931 | 1.0552 | - | - | - | - | | 0.9173 | 932 | 0.8224 | - | - | - | - | | 0.9183 | 933 | 2.824 | - | - | - | - | | 0.9193 | 934 | 1.3514 | - | - | - | - | | 0.9203 | 935 | 1.3339 | - | - | - | - | | 0.9213 | 936 | 0.8439 | - | - | - | - | | 0.9222 | 937 | 0.6325 | - | - | - | - | | 0.9232 | 938 | 0.7714 | - | - | - | - | | 0.9242 | 939 | 0.4552 | - | - | - | - | | 0.9252 | 940 | 1.3962 | - | - | - | - | | 0.9262 | 941 | 1.3079 | - | - | - | - | | 0.9272 | 942 | 0.8963 | - | - | - | - | | 0.9281 | 943 | 0.7712 | - | - | - | - | | 0.9291 | 944 | 0.7079 | - | - | - | - | | 0.9301 | 945 | 1.2151 | - | - | - | - | | 0.9311 | 946 | 0.5961 | - | - | - | - | | 0.9321 | 947 | 1.5555 | - | - | - | - | | 0.9331 | 948 | 0.6374 | - | - | - | - | | 0.9341 | 949 | 0.8514 | - | - | - | - | | 0.9350 | 950 | 1.0144 | - | - | - | - | | 0.9360 | 951 | 0.346 | - | - | - | - | | 0.9370 | 952 | 0.7938 | - | - | - | - | | 0.9380 | 953 | 0.7822 | - | - | - | - | | 0.9390 | 954 | 2.5079 | - | - | - | - | | 0.9400 | 955 | 0.4717 | - | - | - | - | | 0.9409 | 956 | 2.047 | - | - | - | - | | 0.9419 | 957 | 1.4548 | - | - | - | - | | 0.9429 | 958 | 0.6623 | - | - | - | - | | 0.9439 | 959 | 0.8172 | - | - | - | - | | 0.9449 | 960 | 0.9362 | - | - | - | - | | 0.9459 | 961 | 1.6731 | - | - | - | - | | 0.9469 | 962 | 0.4495 | - | - | - | - | | 0.9478 | 963 | 0.5375 | - | - | - | - | | 0.9488 | 964 | 1.3343 | - | - | - | - | | 0.9498 | 965 | 0.5332 | - | - | - | - | | 0.9508 | 966 | 1.0183 | - | - | - | - | | 0.9518 | 967 | 0.6058 | - | - | - | - | | 0.9528 | 968 | 0.6536 | - | - | - | - | | 0.9537 | 969 | 1.0448 | - | - | - | - | | 0.9547 | 970 | 0.9479 | - | - | - | - | | 0.9557 | 971 | 0.8316 | - | - | - | - | | 0.9567 | 972 | 1.0847 | - | - | - | - | | 0.9577 | 973 | 1.3262 | - | - | - | - | | 0.9587 | 974 | 0.6488 | - | - | - | - | | 0.9596 | 975 | 0.7577 | - | - | - | - | | 0.9606 | 976 | 1.0546 | - | - | - | - | | 0.9616 | 977 | 0.9759 | - | - | - | - | | 0.9626 | 978 | 0.526 | - | - | - | - | | 0.9636 | 979 | 0.9726 | - | - | - | - | | 0.9646 | 980 | 0.7035 | - | - | - | - | | 0.9656 | 981 | 0.4028 | - | - | - | - | | 0.9665 | 982 | 0.889 | - | - | - | - | | 0.9675 | 983 | 0.6391 | - | - | - | - | | 0.9685 | 984 | 2.2124 | - | - | - | - | | 0.9695 | 985 | 2.5108 | - | - | - | - | | 0.9705 | 986 | 0.5352 | - | - | - | - | | 0.9715 | 987 | 0.7982 | - | - | - | - | | 0.9724 | 988 | 0.8057 | - | - | - | - | | 0.9734 | 989 | 0.6363 | - | - | - | - | | 0.9744 | 990 | 1.4105 | - | - | - | - | | 0.9754 | 991 | 0.6527 | - | - | - | - | | 0.9764 | 992 | 0.7418 | - | - | - | - | | 0.9774 | 993 | 1.5734 | - | - | - | - | | 0.9783 | 994 | 0.512 | - | - | - | - | | 0.9793 | 995 | 0.7346 | - | - | - | - | | 0.9803 | 996 | 0.6094 | - | - | - | - | | 0.9813 | 997 | 0.9234 | - | - | - | - | | 0.9823 | 998 | 2.1518 | - | - | - | - | | 0.9833 | 999 | 0.458 | - | - | - | - | | 0.9843 | 1000 | 1.0281 | - | - | - | - | | 0.9852 | 1001 | 0.735 | - | - | - | - | | 0.9862 | 1002 | 1.1242 | - | - | - | - | | 0.9872 | 1003 | 1.3979 | - | - | - | - | | 0.9882 | 1004 | 0.8926 | - | - | - | - | | 0.9892 | 1005 | 2.1105 | - | - | - | - | | 0.9902 | 1006 | 0.6443 | - | - | - | - | | 0.9911 | 1007 | 1.6493 | - | - | - | - | | 0.9921 | 1008 | 1.125 | - | - | - | - | | 0.9931 | 1009 | 0.3277 | - | - | - | - | | 0.9941 | 1010 | 0.8848 | - | - | - | - | | 0.9951 | 1011 | 0.6624 | - | - | - | - | | 0.9961 | 1012 | 0.7913 | - | - | - | - | | 0.9970 | 1013 | 1.2572 | - | - | - | - | | 0.9980 | 1014 | 1.2533 | - | - | - | - | | 0.9990 | 1015 | 0.7953 | - | - | - | - | | 1.0 | 1016 | 0.3578 | - | - | - | - | | 1.0010 | 1017 | 1.1694 | - | - | - | - | | 1.0020 | 1018 | 1.0959 | - | - | - | - | | 1.0030 | 1019 | 0.8922 | - | - | - | - | | 1.0039 | 1020 | 0.7743 | - | - | - | - | | 1.0049 | 1021 | 0.5631 | - | - | - | - | | 1.0059 | 1022 | 1.2144 | - | - | - | - | | 1.0069 | 1023 | 0.5034 | - | - | - | - | | 1.0079 | 1024 | 0.7687 | - | - | - | - | | 1.0089 | 1025 | 0.7181 | - | - | - | - | | 1.0098 | 1026 | 1.0367 | - | - | - | - | | 1.0108 | 1027 | 0.8523 | - | - | - | - | | 1.0118 | 1028 | 1.1932 | - | - | - | - | | 1.0128 | 1029 | 1.3118 | - | - | - | - | | 1.0138 | 1030 | 0.8769 | - | - | - | - | | 1.0148 | 1031 | 0.8931 | - | - | - | - | | 1.0157 | 1032 | 0.8208 | - | - | - | - | | 1.0167 | 1033 | 0.7876 | - | - | - | - | | 1.0177 | 1034 | 1.1651 | - | - | - | - | | 1.0187 | 1035 | 0.8233 | - | - | - | - | | 1.0197 | 1036 | 0.7586 | - | - | - | - | | 1.0207 | 1037 | 0.8531 | - | - | - | - | | 1.0217 | 1038 | 1.81 | - | - | - | - | | 1.0226 | 1039 | 0.601 | - | - | - | - | | 1.0236 | 1040 | 0.6086 | - | - | - | - | | 1.0246 | 1041 | 0.6538 | - | - | - | - | | 1.0256 | 1042 | 0.5518 | - | - | - | - | | 1.0266 | 1043 | 1.249 | - | - | - | - | | 1.0276 | 1044 | 0.5059 | - | - | - | - | | 1.0285 | 1045 | 0.6202 | - | - | - | - | | 1.0295 | 1046 | 0.8073 | - | - | - | - | | 1.0305 | 1047 | 0.4438 | - | - | - | - | | 1.0315 | 1048 | 1.4425 | - | - | - | - | | 1.0325 | 1049 | 0.3772 | - | - | - | - | | 1.0335 | 1050 | 0.4225 | - | - | - | - | | 1.0344 | 1051 | 0.7363 | - | - | - | - | | 1.0354 | 1052 | 0.4342 | - | - | - | - | | 1.0364 | 1053 | 0.8763 | - | - | - | - | | 1.0374 | 1054 | 0.8974 | - | - | - | - | | 1.0384 | 1055 | 0.9175 | - | - | - | - | | 1.0394 | 1056 | 0.9145 | - | - | - | - | | 1.0404 | 1057 | 0.7247 | - | - | - | - | | 1.0413 | 1058 | 0.6066 | - | - | - | - | | 1.0423 | 1059 | 0.5892 | - | - | - | - | | 1.0433 | 1060 | 2.1779 | - | - | - | - | | 1.0443 | 1061 | 0.7973 | - | - | - | - | | 1.0453 | 1062 | 0.4354 | - | - | - | - | | 1.0463 | 1063 | 1.2032 | - | - | - | - | | 1.0472 | 1064 | 1.088 | - | - | - | - | | 1.0482 | 1065 | 0.3944 | - | - | - | - | | 1.0492 | 1066 | 0.5178 | - | - | - | - | | 1.0502 | 1067 | 1.0818 | - | - | - | - | | 1.0512 | 1068 | 0.8308 | - | - | - | - | | 1.0522 | 1069 | 1.54 | - | - | - | - | | 1.0531 | 1070 | 0.8444 | - | - | - | - | | 1.0541 | 1071 | 0.4829 | 0.7322 | 0.5793 | 0.4943 | 0.6916 | | 1.0551 | 1072 | 0.495 | - | - | - | - | | 1.0561 | 1073 | 0.8591 | - | - | - | - | | 1.0571 | 1074 | 0.327 | - | - | - | - | | 1.0581 | 1075 | 0.7161 | - | - | - | - | | 1.0591 | 1076 | 0.6374 | - | - | - | - | | 1.0600 | 1077 | 1.1748 | - | - | - | - | | 1.0610 | 1078 | 1.7501 | - | - | - | - | | 1.0620 | 1079 | 0.5544 | - | - | - | - | | 1.0630 | 1080 | 0.6265 | - | - | - | - | | 1.0640 | 1081 | 1.6517 | - | - | - | - | | 1.0650 | 1082 | 0.7457 | - | - | - | - | | 1.0659 | 1083 | 0.7492 | - | - | - | - | | 1.0669 | 1084 | 0.8013 | - | - | - | - | | 1.0679 | 1085 | 0.1619 | - | - | - | - | | 1.0689 | 1086 | 0.5057 | - | - | - | - | | 1.0699 | 1087 | 0.4712 | - | - | - | - | | 1.0709 | 1088 | 0.8382 | - | - | - | - | | 1.0719 | 1089 | 0.6045 | - | - | - | - | | 1.0728 | 1090 | 0.6117 | - | - | - | - | | 1.0738 | 1091 | 0.7028 | - | - | - | - | | 1.0748 | 1092 | 1.2376 | - | - | - | - | | 1.0758 | 1093 | 1.045 | - | - | - | - | | 1.0768 | 1094 | 1.1152 | - | - | - | - | | 1.0778 | 1095 | 0.5572 | - | - | - | - | | 1.0787 | 1096 | 0.7047 | - | - | - | - | | 1.0797 | 1097 | 1.4233 | - | - | - | - | | 1.0807 | 1098 | 0.8478 | - | - | - | - | | 1.0817 | 1099 | 0.6851 | - | - | - | - | | 1.0827 | 1100 | 0.4462 | - | - | - | - | | 1.0837 | 1101 | 2.1139 | - | - | - | - | | 1.0846 | 1102 | 0.8097 | - | - | - | - | | 1.0856 | 1103 | 1.0912 | - | - | - | - | | 1.0866 | 1104 | 1.1922 | - | - | - | - | | 1.0876 | 1105 | 0.3888 | - | - | - | - | | 1.0886 | 1106 | 0.7842 | - | - | - | - | | 1.0896 | 1107 | 0.1422 | - | - | - | - | | 1.0906 | 1108 | 0.6949 | - | - | - | - | | 1.0915 | 1109 | 0.819 | - | - | - | - | | 1.0925 | 1110 | 0.4947 | - | - | - | - | | 1.0935 | 1111 | 0.3346 | - | - | - | - | | 1.0945 | 1112 | 1.1459 | - | - | - | - | | 1.0955 | 1113 | 0.3276 | - | - | - | - | | 1.0965 | 1114 | 0.7464 | - | - | - | - | | 1.0974 | 1115 | 0.8906 | - | - | - | - | | 1.0984 | 1116 | 1.9711 | - | - | - | - | | 1.0994 | 1117 | 0.6403 | - | - | - | - | | 1.1004 | 1118 | 1.3684 | - | - | - | - | | 1.1014 | 1119 | 1.2074 | - | - | - | - | | 1.1024 | 1120 | 0.5098 | - | - | - | - | | 1.1033 | 1121 | 0.5498 | - | - | - | - | | 1.1043 | 1122 | 0.3848 | - | - | - | - | | 1.1053 | 1123 | 2.0202 | - | - | - | - | | 1.1063 | 1124 | 0.5944 | - | - | - | - | | 1.1073 | 1125 | 0.3266 | - | - | - | - | | 1.1083 | 1126 | 1.0289 | - | - | - | - | | 1.1093 | 1127 | 1.0807 | - | - | - | - | | 1.1102 | 1128 | 0.6155 | - | - | - | - | | 1.1112 | 1129 | 1.1686 | - | - | - | - | | 1.1122 | 1130 | 1.0762 | - | - | - | - | | 1.1132 | 1131 | 0.6781 | - | - | - | - | | 1.1142 | 1132 | 0.6144 | - | - | - | - | | 1.1152 | 1133 | 0.8022 | - | - | - | - | | 1.1161 | 1134 | 0.5213 | - | - | - | - | | 1.1171 | 1135 | 0.6014 | - | - | - | - | | 1.1181 | 1136 | 0.901 | - | - | - | - | | 1.1191 | 1137 | 0.9938 | - | - | - | - | | 1.1201 | 1138 | 1.8173 | - | - | - | - | | 1.1211 | 1139 | 0.5572 | - | - | - | - | | 1.1220 | 1140 | 0.7489 | - | - | - | - | | 1.1230 | 1141 | 0.4338 | - | - | - | - | | 1.1240 | 1142 | 0.3086 | - | - | - | - | | 1.125 | 1143 | 0.6942 | - | - | - | - | | 1.1260 | 1144 | 0.7665 | - | - | - | - | | 1.1270 | 1145 | 0.2734 | - | - | - | - | | 1.1280 | 1146 | 0.9961 | - | - | - | - | | 1.1289 | 1147 | 0.5258 | - | - | - | - | | 1.1299 | 1148 | 0.7122 | - | - | - | - | | 1.1309 | 1149 | 0.3747 | - | - | - | - | | 1.1319 | 1150 | 0.6397 | - | - | - | - | | 1.1329 | 1151 | 0.5504 | - | - | - | - | | 1.1339 | 1152 | 0.5572 | - | - | - | - | | 1.1348 | 1153 | 0.7828 | - | - | - | - | | 1.1358 | 1154 | 1.0443 | - | - | - | - | | 1.1368 | 1155 | 1.0731 | - | - | - | - | | 1.1378 | 1156 | 1.1341 | - | - | - | - | | 1.1388 | 1157 | 0.391 | - | - | - | - | | 1.1398 | 1158 | 1.462 | - | - | - | - | | 1.1407 | 1159 | 0.8131 | - | - | - | - | | 1.1417 | 1160 | 0.7323 | - | - | - | - | | 1.1427 | 1161 | 0.5473 | - | - | - | - | | 1.1437 | 1162 | 0.7973 | - | - | - | - | | 1.1447 | 1163 | 0.5875 | - | - | - | - | | 1.1457 | 1164 | 0.9248 | - | - | - | - | | 1.1467 | 1165 | 0.6898 | - | - | - | - | | 1.1476 | 1166 | 1.4924 | - | - | - | - | | 1.1486 | 1167 | 0.8908 | - | - | - | - | | 1.1496 | 1168 | 0.564 | - | - | - | - | | 1.1506 | 1169 | 0.3779 | - | - | - | - | | 1.1516 | 1170 | 1.0715 | - | - | - | - | | 1.1526 | 1171 | 0.4366 | - | - | - | - | | 1.1535 | 1172 | 0.6391 | - | - | - | - | | 1.1545 | 1173 | 1.2133 | - | - | - | - | | 1.1555 | 1174 | 1.4135 | - | - | - | - | | 1.1565 | 1175 | 0.7748 | - | - | - | - | | 1.1575 | 1176 | 0.544 | - | - | - | - | | 1.1585 | 1177 | 0.5168 | - | - | - | - | | 1.1594 | 1178 | 0.6931 | - | - | - | - | | 1.1604 | 1179 | 0.87 | - | - | - | - | | 1.1614 | 1180 | 0.9842 | - | - | - | - | | 1.1624 | 1181 | 0.3614 | - | - | - | - | | 1.1634 | 1182 | 0.4167 | - | - | - | - | | 1.1644 | 1183 | 0.3688 | - | - | - | - | | 1.1654 | 1184 | 0.5431 | - | - | - | - | | 1.1663 | 1185 | 0.6127 | - | - | - | - | | 1.1673 | 1186 | 0.8693 | - | - | - | - | | 1.1683 | 1187 | 0.7596 | - | - | - | - | | 1.1693 | 1188 | 0.724 | - | - | - | - | | 1.1703 | 1189 | 0.9105 | - | - | - | - | | 1.1713 | 1190 | 0.3941 | - | - | - | - | | 1.1722 | 1191 | 1.1768 | - | - | - | - | | 1.1732 | 1192 | 0.5509 | - | - | - | - | | 1.1742 | 1193 | 1.1616 | - | - | - | - | | 1.1752 | 1194 | 0.6835 | - | - | - | - | | 1.1762 | 1195 | 0.4379 | - | - | - | - | | 1.1772 | 1196 | 0.5453 | - | - | - | - | | 1.1781 | 1197 | 0.5505 | - | - | - | - | | 1.1791 | 1198 | 0.7472 | - | - | - | - | | 1.1801 | 1199 | 0.3541 | - | - | - | - | | 1.1811 | 1200 | 0.796 | - | - | - | - | | 1.1821 | 1201 | 0.558 | - | - | - | - | | 1.1831 | 1202 | 0.8679 | - | - | - | - | | 1.1841 | 1203 | 0.7619 | - | - | - | - | | 1.1850 | 1204 | 0.7039 | - | - | - | - | | 1.1860 | 1205 | 0.7166 | - | - | - | - | | 1.1870 | 1206 | 0.6982 | - | - | - | - | | 1.1880 | 1207 | 0.4206 | - | - | - | - | | 1.1890 | 1208 | 0.6361 | - | - | - | - | | 1.1900 | 1209 | 0.6248 | - | - | - | - | | 1.1909 | 1210 | 0.7933 | - | - | - | - | | 1.1919 | 1211 | 0.5985 | - | - | - | - | | 1.1929 | 1212 | 0.6147 | - | - | - | - | | 1.1939 | 1213 | 0.6085 | - | - | - | - | | 1.1949 | 1214 | 0.6713 | - | - | - | - | | 1.1959 | 1215 | 1.0315 | - | - | - | - | | 1.1969 | 1216 | 2.0024 | - | - | - | - | | 1.1978 | 1217 | 1.6034 | - | - | - | - | | 1.1988 | 1218 | 1.7407 | - | - | - | - | | 1.1998 | 1219 | 1.2014 | - | - | - | - | | 1.2008 | 1220 | 1.8377 | - | - | - | - | | 1.2018 | 1221 | 0.6652 | - | - | - | - | | 1.2028 | 1222 | 0.2618 | - | - | - | - | | 1.2037 | 1223 | 1.4023 | - | - | - | - | | 1.2047 | 1224 | 0.2575 | 0.6752 | 0.5964 | 0.4982 | 0.7087 | | 1.2057 | 1225 | 0.6646 | - | - | - | - | | 1.2067 | 1226 | 0.8142 | - | - | - | - | | 1.2077 | 1227 | 0.7552 | - | - | - | - | | 1.2087 | 1228 | 0.8724 | - | - | - | - | | 1.2096 | 1229 | 0.92 | - | - | - | - | | 1.2106 | 1230 | 0.8513 | - | - | - | - | | 1.2116 | 1231 | 0.5221 | - | - | - | - | | 1.2126 | 1232 | 0.8456 | - | - | - | - | | 1.2136 | 1233 | 0.3728 | - | - | - | - | | 1.2146 | 1234 | 1.1982 | - | - | - | - | | 1.2156 | 1235 | 0.4944 | - | - | - | - | | 1.2165 | 1236 | 0.454 | - | - | - | - | | 1.2175 | 1237 | 0.8594 | - | - | - | - | | 1.2185 | 1238 | 0.8604 | - | - | - | - | | 1.2195 | 1239 | 0.9616 | - | - | - | - | | 1.2205 | 1240 | 0.9257 | - | - | - | - | | 1.2215 | 1241 | 0.8514 | - | - | - | - | | 1.2224 | 1242 | 0.6498 | - | - | - | - | | 1.2234 | 1243 | 1.0719 | - | - | - | - | | 1.2244 | 1244 | 1.2279 | - | - | - | - | | 1.2254 | 1245 | 1.0294 | - | - | - | - | | 1.2264 | 1246 | 0.7619 | - | - | - | - | | 1.2274 | 1247 | 0.3707 | - | - | - | - | | 1.2283 | 1248 | 0.3229 | - | - | - | - | | 1.2293 | 1249 | 0.9892 | - | - | - | - | | 1.2303 | 1250 | 0.7125 | - | - | - | - | | 1.2313 | 1251 | 0.3682 | - | - | - | - | | 1.2323 | 1252 | 0.5191 | - | - | - | - | | 1.2333 | 1253 | 0.5471 | - | - | - | - | | 1.2343 | 1254 | 0.3635 | - | - | - | - | | 1.2352 | 1255 | 0.5368 | - | - | - | - | | 1.2362 | 1256 | 0.4115 | - | - | - | - | | 1.2372 | 1257 | 0.3883 | - | - | - | - | | 1.2382 | 1258 | 0.4394 | - | - | - | - | | 1.2392 | 1259 | 0.6474 | - | - | - | - | | 1.2402 | 1260 | 1.0838 | - | - | - | - | | 1.2411 | 1261 | 0.7188 | - | - | - | - | | 1.2421 | 1262 | 0.5869 | - | - | - | - | | 1.2431 | 1263 | 2.6805 | - | - | - | - | | 1.2441 | 1264 | 0.7447 | - | - | - | - | | 1.2451 | 1265 | 1.1048 | - | - | - | - | | 1.2461 | 1266 | 0.4745 | - | - | - | - | | 1.2470 | 1267 | 1.3479 | - | - | - | - | | 1.2480 | 1268 | 0.4079 | - | - | - | - | | 1.2490 | 1269 | 0.3326 | - | - | - | - | | 1.25 | 1270 | 0.5237 | - | - | - | - | | 1.2510 | 1271 | 0.2571 | - | - | - | - | | 1.2520 | 1272 | 0.7165 | - | - | - | - | | 1.2530 | 1273 | 0.5696 | - | - | - | - | | 1.2539 | 1274 | 0.8936 | - | - | - | - | | 1.2549 | 1275 | 0.3444 | - | - | - | - | | 1.2559 | 1276 | 0.785 | - | - | - | - | | 1.2569 | 1277 | 0.3361 | - | - | - | - | | 1.2579 | 1278 | 0.3905 | - | - | - | - | | 1.2589 | 1279 | 0.8173 | - | - | - | - | | 1.2598 | 1280 | 0.4759 | - | - | - | - | | 1.2608 | 1281 | 0.3544 | - | - | - | - | | 1.2618 | 1282 | 0.4727 | - | - | - | - | | 1.2628 | 1283 | 0.5195 | - | - | - | - | | 1.2638 | 1284 | 0.5446 | - | - | - | - | | 1.2648 | 1285 | 0.585 | - | - | - | - | | 1.2657 | 1286 | 0.4068 | - | - | - | - | | 1.2667 | 1287 | 1.4534 | - | - | - | - | | 1.2677 | 1288 | 0.3907 | - | - | - | - | | 1.2687 | 1289 | 0.8361 | - | - | - | - | | 1.2697 | 1290 | 1.1358 | - | - | - | - | | 1.2707 | 1291 | 0.6607 | - | - | - | - | | 1.2717 | 1292 | 0.5284 | - | - | - | - | | 1.2726 | 1293 | 0.8732 | - | - | - | - | | 1.2736 | 1294 | 0.4414 | - | - | - | - | | 1.2746 | 1295 | 0.9862 | - | - | - | - | | 1.2756 | 1296 | 0.5916 | - | - | - | - | | 1.2766 | 1297 | 0.4013 | - | - | - | - | | 1.2776 | 1298 | 0.5889 | - | - | - | - | | 1.2785 | 1299 | 0.7337 | - | - | - | - | | 1.2795 | 1300 | 0.4836 | - | - | - | - | | 1.2805 | 1301 | 0.6721 | - | - | - | - | | 1.2815 | 1302 | 0.622 | - | - | - | - | | 1.2825 | 1303 | 0.4463 | - | - | - | - | | 1.2835 | 1304 | 1.0106 | - | - | - | - | | 1.2844 | 1305 | 0.9205 | - | - | - | - | | 1.2854 | 1306 | 1.0984 | - | - | - | - | | 1.2864 | 1307 | 0.3085 | - | - | - | - | | 1.2874 | 1308 | 0.4345 | - | - | - | - | | 1.2884 | 1309 | 0.3946 | - | - | - | - | | 1.2894 | 1310 | 1.6366 | - | - | - | - | | 1.2904 | 1311 | 0.909 | - | - | - | - | | 1.2913 | 1312 | 1.0468 | - | - | - | - | | 1.2923 | 1313 | 1.0732 | - | - | - | - | | 1.2933 | 1314 | 0.5856 | - | - | - | - | | 1.2943 | 1315 | 0.8502 | - | - | - | - | | 1.2953 | 1316 | 0.8886 | - | - | - | - | | 1.2963 | 1317 | 0.7551 | - | - | - | - | | 1.2972 | 1318 | 0.7487 | - | - | - | - | | 1.2982 | 1319 | 0.9703 | - | - | - | - | | 1.2992 | 1320 | 0.4291 | - | - | - | - | | 1.3002 | 1321 | 0.7965 | - | - | - | - | | 1.3012 | 1322 | 0.811 | - | - | - | - | | 1.3022 | 1323 | 0.9556 | - | - | - | - | | 1.3031 | 1324 | 0.8323 | - | - | - | - | | 1.3041 | 1325 | 0.327 | - | - | - | - | | 1.3051 | 1326 | 0.7244 | - | - | - | - | | 1.3061 | 1327 | 1.088 | - | - | - | - | | 1.3071 | 1328 | 0.9094 | - | - | - | - | | 1.3081 | 1329 | 0.7003 | - | - | - | - | | 1.3091 | 1330 | 0.8419 | - | - | - | - | | 1.3100 | 1331 | 0.6017 | - | - | - | - | | 1.3110 | 1332 | 0.4095 | - | - | - | - | | 1.3120 | 1333 | 0.8019 | - | - | - | - | | 1.3130 | 1334 | 0.7212 | - | - | - | - | | 1.3140 | 1335 | 0.6535 | - | - | - | - | | 1.3150 | 1336 | 1.2404 | - | - | - | - | | 1.3159 | 1337 | 0.8993 | - | - | - | - | | 1.3169 | 1338 | 0.5882 | - | - | - | - | | 1.3179 | 1339 | 0.6385 | - | - | - | - | | 1.3189 | 1340 | 0.5562 | - | - | - | - | | 1.3199 | 1341 | 0.2869 | - | - | - | - | | 1.3209 | 1342 | 0.3641 | - | - | - | - | | 1.3219 | 1343 | 0.4218 | - | - | - | - | | 1.3228 | 1344 | 0.606 | - | - | - | - | | 1.3238 | 1345 | 0.3806 | - | - | - | - | | 1.3248 | 1346 | 0.8854 | - | - | - | - | | 1.3258 | 1347 | 0.4355 | - | - | - | - | | 1.3268 | 1348 | 0.1498 | - | - | - | - | | 1.3278 | 1349 | 1.2401 | - | - | - | - | | 1.3287 | 1350 | 0.3354 | - | - | - | - | | 1.3297 | 1351 | 0.9802 | - | - | - | - | | 1.3307 | 1352 | 0.3976 | - | - | - | - | | 1.3317 | 1353 | 1.476 | - | - | - | - | | 1.3327 | 1354 | 1.0131 | - | - | - | - | | 1.3337 | 1355 | 0.6467 | - | - | - | - | | 1.3346 | 1356 | 0.6601 | - | - | - | - | | 1.3356 | 1357 | 0.5619 | - | - | - | - | | 1.3366 | 1358 | 0.5519 | - | - | - | - | | 1.3376 | 1359 | 0.2673 | - | - | - | - | | 1.3386 | 1360 | 0.7003 | - | - | - | - | | 1.3396 | 1361 | 0.4145 | - | - | - | - | | 1.3406 | 1362 | 0.9338 | - | - | - | - | | 1.3415 | 1363 | 1.6307 | - | - | - | - | | 1.3425 | 1364 | 0.353 | - | - | - | - | | 1.3435 | 1365 | 0.6528 | - | - | - | - | | 1.3445 | 1366 | 0.7904 | - | - | - | - | | 1.3455 | 1367 | 0.7177 | - | - | - | - | | 1.3465 | 1368 | 0.2139 | - | - | - | - | | 1.3474 | 1369 | 0.6728 | - | - | - | - | | 1.3484 | 1370 | 0.9091 | - | - | - | - | | 1.3494 | 1371 | 0.5011 | - | - | - | - | | 1.3504 | 1372 | 0.8399 | - | - | - | - | | 1.3514 | 1373 | 0.5121 | - | - | - | - | | 1.3524 | 1374 | 1.4742 | - | - | - | - | | 1.3533 | 1375 | 0.4506 | - | - | - | - | | 1.3543 | 1376 | 0.3336 | - | - | - | - | | 1.3553 | 1377 | 0.4187 | 0.6560 | 0.6240 | 0.5022 | 0.7068 | | 1.3563 | 1378 | 0.5715 | - | - | - | - | | 1.3573 | 1379 | 0.5358 | - | - | - | - | | 1.3583 | 1380 | 0.5081 | - | - | - | - | | 1.3593 | 1381 | 0.8904 | - | - | - | - | | 1.3602 | 1382 | 0.8929 | - | - | - | - | | 1.3612 | 1383 | 0.658 | - | - | - | - | | 1.3622 | 1384 | 0.7433 | - | - | - | - | | 1.3632 | 1385 | 1.4056 | - | - | - | - | | 1.3642 | 1386 | 0.3945 | - | - | - | - | | 1.3652 | 1387 | 0.5946 | - | - | - | - | | 1.3661 | 1388 | 0.6706 | - | - | - | - | | 1.3671 | 1389 | 0.7309 | - | - | - | - | | 1.3681 | 1390 | 0.5186 | - | - | - | - | | 1.3691 | 1391 | 0.5135 | - | - | - | - | | 1.3701 | 1392 | 1.2628 | - | - | - | - | | 1.3711 | 1393 | 0.4493 | - | - | - | - | | 1.3720 | 1394 | 1.0504 | - | - | - | - | | 1.3730 | 1395 | 0.5056 | - | - | - | - | | 1.3740 | 1396 | 0.7245 | - | - | - | - | | 1.375 | 1397 | 0.7753 | - | - | - | - | | 1.3760 | 1398 | 0.5531 | - | - | - | - | | 1.3770 | 1399 | 0.6692 | - | - | - | - | | 1.3780 | 1400 | 0.5516 | - | - | - | - | | 1.3789 | 1401 | 0.637 | - | - | - | - | | 1.3799 | 1402 | 0.3756 | - | - | - | - | | 1.3809 | 1403 | 0.7963 | - | - | - | - | | 1.3819 | 1404 | 0.623 | - | - | - | - | | 1.3829 | 1405 | 0.5124 | - | - | - | - | | 1.3839 | 1406 | 0.5348 | - | - | - | - | | 1.3848 | 1407 | 0.5751 | - | - | - | - | | 1.3858 | 1408 | 0.6647 | - | - | - | - | | 1.3868 | 1409 | 0.5282 | - | - | - | - | | 1.3878 | 1410 | 0.678 | - | - | - | - | | 1.3888 | 1411 | 0.9675 | - | - | - | - | | 1.3898 | 1412 | 0.8766 | - | - | - | - | | 1.3907 | 1413 | 0.5828 | - | - | - | - | | 1.3917 | 1414 | 0.5702 | - | - | - | - | | 1.3927 | 1415 | 0.1859 | - | - | - | - | | 1.3937 | 1416 | 1.3485 | - | - | - | - | | 1.3947 | 1417 | 0.5655 | - | - | - | - | | 1.3957 | 1418 | 0.389 | - | - | - | - | | 1.3967 | 1419 | 0.3533 | - | - | - | - | | 1.3976 | 1420 | 0.4214 | - | - | - | - | | 1.3986 | 1421 | 0.2939 | - | - | - | - | | 1.3996 | 1422 | 0.5645 | - | - | - | - | | 1.4006 | 1423 | 0.7114 | - | - | - | - | | 1.4016 | 1424 | 0.3381 | - | - | - | - | | 1.4026 | 1425 | 0.3896 | - | - | - | - | | 1.4035 | 1426 | 0.7151 | - | - | - | - | | 1.4045 | 1427 | 0.8335 | - | - | - | - | | 1.4055 | 1428 | 0.5981 | - | - | - | - | | 1.4065 | 1429 | 0.8689 | - | - | - | - | | 1.4075 | 1430 | 0.3731 | - | - | - | - | | 1.4085 | 1431 | 0.8882 | - | - | - | - | | 1.4094 | 1432 | 0.7825 | - | - | - | - | | 1.4104 | 1433 | 0.6815 | - | - | - | - | | 1.4114 | 1434 | 0.2557 | - | - | - | - | | 1.4124 | 1435 | 0.777 | - | - | - | - | | 1.4134 | 1436 | 0.2612 | - | - | - | - | | 1.4144 | 1437 | 0.9318 | - | - | - | - | | 1.4154 | 1438 | 0.5541 | - | - | - | - | | 1.4163 | 1439 | 0.7122 | - | - | - | - | | 1.4173 | 1440 | 0.8204 | - | - | - | - | | 1.4183 | 1441 | 0.4663 | - | - | - | - | | 1.4193 | 1442 | 0.5459 | - | - | - | - | | 1.4203 | 1443 | 0.6332 | - | - | - | - | | 1.4213 | 1444 | 0.5651 | - | - | - | - | | 1.4222 | 1445 | 0.6551 | - | - | - | - | | 1.4232 | 1446 | 0.2372 | - | - | - | - | | 1.4242 | 1447 | 0.4671 | - | - | - | - | | 1.4252 | 1448 | 0.5134 | - | - | - | - | | 1.4262 | 1449 | 0.6305 | - | - | - | - | | 1.4272 | 1450 | 1.5586 | - | - | - | - | | 1.4281 | 1451 | 0.294 | - | - | - | - | | 1.4291 | 1452 | 1.0767 | - | - | - | - | | 1.4301 | 1453 | 0.8044 | - | - | - | - | | 1.4311 | 1454 | 1.206 | - | - | - | - | | 1.4321 | 1455 | 0.3643 | - | - | - | - | | 1.4331 | 1456 | 1.0759 | - | - | - | - | | 1.4341 | 1457 | 0.2343 | - | - | - | - | | 1.4350 | 1458 | 0.5088 | - | - | - | - | | 1.4360 | 1459 | 0.7708 | - | - | - | - | | 1.4370 | 1460 | 0.5081 | - | - | - | - | | 1.4380 | 1461 | 1.1688 | - | - | - | - | | 1.4390 | 1462 | 0.4619 | - | - | - | - | | 1.4400 | 1463 | 0.6047 | - | - | - | - | | 1.4409 | 1464 | 0.4521 | - | - | - | - | | 1.4419 | 1465 | 0.4313 | - | - | - | - | | 1.4429 | 1466 | 0.781 | - | - | - | - | | 1.4439 | 1467 | 0.4163 | - | - | - | - | | 1.4449 | 1468 | 1.0091 | - | - | - | - | | 1.4459 | 1469 | 0.9163 | - | - | - | - | | 1.4469 | 1470 | 0.297 | - | - | - | - | | 1.4478 | 1471 | 0.6652 | - | - | - | - | | 1.4488 | 1472 | 0.51 | - | - | - | - | | 1.4498 | 1473 | 0.4238 | - | - | - | - | | 1.4508 | 1474 | 0.2851 | - | - | - | - | | 1.4518 | 1475 | 0.7563 | - | - | - | - | | 1.4528 | 1476 | 1.5687 | - | - | - | - | | 1.4537 | 1477 | 0.4711 | - | - | - | - | | 1.4547 | 1478 | 0.3604 | - | - | - | - | | 1.4557 | 1479 | 0.4551 | - | - | - | - | | 1.4567 | 1480 | 0.5354 | - | - | - | - | | 1.4577 | 1481 | 0.6896 | - | - | - | - | | 1.4587 | 1482 | 0.9103 | - | - | - | - | | 1.4596 | 1483 | 0.2517 | - | - | - | - | | 1.4606 | 1484 | 1.1375 | - | - | - | - | | 1.4616 | 1485 | 0.6002 | - | - | - | - | | 1.4626 | 1486 | 0.483 | - | - | - | - | | 1.4636 | 1487 | 0.5464 | - | - | - | - | | 1.4646 | 1488 | 0.4677 | - | - | - | - | | 1.4656 | 1489 | 0.673 | - | - | - | - | | 1.4665 | 1490 | 1.1392 | - | - | - | - | | 1.4675 | 1491 | 0.69 | - | - | - | - | | 1.4685 | 1492 | 0.5697 | - | - | - | - | | 1.4695 | 1493 | 0.3707 | - | - | - | - | | 1.4705 | 1494 | 0.7141 | - | - | - | - | | 1.4715 | 1495 | 0.4173 | - | - | - | - | | 1.4724 | 1496 | 1.0088 | - | - | - | - | | 1.4734 | 1497 | 0.5028 | - | - | - | - | | 1.4744 | 1498 | 0.6502 | - | - | - | - | | 1.4754 | 1499 | 0.5432 | - | - | - | - | | 1.4764 | 1500 | 0.7481 | - | - | - | - | | 1.4774 | 1501 | 0.6316 | - | - | - | - | | 1.4783 | 1502 | 0.5775 | - | - | - | - | | 1.4793 | 1503 | 0.5893 | - | - | - | - | | 1.4803 | 1504 | 0.8438 | - | - | - | - | | 1.4813 | 1505 | 0.4522 | - | - | - | - | | 1.4823 | 1506 | 0.5695 | - | - | - | - | | 1.4833 | 1507 | 0.9334 | - | - | - | - | | 1.4843 | 1508 | 0.8144 | - | - | - | - | | 1.4852 | 1509 | 0.6911 | - | - | - | - | | 1.4862 | 1510 | 0.2779 | - | - | - | - | | 1.4872 | 1511 | 0.7079 | - | - | - | - | | 1.4882 | 1512 | 0.4727 | - | - | - | - | | 1.4892 | 1513 | 0.3663 | - | - | - | - | | 1.4902 | 1514 | 0.5314 | - | - | - | - | | 1.4911 | 1515 | 0.2767 | - | - | - | - | | 1.4921 | 1516 | 0.3167 | - | - | - | - | | 1.4931 | 1517 | 0.4638 | - | - | - | - | | 1.4941 | 1518 | 0.675 | - | - | - | - | | 1.4951 | 1519 | 0.5539 | - | - | - | - | | 1.4961 | 1520 | 1.0517 | - | - | - | - | | 1.4970 | 1521 | 0.5162 | - | - | - | - | | 1.4980 | 1522 | 0.6293 | - | - | - | - | | 1.4990 | 1523 | 0.5688 | - | - | - | - | | 1.5 | 1524 | 0.3404 | - | - | - | - | | 1.5010 | 1525 | 0.512 | - | - | - | - | | 1.5020 | 1526 | 0.5594 | - | - | - | - | | 1.5030 | 1527 | 0.894 | - | - | - | - | | 1.5039 | 1528 | 0.6125 | - | - | - | - | | 1.5049 | 1529 | 0.6056 | - | - | - | - | | 1.5059 | 1530 | 0.7177 | 0.6076 | 0.6309 | 0.4971 | 0.6999 | | 1.5069 | 1531 | 0.3312 | - | - | - | - | | 1.5079 | 1532 | 0.4585 | - | - | - | - | | 1.5089 | 1533 | 0.4917 | - | - | - | - | | 1.5098 | 1534 | 0.614 | - | - | - | - | | 1.5108 | 1535 | 0.1733 | - | - | - | - | | 1.5118 | 1536 | 0.7729 | - | - | - | - | | 1.5128 | 1537 | 0.2272 | - | - | - | - | | 1.5138 | 1538 | 0.4664 | - | - | - | - | | 1.5148 | 1539 | 0.4116 | - | - | - | - | | 1.5157 | 1540 | 0.2704 | - | - | - | - | | 1.5167 | 1541 | 1.8474 | - | - | - | - | | 1.5177 | 1542 | 0.91 | - | - | - | - | | 1.5187 | 1543 | 0.1718 | - | - | - | - | | 1.5197 | 1544 | 0.528 | - | - | - | - | | 1.5207 | 1545 | 0.3511 | - | - | - | - | | 1.5217 | 1546 | 0.7824 | - | - | - | - | | 1.5226 | 1547 | 0.2457 | - | - | - | - | | 1.5236 | 1548 | 1.3333 | - | - | - | - | | 1.5246 | 1549 | 0.3311 | - | - | - | - | | 1.5256 | 1550 | 0.9244 | - | - | - | - | | 1.5266 | 1551 | 0.8461 | - | - | - | - | | 1.5276 | 1552 | 0.5966 | - | - | - | - | | 1.5285 | 1553 | 0.6486 | - | - | - | - | | 1.5295 | 1554 | 0.3623 | - | - | - | - | | 1.5305 | 1555 | 1.0995 | - | - | - | - | | 1.5315 | 1556 | 0.6517 | - | - | - | - | | 1.5325 | 1557 | 0.3321 | - | - | - | - | | 1.5335 | 1558 | 0.5902 | - | - | - | - | | 1.5344 | 1559 | 2.0103 | - | - | - | - | | 1.5354 | 1560 | 0.6423 | - | - | - | - | | 1.5364 | 1561 | 0.6593 | - | - | - | - | | 1.5374 | 1562 | 1.1699 | - | - | - | - | | 1.5384 | 1563 | 0.4871 | - | - | - | - | | 1.5394 | 1564 | 1.2181 | - | - | - | - | | 1.5404 | 1565 | 0.6265 | - | - | - | - | | 1.5413 | 1566 | 0.3751 | - | - | - | - | | 1.5423 | 1567 | 0.3528 | - | - | - | - | | 1.5433 | 1568 | 0.3335 | - | - | - | - | | 1.5443 | 1569 | 0.3162 | - | - | - | - | | 1.5453 | 1570 | 0.9398 | - | - | - | - | | 1.5463 | 1571 | 0.567 | - | - | - | - | | 1.5472 | 1572 | 0.5336 | - | - | - | - | | 1.5482 | 1573 | 1.33 | - | - | - | - | | 1.5492 | 1574 | 1.4235 | - | - | - | - | | 1.5502 | 1575 | 0.9983 | - | - | - | - | | 1.5512 | 1576 | 0.4337 | - | - | - | - | | 1.5522 | 1577 | 0.4167 | - | - | - | - | | 1.5531 | 1578 | 0.2232 | - | - | - | - | | 1.5541 | 1579 | 0.3178 | - | - | - | - | | 1.5551 | 1580 | 0.3089 | - | - | - | - | | 1.5561 | 1581 | 0.4723 | - | - | - | - | | 1.5571 | 1582 | 0.9546 | - | - | - | - | | 1.5581 | 1583 | 0.5077 | - | - | - | - | | 1.5591 | 1584 | 0.8998 | - | - | - | - | | 1.5600 | 1585 | 0.2729 | - | - | - | - | | 1.5610 | 1586 | 0.8975 | - | - | - | - | | 1.5620 | 1587 | 0.5164 | - | - | - | - | | 1.5630 | 1588 | 0.4061 | - | - | - | - | | 1.5640 | 1589 | 0.6179 | - | - | - | - | | 1.5650 | 1590 | 0.2995 | - | - | - | - | | 1.5659 | 1591 | 0.2999 | - | - | - | - | | 1.5669 | 1592 | 0.7981 | - | - | - | - | | 1.5679 | 1593 | 0.646 | - | - | - | - | | 1.5689 | 1594 | 0.2591 | - | - | - | - | | 1.5699 | 1595 | 0.3448 | - | - | - | - | | 1.5709 | 1596 | 0.3245 | - | - | - | - | | 1.5719 | 1597 | 0.713 | - | - | - | - | | 1.5728 | 1598 | 0.565 | - | - | - | - | | 1.5738 | 1599 | 0.5098 | - | - | - | - | | 1.5748 | 1600 | 1.2973 | - | - | - | - | | 1.5758 | 1601 | 0.2531 | - | - | - | - | | 1.5768 | 1602 | 0.6581 | - | - | - | - | | 1.5778 | 1603 | 0.9468 | - | - | - | - | | 1.5787 | 1604 | 0.4272 | - | - | - | - | | 1.5797 | 1605 | 0.5431 | - | - | - | - | | 1.5807 | 1606 | 0.8867 | - | - | - | - | | 1.5817 | 1607 | 0.8721 | - | - | - | - | | 1.5827 | 1608 | 0.6227 | - | - | - | - | | 1.5837 | 1609 | 0.1811 | - | - | - | - | | 1.5846 | 1610 | 0.7213 | - | - | - | - | | 1.5856 | 1611 | 0.2797 | - | - | - | - | | 1.5866 | 1612 | 0.6565 | - | - | - | - | | 1.5876 | 1613 | 0.7022 | - | - | - | - | | 1.5886 | 1614 | 0.7888 | - | - | - | - | | 1.5896 | 1615 | 0.709 | - | - | - | - | | 1.5906 | 1616 | 0.7434 | - | - | - | - | | 1.5915 | 1617 | 0.53 | - | - | - | - | | 1.5925 | 1618 | 0.4844 | - | - | - | - | | 1.5935 | 1619 | 0.5643 | - | - | - | - | | 1.5945 | 1620 | 0.3544 | - | - | - | - | | 1.5955 | 1621 | 0.2189 | - | - | - | - | | 1.5965 | 1622 | 0.4058 | - | - | - | - | | 1.5974 | 1623 | 0.7974 | - | - | - | - | | 1.5984 | 1624 | 0.5026 | - | - | - | - | | 1.5994 | 1625 | 0.5145 | - | - | - | - | | 1.6004 | 1626 | 0.7416 | - | - | - | - | | 1.6014 | 1627 | 0.7841 | - | - | - | - | | 1.6024 | 1628 | 0.7778 | - | - | - | - | | 1.6033 | 1629 | 0.3109 | - | - | - | - | | 1.6043 | 1630 | 0.2943 | - | - | - | - | | 1.6053 | 1631 | 0.3306 | - | - | - | - | | 1.6063 | 1632 | 0.4688 | - | - | - | - | | 1.6073 | 1633 | 0.319 | - | - | - | - | | 1.6083 | 1634 | 0.4538 | - | - | - | - | | 1.6093 | 1635 | 0.5982 | - | - | - | - | | 1.6102 | 1636 | 0.3236 | - | - | - | - | | 1.6112 | 1637 | 0.5368 | - | - | - | - | | 1.6122 | 1638 | 0.5106 | - | - | - | - | | 1.6132 | 1639 | 0.4051 | - | - | - | - | | 1.6142 | 1640 | 0.6246 | - | - | - | - | | 1.6152 | 1641 | 0.3804 | - | - | - | - | | 1.6161 | 1642 | 0.3031 | - | - | - | - | | 1.6171 | 1643 | 0.6316 | - | - | - | - | | 1.6181 | 1644 | 0.2239 | - | - | - | - | | 1.6191 | 1645 | 1.37 | - | - | - | - | | 1.6201 | 1646 | 0.2093 | - | - | - | - | | 1.6211 | 1647 | 0.4044 | - | - | - | - | | 1.6220 | 1648 | 0.3808 | - | - | - | - | | 1.6230 | 1649 | 0.4414 | - | - | - | - | | 1.6240 | 1650 | 0.7992 | - | - | - | - | | 1.625 | 1651 | 0.4573 | - | - | - | - | | 1.6260 | 1652 | 0.2918 | - | - | - | - | | 1.6270 | 1653 | 0.423 | - | - | - | - | | 1.6280 | 1654 | 0.367 | - | - | - | - | | 1.6289 | 1655 | 0.4115 | - | - | - | - | | 1.6299 | 1656 | 0.3583 | - | - | - | - | | 1.6309 | 1657 | 0.3222 | - | - | - | - | | 1.6319 | 1658 | 0.8085 | - | - | - | - | | 1.6329 | 1659 | 0.2026 | - | - | - | - | | 1.6339 | 1660 | 0.5456 | - | - | - | - | | 1.6348 | 1661 | 0.8468 | - | - | - | - | | 1.6358 | 1662 | 1.1053 | - | - | - | - | | 1.6368 | 1663 | 0.7123 | - | - | - | - | | 1.6378 | 1664 | 0.2607 | - | - | - | - | | 1.6388 | 1665 | 0.0968 | - | - | - | - | | 1.6398 | 1666 | 0.2164 | - | - | - | - | | 1.6407 | 1667 | 0.69 | - | - | - | - | | 1.6417 | 1668 | 1.0048 | - | - | - | - | | 1.6427 | 1669 | 0.3305 | - | - | - | - | | 1.6437 | 1670 | 0.2231 | - | - | - | - | | 1.6447 | 1671 | 0.2445 | - | - | - | - | | 1.6457 | 1672 | 0.3242 | - | - | - | - | | 1.6467 | 1673 | 0.089 | - | - | - | - | | 1.6476 | 1674 | 0.5702 | - | - | - | - | | 1.6486 | 1675 | 0.4989 | - | - | - | - | | 1.6496 | 1676 | 0.9726 | - | - | - | - | | 1.6506 | 1677 | 0.4638 | - | - | - | - | | 1.6516 | 1678 | 0.4957 | - | - | - | - | | 1.6526 | 1679 | 0.8089 | - | - | - | - | | 1.6535 | 1680 | 0.2915 | - | - | - | - | | 1.6545 | 1681 | 0.5772 | - | - | - | - | | 1.6555 | 1682 | 0.569 | - | - | - | - | | 1.6565 | 1683 | 0.568 | 0.5907 | 0.6242 | 0.5246 | 0.7027 | | 1.6575 | 1684 | 0.4959 | - | - | - | - | | 1.6585 | 1685 | 0.4703 | - | - | - | - | | 1.6594 | 1686 | 0.2729 | - | - | - | - | | 1.6604 | 1687 | 0.9194 | - | - | - | - | | 1.6614 | 1688 | 0.4448 | - | - | - | - | | 1.6624 | 1689 | 1.034 | - | - | - | - | | 1.6634 | 1690 | 0.7181 | - | - | - | - | | 1.6644 | 1691 | 0.3676 | - | - | - | - | | 1.6654 | 1692 | 0.2037 | - | - | - | - | | 1.6663 | 1693 | 0.5381 | - | - | - | - | | 1.6673 | 1694 | 0.5897 | - | - | - | - | | 1.6683 | 1695 | 0.3893 | - | - | - | - | | 1.6693 | 1696 | 0.2726 | - | - | - | - | | 1.6703 | 1697 | 0.3016 | - | - | - | - | | 1.6713 | 1698 | 0.3622 | - | - | - | - | | 1.6722 | 1699 | 0.7413 | - | - | - | - | | 1.6732 | 1700 | 0.4711 | - | - | - | - | | 1.6742 | 1701 | 0.5852 | - | - | - | - | | 1.6752 | 1702 | 0.2488 | - | - | - | - | | 1.6762 | 1703 | 0.6424 | - | - | - | - | | 1.6772 | 1704 | 0.5929 | - | - | - | - | | 1.6781 | 1705 | 1.1645 | - | - | - | - | | 1.6791 | 1706 | 0.3906 | - | - | - | - | | 1.6801 | 1707 | 0.6635 | - | - | - | - | | 1.6811 | 1708 | 0.3191 | - | - | - | - | | 1.6821 | 1709 | 1.1335 | - | - | - | - | | 1.6831 | 1710 | 0.4492 | - | - | - | - | | 1.6841 | 1711 | 0.5182 | - | - | - | - | | 1.6850 | 1712 | 1.1094 | - | - | - | - | | 1.6860 | 1713 | 0.2395 | - | - | - | - | | 1.6870 | 1714 | 0.7895 | - | - | - | - | | 1.6880 | 1715 | 0.1977 | - | - | - | - | | 1.6890 | 1716 | 0.3888 | - | - | - | - | | 1.6900 | 1717 | 0.5365 | - | - | - | - | | 1.6909 | 1718 | 0.7392 | - | - | - | - | | 1.6919 | 1719 | 0.7695 | - | - | - | - | | 1.6929 | 1720 | 0.6455 | - | - | - | - | | 1.6939 | 1721 | 0.25 | - | - | - | - | | 1.6949 | 1722 | 0.4361 | - | - | - | - | | 1.6959 | 1723 | 0.5931 | - | - | - | - | | 1.6969 | 1724 | 0.3968 | - | - | - | - | | 1.6978 | 1725 | 0.7418 | - | - | - | - | | 1.6988 | 1726 | 1.2343 | - | - | - | - | | 1.6998 | 1727 | 0.5609 | - | - | - | - | | 1.7008 | 1728 | 0.2499 | - | - | - | - | | 1.7018 | 1729 | 0.3217 | - | - | - | - | | 1.7028 | 1730 | 0.5106 | - | - | - | - | | 1.7037 | 1731 | 0.5158 | - | - | - | - | | 1.7047 | 1732 | 0.3063 | - | - | - | - | | 1.7057 | 1733 | 0.6839 | - | - | - | - | | 1.7067 | 1734 | 0.7934 | - | - | - | - | | 1.7077 | 1735 | 0.3674 | - | - | - | - | | 1.7087 | 1736 | 0.7417 | - | - | - | - | | 1.7096 | 1737 | 0.5724 | - | - | - | - | | 1.7106 | 1738 | 0.4792 | - | - | - | - | | 1.7116 | 1739 | 0.1971 | - | - | - | - | | 1.7126 | 1740 | 0.1942 | - | - | - | - | | 1.7136 | 1741 | 0.1964 | - | - | - | - | | 1.7146 | 1742 | 0.5801 | - | - | - | - | | 1.7156 | 1743 | 0.4141 | - | - | - | - | | 1.7165 | 1744 | 0.7436 | - | - | - | - | | 1.7175 | 1745 | 0.5944 | - | - | - | - | | 1.7185 | 1746 | 0.2409 | - | - | - | - | | 1.7195 | 1747 | 0.7519 | - | - | - | - | | 1.7205 | 1748 | 0.539 | - | - | - | - | | 1.7215 | 1749 | 0.4905 | - | - | - | - | | 1.7224 | 1750 | 0.5004 | - | - | - | - | | 1.7234 | 1751 | 0.8092 | - | - | - | - | | 1.7244 | 1752 | 0.7336 | - | - | - | - | | 1.7254 | 1753 | 0.7179 | - | - | - | - | | 1.7264 | 1754 | 0.5934 | - | - | - | - | | 1.7274 | 1755 | 0.3778 | - | - | - | - | | 1.7283 | 1756 | 0.536 | - | - | - | - | | 1.7293 | 1757 | 0.7303 | - | - | - | - | | 1.7303 | 1758 | 0.4749 | - | - | - | - | | 1.7313 | 1759 | 0.2381 | - | - | - | - | | 1.7323 | 1760 | 0.3432 | - | - | - | - | | 1.7333 | 1761 | 0.551 | - | - | - | - | | 1.7343 | 1762 | 0.7364 | - | - | - | - | | 1.7352 | 1763 | 0.3735 | - | - | - | - | | 1.7362 | 1764 | 0.219 | - | - | - | - | | 1.7372 | 1765 | 0.5522 | - | - | - | - | | 1.7382 | 1766 | 0.5187 | - | - | - | - | | 1.7392 | 1767 | 0.8373 | - | - | - | - | | 1.7402 | 1768 | 0.3356 | - | - | - | - | | 1.7411 | 1769 | 0.4305 | - | - | - | - | | 1.7421 | 1770 | 0.5027 | - | - | - | - | | 1.7431 | 1771 | 0.5996 | - | - | - | - | | 1.7441 | 1772 | 0.6392 | - | - | - | - | | 1.7451 | 1773 | 0.5633 | - | - | - | - | | 1.7461 | 1774 | 0.527 | - | - | - | - | | 1.7470 | 1775 | 0.792 | - | - | - | - | | 1.7480 | 1776 | 0.3731 | - | - | - | - | | 1.7490 | 1777 | 0.5097 | - | - | - | - | | 1.75 | 1778 | 0.6975 | - | - | - | - | | 1.7510 | 1779 | 0.4482 | - | - | - | - | | 1.7520 | 1780 | 0.3304 | - | - | - | - | | 1.7530 | 1781 | 0.7658 | - | - | - | - | | 1.7539 | 1782 | 0.3893 | - | - | - | - | | 1.7549 | 1783 | 0.4672 | - | - | - | - | | 1.7559 | 1784 | 0.6018 | - | - | - | - | | 1.7569 | 1785 | 0.299 | - | - | - | - | | 1.7579 | 1786 | 0.5875 | - | - | - | - | | 1.7589 | 1787 | 0.5496 | - | - | - | - | | 1.7598 | 1788 | 0.2671 | - | - | - | - | | 1.7608 | 1789 | 0.3964 | - | - | - | - | | 1.7618 | 1790 | 0.7899 | - | - | - | - | | 1.7628 | 1791 | 0.2364 | - | - | - | - | | 1.7638 | 1792 | 0.6523 | - | - | - | - | | 1.7648 | 1793 | 0.1899 | - | - | - | - | | 1.7657 | 1794 | 0.5742 | - | - | - | - | | 1.7667 | 1795 | 0.406 | - | - | - | - | | 1.7677 | 1796 | 0.3509 | - | - | - | - | | 1.7687 | 1797 | 0.2206 | - | - | - | - | | 1.7697 | 1798 | 0.7158 | - | - | - | - | | 1.7707 | 1799 | 0.403 | - | - | - | - | | 1.7717 | 1800 | 0.4324 | - | - | - | - | | 1.7726 | 1801 | 0.4338 | - | - | - | - | | 1.7736 | 1802 | 0.4808 | - | - | - | - | | 1.7746 | 1803 | 0.3099 | - | - | - | - | | 1.7756 | 1804 | 0.9415 | - | - | - | - | | 1.7766 | 1805 | 0.8304 | - | - | - | - | | 1.7776 | 1806 | 0.4728 | - | - | - | - | | 1.7785 | 1807 | 0.5041 | - | - | - | - | | 1.7795 | 1808 | 0.1113 | - | - | - | - | | 1.7805 | 1809 | 0.6698 | - | - | - | - | | 1.7815 | 1810 | 0.2146 | - | - | - | - | | 1.7825 | 1811 | 0.3076 | - | - | - | - | | 1.7835 | 1812 | 0.431 | - | - | - | - | | 1.7844 | 1813 | 0.3019 | - | - | - | - | | 1.7854 | 1814 | 0.4078 | - | - | - | - | | 1.7864 | 1815 | 0.5552 | - | - | - | - | | 1.7874 | 1816 | 0.7442 | - | - | - | - | | 1.7884 | 1817 | 0.855 | - | - | - | - | | 1.7894 | 1818 | 0.5502 | - | - | - | - | | 1.7904 | 1819 | 0.4423 | - | - | - | - | | 1.7913 | 1820 | 0.4353 | - | - | - | - | | 1.7923 | 1821 | 0.4199 | - | - | - | - | | 1.7933 | 1822 | 0.5881 | - | - | - | - | | 1.7943 | 1823 | 0.393 | - | - | - | - | | 1.7953 | 1824 | 0.8371 | - | - | - | - | | 1.7963 | 1825 | 0.8951 | - | - | - | - | | 1.7972 | 1826 | 0.5165 | - | - | - | - | | 1.7982 | 1827 | 0.2122 | - | - | - | - | | 1.7992 | 1828 | 0.5037 | - | - | - | - | | 1.8002 | 1829 | 0.4873 | - | - | - | - | | 1.8012 | 1830 | 0.5968 | - | - | - | - | | 1.8022 | 1831 | 0.4316 | - | - | - | - | | 1.8031 | 1832 | 0.1818 | - | - | - | - | | 1.8041 | 1833 | 0.2078 | - | - | - | - | | 1.8051 | 1834 | 0.5342 | - | - | - | - | | 1.8061 | 1835 | 0.2382 | - | - | - | - | | 1.8071 | 1836 | 0.1414 | 0.5629 | 0.6425 | 0.5239 | 0.6921 | | 1.8081 | 1837 | 0.3592 | - | - | - | - | | 1.8091 | 1838 | 0.893 | - | - | - | - | | 1.8100 | 1839 | 0.3389 | - | - | - | - | | 1.8110 | 1840 | 1.2053 | - | - | - | - | | 1.8120 | 1841 | 0.2925 | - | - | - | - | | 1.8130 | 1842 | 0.3789 | - | - | - | - | | 1.8140 | 1843 | 0.4395 | - | - | - | - | | 1.8150 | 1844 | 0.1913 | - | - | - | - | | 1.8159 | 1845 | 0.2172 | - | - | - | - | | 1.8169 | 1846 | 0.6572 | - | - | - | - | | 1.8179 | 1847 | 0.3379 | - | - | - | - | | 1.8189 | 1848 | 0.3634 | - | - | - | - | | 1.8199 | 1849 | 0.2917 | - | - | - | - | | 1.8209 | 1850 | 0.0589 | - | - | - | - | | 1.8219 | 1851 | 0.3823 | - | - | - | - | | 1.8228 | 1852 | 0.6974 | - | - | - | - | | 1.8238 | 1853 | 0.692 | - | - | - | - | | 1.8248 | 1854 | 0.2734 | - | - | - | - | | 1.8258 | 1855 | 0.3252 | - | - | - | - | | 1.8268 | 1856 | 0.2146 | - | - | - | - | | 1.8278 | 1857 | 0.5838 | - | - | - | - | | 1.8287 | 1858 | 0.6808 | - | - | - | - | | 1.8297 | 1859 | 0.7431 | - | - | - | - | | 1.8307 | 1860 | 0.2359 | - | - | - | - | | 1.8317 | 1861 | 0.3265 | - | - | - | - | | 1.8327 | 1862 | 0.7019 | - | - | - | - | | 1.8337 | 1863 | 1.182 | - | - | - | - | | 1.8346 | 1864 | 0.3365 | - | - | - | - | | 1.8356 | 1865 | 0.2282 | - | - | - | - | | 1.8366 | 1866 | 0.7224 | - | - | - | - | | 1.8376 | 1867 | 0.3317 | - | - | - | - | | 1.8386 | 1868 | 0.922 | - | - | - | - | | 1.8396 | 1869 | 0.7089 | - | - | - | - | | 1.8406 | 1870 | 0.1003 | - | - | - | - | | 1.8415 | 1871 | 0.1736 | - | - | - | - | | 1.8425 | 1872 | 0.8854 | - | - | - | - | | 1.8435 | 1873 | 0.2689 | - | - | - | - | | 1.8445 | 1874 | 0.2709 | - | - | - | - | | 1.8455 | 1875 | 0.4293 | - | - | - | - | | 1.8465 | 1876 | 0.6023 | - | - | - | - | | 1.8474 | 1877 | 0.817 | - | - | - | - | | 1.8484 | 1878 | 0.3847 | - | - | - | - | | 1.8494 | 1879 | 0.8794 | - | - | - | - | | 1.8504 | 1880 | 0.8067 | - | - | - | - | | 1.8514 | 1881 | 0.3147 | - | - | - | - | | 1.8524 | 1882 | 0.8664 | - | - | - | - | | 1.8533 | 1883 | 0.8473 | - | - | - | - | | 1.8543 | 1884 | 0.6057 | - | - | - | - | | 1.8553 | 1885 | 0.702 | - | - | - | - | | 1.8563 | 1886 | 1.3453 | - | - | - | - | | 1.8573 | 1887 | 0.8523 | - | - | - | - | | 1.8583 | 1888 | 0.2808 | - | - | - | - | | 1.8593 | 1889 | 0.7078 | - | - | - | - | | 1.8602 | 1890 | 0.5023 | - | - | - | - | | 1.8612 | 1891 | 0.4426 | - | - | - | - | | 1.8622 | 1892 | 0.5713 | - | - | - | - | | 1.8632 | 1893 | 0.2241 | - | - | - | - | | 1.8642 | 1894 | 0.0912 | - | - | - | - | | 1.8652 | 1895 | 0.6717 | - | - | - | - | | 1.8661 | 1896 | 0.4985 | - | - | - | - | | 1.8671 | 1897 | 0.485 | - | - | - | - | | 1.8681 | 1898 | 0.9783 | - | - | - | - | | 1.8691 | 1899 | 0.4758 | - | - | - | - | | 1.8701 | 1900 | 0.5097 | - | - | - | - | | 1.8711 | 1901 | 0.282 | - | - | - | - | | 1.8720 | 1902 | 0.8734 | - | - | - | - | | 1.8730 | 1903 | 0.5185 | - | - | - | - | | 1.8740 | 1904 | 0.2085 | - | - | - | - | | 1.875 | 1905 | 0.3836 | - | - | - | - | | 1.8760 | 1906 | 0.4029 | - | - | - | - | | 1.8770 | 1907 | 0.4809 | - | - | - | - | | 1.8780 | 1908 | 0.8473 | - | - | - | - | | 1.8789 | 1909 | 0.7449 | - | - | - | - | | 1.8799 | 1910 | 0.7715 | - | - | - | - | | 1.8809 | 1911 | 0.6199 | - | - | - | - | | 1.8819 | 1912 | 0.1564 | - | - | - | - | | 1.8829 | 1913 | 0.3665 | - | - | - | - | | 1.8839 | 1914 | 0.155 | - | - | - | - | | 1.8848 | 1915 | 0.8861 | - | - | - | - | | 1.8858 | 1916 | 0.4216 | - | - | - | - | | 1.8868 | 1917 | 0.3504 | - | - | - | - | | 1.8878 | 1918 | 0.764 | - | - | - | - | | 1.8888 | 1919 | 0.2264 | - | - | - | - | | 1.8898 | 1920 | 0.7582 | - | - | - | - | | 1.8907 | 1921 | 0.3519 | - | - | - | - | | 1.8917 | 1922 | 0.4565 | - | - | - | - | | 1.8927 | 1923 | 0.7107 | - | - | - | - | | 1.8937 | 1924 | 0.6174 | - | - | - | - | | 1.8947 | 1925 | 0.9543 | - | - | - | - | | 1.8957 | 1926 | 0.4905 | - | - | - | - | | 1.8967 | 1927 | 0.6205 | - | - | - | - | | 1.8976 | 1928 | 0.6184 | - | - | - | - | | 1.8986 | 1929 | 0.4762 | - | - | - | - | | 1.8996 | 1930 | 0.5842 | - | - | - | - | | 1.9006 | 1931 | 0.0988 | - | - | - | - | | 1.9016 | 1932 | 0.7592 | - | - | - | - | | 1.9026 | 1933 | 0.4981 | - | - | - | - | | 1.9035 | 1934 | 0.3224 | - | - | - | - | | 1.9045 | 1935 | 0.8206 | - | - | - | - | | 1.9055 | 1936 | 0.781 | - | - | - | - | | 1.9065 | 1937 | 0.6597 | - | - | - | - | | 1.9075 | 1938 | 0.3783 | - | - | - | - | | 1.9085 | 1939 | 0.3694 | - | - | - | - | | 1.9094 | 1940 | 0.4454 | - | - | - | - | | 1.9104 | 1941 | 0.2308 | - | - | - | - | | 1.9114 | 1942 | 0.325 | - | - | - | - | | 1.9124 | 1943 | 0.4636 | - | - | - | - | | 1.9134 | 1944 | 0.2686 | - | - | - | - | | 1.9144 | 1945 | 0.6857 | - | - | - | - | | 1.9154 | 1946 | 0.5308 | - | - | - | - | | 1.9163 | 1947 | 0.4918 | - | - | - | - | | 1.9173 | 1948 | 0.4506 | - | - | - | - | | 1.9183 | 1949 | 0.5216 | - | - | - | - | | 1.9193 | 1950 | 0.7475 | - | - | - | - | | 1.9203 | 1951 | 0.6182 | - | - | - | - | | 1.9213 | 1952 | 0.3789 | - | - | - | - | | 1.9222 | 1953 | 0.3469 | - | - | - | - | | 1.9232 | 1954 | 0.5435 | - | - | - | - | | 1.9242 | 1955 | 0.1886 | - | - | - | - | | 1.9252 | 1956 | 0.7569 | - | - | - | - | | 1.9262 | 1957 | 0.3396 | - | - | - | - | | 1.9272 | 1958 | 0.5911 | - | - | - | - | | 1.9281 | 1959 | 0.2211 | - | - | - | - | | 1.9291 | 1960 | 0.4902 | - | - | - | - | | 1.9301 | 1961 | 0.5863 | - | - | - | - | | 1.9311 | 1962 | 0.3685 | - | - | - | - | | 1.9321 | 1963 | 0.5296 | - | - | - | - | | 1.9331 | 1964 | 0.2576 | - | - | - | - | | 1.9341 | 1965 | 0.2258 | - | - | - | - | | 1.9350 | 1966 | 0.4208 | - | - | - | - | | 1.9360 | 1967 | 0.4088 | - | - | - | - | | 1.9370 | 1968 | 0.4198 | - | - | - | - | | 1.9380 | 1969 | 0.3591 | - | - | - | - | | 1.9390 | 1970 | 0.2849 | - | - | - | - | | 1.9400 | 1971 | 0.6841 | - | - | - | - | | 1.9409 | 1972 | 0.1712 | - | - | - | - | | 1.9419 | 1973 | 0.2629 | - | - | - | - | | 1.9429 | 1974 | 0.444 | - | - | - | - | | 1.9439 | 1975 | 0.1811 | - | - | - | - | | 1.9449 | 1976 | 0.4874 | - | - | - | - | | 1.9459 | 1977 | 0.6704 | - | - | - | - | | 1.9469 | 1978 | 0.1352 | - | - | - | - | | 1.9478 | 1979 | 0.243 | - | - | - | - | | 1.9488 | 1980 | 0.7386 | - | - | - | - | | 1.9498 | 1981 | 0.188 | - | - | - | - | | 1.9508 | 1982 | 0.4885 | - | - | - | - | | 1.9518 | 1983 | 0.398 | - | - | - | - | | 1.9528 | 1984 | 0.4067 | - | - | - | - | | 1.9537 | 1985 | 0.2526 | - | - | - | - | | 1.9547 | 1986 | 0.4214 | - | - | - | - | | 1.9557 | 1987 | 0.699 | - | - | - | - | | 1.9567 | 1988 | 1.1089 | - | - | - | - | | 1.9577 | 1989 | 0.6792 | 0.5615 | 0.6588 | 0.5221 | 0.6928 | | 1.9587 | 1990 | 0.4697 | - | - | - | - | | 1.9596 | 1991 | 0.1804 | - | - | - | - | | 1.9606 | 1992 | 0.9363 | - | - | - | - | | 1.9616 | 1993 | 0.315 | - | - | - | - | | 1.9626 | 1994 | 0.216 | - | - | - | - | | 1.9636 | 1995 | 1.0211 | - | - | - | - | | 1.9646 | 1996 | 0.2225 | - | - | - | - | | 1.9656 | 1997 | 0.3734 | - | - | - | - | | 1.9665 | 1998 | 1.1127 | - | - | - | - | | 1.9675 | 1999 | 0.5302 | - | - | - | - | | 1.9685 | 2000 | 0.4619 | - | - | - | - | | 1.9695 | 2001 | 0.4452 | - | - | - | - | | 1.9705 | 2002 | 0.2555 | - | - | - | - | | 1.9715 | 2003 | 0.3951 | - | - | - | - | | 1.9724 | 2004 | 0.4926 | - | - | - | - | | 1.9734 | 2005 | 0.4563 | - | - | - | - | | 1.9744 | 2006 | 0.2664 | - | - | - | - | | 1.9754 | 2007 | 0.5579 | - | - | - | - | | 1.9764 | 2008 | 0.4412 | - | - | - | - | | 1.9774 | 2009 | 0.641 | - | - | - | - | | 1.9783 | 2010 | 0.2505 | - | - | - | - | | 1.9793 | 2011 | 0.5773 | - | - | - | - | | 1.9803 | 2012 | 0.4118 | - | - | - | - | | 1.9813 | 2013 | 0.6585 | - | - | - | - | | 1.9823 | 2014 | 1.0842 | - | - | - | - | | 1.9833 | 2015 | 0.5697 | - | - | - | - | | 1.9843 | 2016 | 0.4335 | - | - | - | - | | 1.9852 | 2017 | 1.0189 | - | - | - | - | | 1.9862 | 2018 | 0.7046 | - | - | - | - | | 1.9872 | 2019 | 0.2414 | - | - | - | - | | 1.9882 | 2020 | 0.3538 | - | - | - | - | | 1.9892 | 2021 | 0.6771 | - | - | - | - | | 1.9902 | 2022 | 0.4546 | - | - | - | - | | 1.9911 | 2023 | 0.3169 | - | - | - | - | | 1.9921 | 2024 | 0.4244 | - | - | - | - | | 1.9931 | 2025 | 0.0684 | - | - | - | - | | 1.9941 | 2026 | 0.4007 | - | - | - | - | | 1.9951 | 2027 | 0.3198 | - | - | - | - | | 1.9961 | 2028 | 0.1821 | - | - | - | - | | 1.9970 | 2029 | 0.491 | - | - | - | - | | 1.9980 | 2030 | 0.8449 | - | - | - | - | | 1.9990 | 2031 | 0.2122 | - | - | - | - | | 2.0 | 2032 | 0.212 | - | - | - | - | | 2.0010 | 2033 | 0.5254 | - | - | - | - | | 2.0020 | 2034 | 0.7473 | - | - | - | - | | 2.0030 | 2035 | 0.0799 | - | - | - | - | | 2.0039 | 2036 | 0.4975 | - | - | - | - | | 2.0049 | 2037 | 0.4425 | - | - | - | - | | 2.0059 | 2038 | 0.3234 | - | - | - | - | | 2.0069 | 2039 | 0.3183 | - | - | - | - | | 2.0079 | 2040 | 0.3073 | - | - | - | - | | 2.0089 | 2041 | 0.2292 | - | - | - | - | | 2.0098 | 2042 | 0.3874 | - | - | - | - | | 2.0108 | 2043 | 0.6781 | - | - | - | - | | 2.0118 | 2044 | 0.6645 | - | - | - | - | | 2.0128 | 2045 | 0.2373 | - | - | - | - | | 2.0138 | 2046 | 0.3813 | - | - | - | - | | 2.0148 | 2047 | 0.88 | - | - | - | - | | 2.0157 | 2048 | 0.3683 | - | - | - | - | | 2.0167 | 2049 | 0.519 | - | - | - | - | | 2.0177 | 2050 | 1.0128 | - | - | - | - | | 2.0187 | 2051 | 1.1026 | - | - | - | - | | 2.0197 | 2052 | 0.4198 | - | - | - | - | | 2.0207 | 2053 | 0.1097 | - | - | - | - | | 2.0217 | 2054 | 0.4641 | - | - | - | - | | 2.0226 | 2055 | 0.4183 | - | - | - | - | | 2.0236 | 2056 | 0.2043 | - | - | - | - | | 2.0246 | 2057 | 0.7447 | - | - | - | - | | 2.0256 | 2058 | 0.5261 | - | - | - | - | | 2.0266 | 2059 | 1.0812 | - | - | - | - | | 2.0276 | 2060 | 0.3421 | - | - | - | - | | 2.0285 | 2061 | 0.5063 | - | - | - | - | | 2.0295 | 2062 | 0.2861 | - | - | - | - | | 2.0305 | 2063 | 0.0981 | - | - | - | - | | 2.0315 | 2064 | 0.5772 | - | - | - | - | | 2.0325 | 2065 | 0.0832 | - | - | - | - | | 2.0335 | 2066 | 0.3156 | - | - | - | - | | 2.0344 | 2067 | 0.1706 | - | - | - | - | | 2.0354 | 2068 | 0.3911 | - | - | - | - | | 2.0364 | 2069 | 0.6807 | - | - | - | - | | 2.0374 | 2070 | 0.5363 | - | - | - | - | | 2.0384 | 2071 | 0.5497 | - | - | - | - | | 2.0394 | 2072 | 0.7298 | - | - | - | - | | 2.0404 | 2073 | 0.3255 | - | - | - | - | | 2.0413 | 2074 | 0.2934 | - | - | - | - | | 2.0423 | 2075 | 0.2041 | - | - | - | - | | 2.0433 | 2076 | 0.6235 | - | - | - | - | | 2.0443 | 2077 | 0.4104 | - | - | - | - | | 2.0453 | 2078 | 0.1305 | - | - | - | - | | 2.0463 | 2079 | 0.1591 | - | - | - | - | | 2.0472 | 2080 | 0.3531 | - | - | - | - | | 2.0482 | 2081 | 0.2944 | - | - | - | - | | 2.0492 | 2082 | 0.3121 | - | - | - | - | | 2.0502 | 2083 | 0.5418 | - | - | - | - | | 2.0512 | 2084 | 0.8162 | - | - | - | - | | 2.0522 | 2085 | 0.4787 | - | - | - | - | | 2.0531 | 2086 | 0.1146 | - | - | - | - | | 2.0541 | 2087 | 0.2373 | - | - | - | - | | 2.0551 | 2088 | 0.1548 | - | - | - | - | | 2.0561 | 2089 | 0.4515 | - | - | - | - | | 2.0571 | 2090 | 0.4699 | - | - | - | - | | 2.0581 | 2091 | 0.3675 | - | - | - | - | | 2.0591 | 2092 | 0.2537 | - | - | - | - | | 2.0600 | 2093 | 0.4433 | - | - | - | - | | 2.0610 | 2094 | 0.3595 | - | - | - | - | | 2.0620 | 2095 | 0.4329 | - | - | - | - | | 2.0630 | 2096 | 0.1803 | - | - | - | - | | 2.0640 | 2097 | 0.6451 | - | - | - | - | | 2.0650 | 2098 | 0.3992 | - | - | - | - | | 2.0659 | 2099 | 0.2349 | - | - | - | - | | 2.0669 | 2100 | 0.1526 | - | - | - | - | | 2.0679 | 2101 | 0.133 | - | - | - | - | | 2.0689 | 2102 | 0.8299 | - | - | - | - | | 2.0699 | 2103 | 0.5157 | - | - | - | - | | 2.0709 | 2104 | 0.4256 | - | - | - | - | | 2.0719 | 2105 | 0.3434 | - | - | - | - | | 2.0728 | 2106 | 0.3479 | - | - | - | - | | 2.0738 | 2107 | 0.2604 | - | - | - | - | | 2.0748 | 2108 | 0.3513 | - | - | - | - | | 2.0758 | 2109 | 0.8243 | - | - | - | - | | 2.0768 | 2110 | 0.2352 | - | - | - | - | | 2.0778 | 2111 | 0.2082 | - | - | - | - | | 2.0787 | 2112 | 0.145 | - | - | - | - | | 2.0797 | 2113 | 1.3586 | - | - | - | - | | 2.0807 | 2114 | 0.3679 | - | - | - | - | | 2.0817 | 2115 | 0.3545 | - | - | - | - | | 2.0827 | 2116 | 0.6441 | - | - | - | - | | 2.0837 | 2117 | 0.8558 | - | - | - | - | | 2.0846 | 2118 | 0.4696 | - | - | - | - | | 2.0856 | 2119 | 0.8495 | - | - | - | - | | 2.0866 | 2120 | 0.8995 | - | - | - | - | | 2.0876 | 2121 | 0.3276 | - | - | - | - | | 2.0886 | 2122 | 0.7393 | - | - | - | - | | 2.0896 | 2123 | 0.048 | - | - | - | - | | 2.0906 | 2124 | 0.2266 | - | - | - | - | | 2.0915 | 2125 | 0.6785 | - | - | - | - | | 2.0925 | 2126 | 0.3503 | - | - | - | - | | 2.0935 | 2127 | 0.1894 | - | - | - | - | | 2.0945 | 2128 | 1.2168 | - | - | - | - | | 2.0955 | 2129 | 0.1664 | - | - | - | - | | 2.0965 | 2130 | 0.3649 | - | - | - | - | | 2.0974 | 2131 | 0.5949 | - | - | - | - | | 2.0984 | 2132 | 0.571 | - | - | - | - | | 2.0994 | 2133 | 0.3775 | - | - | - | - | | 2.1004 | 2134 | 0.3978 | - | - | - | - | | 2.1014 | 2135 | 0.4804 | - | - | - | - | | 2.1024 | 2136 | 0.2534 | - | - | - | - | | 2.1033 | 2137 | 0.2701 | - | - | - | - | | 2.1043 | 2138 | 0.2538 | - | - | - | - | | 2.1053 | 2139 | 0.6239 | - | - | - | - | | 2.1063 | 2140 | 0.7077 | - | - | - | - | | 2.1073 | 2141 | 0.1929 | - | - | - | - | | 2.1083 | 2142 | 0.1367 | 0.5293 | 0.6488 | 0.5099 | 0.7068 | | 2.1093 | 2143 | 0.1882 | - | - | - | - | | 2.1102 | 2144 | 0.4297 | - | - | - | - | | 2.1112 | 2145 | 0.5098 | - | - | - | - | | 2.1122 | 2146 | 0.3554 | - | - | - | - | | 2.1132 | 2147 | 0.5338 | - | - | - | - | | 2.1142 | 2148 | 0.4045 | - | - | - | - | | 2.1152 | 2149 | 0.6929 | - | - | - | - | | 2.1161 | 2150 | 0.3397 | - | - | - | - | | 2.1171 | 2151 | 0.4817 | - | - | - | - | | 2.1181 | 2152 | 0.3459 | - | - | - | - | | 2.1191 | 2153 | 0.6743 | - | - | - | - | | 2.1201 | 2154 | 0.461 | - | - | - | - | | 2.1211 | 2155 | 0.4665 | - | - | - | - | | 2.1220 | 2156 | 0.2519 | - | - | - | - | | 2.1230 | 2157 | 0.4271 | - | - | - | - | | 2.1240 | 2158 | 0.1528 | - | - | - | - | | 2.125 | 2159 | 0.3622 | - | - | - | - | | 2.1260 | 2160 | 0.2196 | - | - | - | - | | 2.1270 | 2161 | 0.2029 | - | - | - | - | | 2.1280 | 2162 | 0.7731 | - | - | - | - | | 2.1289 | 2163 | 0.2184 | - | - | - | - | | 2.1299 | 2164 | 0.4623 | - | - | - | - | | 2.1309 | 2165 | 0.1743 | - | - | - | - | | 2.1319 | 2166 | 0.1833 | - | - | - | - | | 2.1329 | 2167 | 0.274 | - | - | - | - | | 2.1339 | 2168 | 0.8368 | - | - | - | - | | 2.1348 | 2169 | 0.2218 | - | - | - | - | | 2.1358 | 2170 | 0.3106 | - | - | - | - | | 2.1368 | 2171 | 0.6703 | - | - | - | - | | 2.1378 | 2172 | 0.2926 | - | - | - | - | | 2.1388 | 2173 | 0.1584 | - | - | - | - | | 2.1398 | 2174 | 0.2456 | - | - | - | - | | 2.1407 | 2175 | 0.4458 | - | - | - | - | | 2.1417 | 2176 | 0.494 | - | - | - | - | | 2.1427 | 2177 | 0.4601 | - | - | - | - | | 2.1437 | 2178 | 0.6571 | - | - | - | - | | 2.1447 | 2179 | 0.1915 | - | - | - | - | | 2.1457 | 2180 | 0.2892 | - | - | - | - | | 2.1467 | 2181 | 0.3592 | - | - | - | - | | 2.1476 | 2182 | 0.89 | - | - | - | - | | 2.1486 | 2183 | 0.4856 | - | - | - | - | | 2.1496 | 2184 | 0.2403 | - | - | - | - | | 2.1506 | 2185 | 0.263 | - | - | - | - | | 2.1516 | 2186 | 0.5816 | - | - | - | - | | 2.1526 | 2187 | 0.2912 | - | - | - | - | | 2.1535 | 2188 | 0.2722 | - | - | - | - | | 2.1545 | 2189 | 0.3503 | - | - | - | - | | 2.1555 | 2190 | 0.3788 | - | - | - | - | | 2.1565 | 2191 | 0.4935 | - | - | - | - | | 2.1575 | 2192 | 0.2505 | - | - | - | - | | 2.1585 | 2193 | 0.3122 | - | - | - | - | | 2.1594 | 2194 | 0.2363 | - | - | - | - | | 2.1604 | 2195 | 0.4411 | - | - | - | - | | 2.1614 | 2196 | 0.5624 | - | - | - | - | | 2.1624 | 2197 | 0.1555 | - | - | - | - | | 2.1634 | 2198 | 0.4505 | - | - | - | - | | 2.1644 | 2199 | 0.2699 | - | - | - | - | | 2.1654 | 2200 | 0.2575 | - | - | - | - | | 2.1663 | 2201 | 0.2773 | - | - | - | - | | 2.1673 | 2202 | 0.7659 | - | - | - | - | | 2.1683 | 2203 | 0.5827 | - | - | - | - | | 2.1693 | 2204 | 0.4094 | - | - | - | - | | 2.1703 | 2205 | 0.5912 | - | - | - | - | | 2.1713 | 2206 | 0.2814 | - | - | - | - | | 2.1722 | 2207 | 0.6024 | - | - | - | - | | 2.1732 | 2208 | 0.4436 | - | - | - | - | | 2.1742 | 2209 | 0.2696 | - | - | - | - | | 2.1752 | 2210 | 0.1876 | - | - | - | - | | 2.1762 | 2211 | 0.4322 | - | - | - | - | | 2.1772 | 2212 | 0.401 | - | - | - | - | | 2.1781 | 2213 | 0.4703 | - | - | - | - | | 2.1791 | 2214 | 0.2829 | - | - | - | - | | 2.1801 | 2215 | 0.217 | - | - | - | - | | 2.1811 | 2216 | 0.2039 | - | - | - | - | | 2.1821 | 2217 | 0.3816 | - | - | - | - | | 2.1831 | 2218 | 0.3872 | - | - | - | - | | 2.1841 | 2219 | 0.5381 | - | - | - | - | | 2.1850 | 2220 | 0.3297 | - | - | - | - | | 2.1860 | 2221 | 0.7472 | - | - | - | - | | 2.1870 | 2222 | 0.409 | - | - | - | - | | 2.1880 | 2223 | 0.3398 | - | - | - | - | | 2.1890 | 2224 | 0.5215 | - | - | - | - | | 2.1900 | 2225 | 0.3045 | - | - | - | - | | 2.1909 | 2226 | 0.195 | - | - | - | - | | 2.1919 | 2227 | 0.457 | - | - | - | - | | 2.1929 | 2228 | 0.387 | - | - | - | - | | 2.1939 | 2229 | 0.3079 | - | - | - | - | | 2.1949 | 2230 | 0.7337 | - | - | - | - | | 2.1959 | 2231 | 0.3105 | - | - | - | - | | 2.1969 | 2232 | 0.4746 | - | - | - | - | | 2.1978 | 2233 | 0.4945 | - | - | - | - | | 2.1988 | 2234 | 0.7614 | - | - | - | - | | 2.1998 | 2235 | 0.5402 | - | - | - | - | | 2.2008 | 2236 | 0.7004 | - | - | - | - | | 2.2018 | 2237 | 0.2853 | - | - | - | - | | 2.2028 | 2238 | 0.061 | - | - | - | - | | 2.2037 | 2239 | 0.9005 | - | - | - | - | | 2.2047 | 2240 | 0.4169 | - | - | - | - | | 2.2057 | 2241 | 0.5792 | - | - | - | - | | 2.2067 | 2242 | 0.2046 | - | - | - | - | | 2.2077 | 2243 | 0.876 | - | - | - | - | | 2.2087 | 2244 | 0.3884 | - | - | - | - | | 2.2096 | 2245 | 0.826 | - | - | - | - | | 2.2106 | 2246 | 0.3453 | - | - | - | - | | 2.2116 | 2247 | 0.1741 | - | - | - | - | | 2.2126 | 2248 | 0.1238 | - | - | - | - | | 2.2136 | 2249 | 0.3539 | - | - | - | - | | 2.2146 | 2250 | 0.6756 | - | - | - | - | | 2.2156 | 2251 | 0.2457 | - | - | - | - | | 2.2165 | 2252 | 0.1128 | - | - | - | - | | 2.2175 | 2253 | 0.5331 | - | - | - | - | | 2.2185 | 2254 | 0.499 | - | - | - | - | | 2.2195 | 2255 | 0.9985 | - | - | - | - | | 2.2205 | 2256 | 0.5565 | - | - | - | - | | 2.2215 | 2257 | 0.545 | - | - | - | - | | 2.2224 | 2258 | 0.6449 | - | - | - | - | | 2.2234 | 2259 | 0.8312 | - | - | - | - | | 2.2244 | 2260 | 0.155 | - | - | - | - | | 2.2254 | 2261 | 0.8201 | - | - | - | - | | 2.2264 | 2262 | 0.2976 | - | - | - | - | | 2.2274 | 2263 | 0.1666 | - | - | - | - | | 2.2283 | 2264 | 0.2341 | - | - | - | - | | 2.2293 | 2265 | 0.1533 | - | - | - | - | | 2.2303 | 2266 | 0.2068 | - | - | - | - | | 2.2313 | 2267 | 0.2045 | - | - | - | - | | 2.2323 | 2268 | 0.2308 | - | - | - | - | | 2.2333 | 2269 | 0.1454 | - | - | - | - | | 2.2343 | 2270 | 0.2369 | - | - | - | - | | 2.2352 | 2271 | 0.1508 | - | - | - | - | | 2.2362 | 2272 | 0.4161 | - | - | - | - | | 2.2372 | 2273 | 0.2739 | - | - | - | - | | 2.2382 | 2274 | 0.7653 | - | - | - | - | | 2.2392 | 2275 | 0.3751 | - | - | - | - | | 2.2402 | 2276 | 0.6602 | - | - | - | - | | 2.2411 | 2277 | 0.2636 | - | - | - | - | | 2.2421 | 2278 | 0.3619 | - | - | - | - | | 2.2431 | 2279 | 1.2106 | - | - | - | - | | 2.2441 | 2280 | 0.5429 | - | - | - | - | | 2.2451 | 2281 | 0.2715 | - | - | - | - | | 2.2461 | 2282 | 0.3696 | - | - | - | - | | 2.2470 | 2283 | 0.5001 | - | - | - | - | | 2.2480 | 2284 | 0.263 | - | - | - | - | | 2.2490 | 2285 | 0.2834 | - | - | - | - | | 2.25 | 2286 | 0.3014 | - | - | - | - | | 2.2510 | 2287 | 0.1766 | - | - | - | - | | 2.2520 | 2288 | 0.452 | - | - | - | - | | 2.2530 | 2289 | 0.3325 | - | - | - | - | | 2.2539 | 2290 | 0.3046 | - | - | - | - | | 2.2549 | 2291 | 0.0783 | - | - | - | - | | 2.2559 | 2292 | 0.5475 | - | - | - | - | | 2.2569 | 2293 | 0.1652 | - | - | - | - | | 2.2579 | 2294 | 0.2344 | - | - | - | - | | 2.2589 | 2295 | 0.6825 | 0.5027 | 0.6741 | 0.5178 | 0.7015 | | 2.2598 | 2296 | 0.172 | - | - | - | - | | 2.2608 | 2297 | 0.1702 | - | - | - | - | | 2.2618 | 2298 | 0.2923 | - | - | - | - | | 2.2628 | 2299 | 0.9845 | - | - | - | - | | 2.2638 | 2300 | 0.3264 | - | - | - | - | | 2.2648 | 2301 | 0.3324 | - | - | - | - | | 2.2657 | 2302 | 0.133 | - | - | - | - | | 2.2667 | 2303 | 0.5128 | - | - | - | - | | 2.2677 | 2304 | 0.3315 | - | - | - | - | | 2.2687 | 2305 | 0.8059 | - | - | - | - | | 2.2697 | 2306 | 0.4871 | - | - | - | - | | 2.2707 | 2307 | 0.4682 | - | - | - | - | | 2.2717 | 2308 | 0.3445 | - | - | - | - | | 2.2726 | 2309 | 0.6977 | - | - | - | - | | 2.2736 | 2310 | 0.2097 | - | - | - | - | | 2.2746 | 2311 | 0.9707 | - | - | - | - | | 2.2756 | 2312 | 0.3347 | - | - | - | - | | 2.2766 | 2313 | 0.1578 | - | - | - | - | | 2.2776 | 2314 | 0.2311 | - | - | - | - | | 2.2785 | 2315 | 0.3391 | - | - | - | - | | 2.2795 | 2316 | 0.3266 | - | - | - | - | | 2.2805 | 2317 | 0.4752 | - | - | - | - | | 2.2815 | 2318 | 0.3747 | - | - | - | - | | 2.2825 | 2319 | 0.2869 | - | - | - | - | | 2.2835 | 2320 | 0.2732 | - | - | - | - | | 2.2844 | 2321 | 0.5805 | - | - | - | - | | 2.2854 | 2322 | 0.6248 | - | - | - | - | | 2.2864 | 2323 | 0.1827 | - | - | - | - | | 2.2874 | 2324 | 0.0837 | - | - | - | - | | 2.2884 | 2325 | 0.3561 | - | - | - | - | | 2.2894 | 2326 | 0.2894 | - | - | - | - | | 2.2904 | 2327 | 0.4555 | - | - | - | - | | 2.2913 | 2328 | 0.5762 | - | - | - | - | | 2.2923 | 2329 | 0.6998 | - | - | - | - | | 2.2933 | 2330 | 0.548 | - | - | - | - | | 2.2943 | 2331 | 0.4924 | - | - | - | - | | 2.2953 | 2332 | 0.5409 | - | - | - | - | | 2.2963 | 2333 | 0.7607 | - | - | - | - | | 2.2972 | 2334 | 0.4493 | - | - | - | - | | 2.2982 | 2335 | 0.1872 | - | - | - | - | | 2.2992 | 2336 | 0.2478 | - | - | - | - | | 2.3002 | 2337 | 0.4008 | - | - | - | - | | 2.3012 | 2338 | 0.2723 | - | - | - | - | | 2.3022 | 2339 | 0.4008 | - | - | - | - | | 2.3031 | 2340 | 0.4166 | - | - | - | - | | 2.3041 | 2341 | 0.2233 | - | - | - | - | | 2.3051 | 2342 | 0.606 | - | - | - | - | | 2.3061 | 2343 | 0.7489 | - | - | - | - | | 2.3071 | 2344 | 0.6439 | - | - | - | - | | 2.3081 | 2345 | 0.5636 | - | - | - | - | | 2.3091 | 2346 | 0.1038 | - | - | - | - | | 2.3100 | 2347 | 0.5164 | - | - | - | - | | 2.3110 | 2348 | 0.3576 | - | - | - | - | | 2.3120 | 2349 | 0.5828 | - | - | - | - | | 2.3130 | 2350 | 0.7128 | - | - | - | - | | 2.3140 | 2351 | 0.4945 | - | - | - | - | | 2.3150 | 2352 | 0.3841 | - | - | - | - | | 2.3159 | 2353 | 0.598 | - | - | - | - | | 2.3169 | 2354 | 0.2705 | - | - | - | - | | 2.3179 | 2355 | 0.2488 | - | - | - | - | | 2.3189 | 2356 | 0.2014 | - | - | - | - | | 2.3199 | 2357 | 0.1288 | - | - | - | - | | 2.3209 | 2358 | 0.2358 | - | - | - | - | | 2.3219 | 2359 | 0.2984 | - | - | - | - | | 2.3228 | 2360 | 0.1404 | - | - | - | - | | 2.3238 | 2361 | 0.1777 | - | - | - | - | | 2.3248 | 2362 | 0.7692 | - | - | - | - | | 2.3258 | 2363 | 0.1564 | - | - | - | - | | 2.3268 | 2364 | 0.1589 | - | - | - | - | | 2.3278 | 2365 | 0.517 | - | - | - | - | | 2.3287 | 2366 | 0.0561 | - | - | - | - | | 2.3297 | 2367 | 0.6459 | - | - | - | - | | 2.3307 | 2368 | 0.3254 | - | - | - | - | | 2.3317 | 2369 | 0.8167 | - | - | - | - | | 2.3327 | 2370 | 0.6455 | - | - | - | - | | 2.3337 | 2371 | 0.4716 | - | - | - | - | | 2.3346 | 2372 | 0.4538 | - | - | - | - | | 2.3356 | 2373 | 0.2246 | - | - | - | - | | 2.3366 | 2374 | 0.2168 | - | - | - | - | | 2.3376 | 2375 | 0.1789 | - | - | - | - | | 2.3386 | 2376 | 0.6535 | - | - | - | - | | 2.3396 | 2377 | 0.1169 | - | - | - | - | | 2.3406 | 2378 | 0.3429 | - | - | - | - | | 2.3415 | 2379 | 0.4071 | - | - | - | - | | 2.3425 | 2380 | 0.2805 | - | - | - | - | | 2.3435 | 2381 | 0.3936 | - | - | - | - | | 2.3445 | 2382 | 0.5997 | - | - | - | - | | 2.3455 | 2383 | 0.4108 | - | - | - | - | | 2.3465 | 2384 | 0.0802 | - | - | - | - | | 2.3474 | 2385 | 0.428 | - | - | - | - | | 2.3484 | 2386 | 0.9649 | - | - | - | - | | 2.3494 | 2387 | 0.3741 | - | - | - | - | | 2.3504 | 2388 | 0.2907 | - | - | - | - | | 2.3514 | 2389 | 0.1665 | - | - | - | - | | 2.3524 | 2390 | 0.464 | - | - | - | - | | 2.3533 | 2391 | 0.2636 | - | - | - | - | | 2.3543 | 2392 | 0.1748 | - | - | - | - | | 2.3553 | 2393 | 0.2673 | - | - | - | - | | 2.3563 | 2394 | 0.4091 | - | - | - | - | | 2.3573 | 2395 | 0.3149 | - | - | - | - | | 2.3583 | 2396 | 0.222 | - | - | - | - | | 2.3593 | 2397 | 0.3191 | - | - | - | - | | 2.3602 | 2398 | 0.6364 | - | - | - | - | | 2.3612 | 2399 | 0.3431 | - | - | - | - | | 2.3622 | 2400 | 0.3021 | - | - | - | - | | 2.3632 | 2401 | 0.5573 | - | - | - | - | | 2.3642 | 2402 | 0.3081 | - | - | - | - | | 2.3652 | 2403 | 0.3263 | - | - | - | - | | 2.3661 | 2404 | 0.345 | - | - | - | - | | 2.3671 | 2405 | 0.2477 | - | - | - | - | | 2.3681 | 2406 | 0.5129 | - | - | - | - | | 2.3691 | 2407 | 0.1907 | - | - | - | - | | 2.3701 | 2408 | 0.5318 | - | - | - | - | | 2.3711 | 2409 | 0.5115 | - | - | - | - | | 2.3720 | 2410 | 0.5919 | - | - | - | - | | 2.3730 | 2411 | 0.2424 | - | - | - | - | | 2.3740 | 2412 | 0.3523 | - | - | - | - | | 2.375 | 2413 | 0.2838 | - | - | - | - | | 2.3760 | 2414 | 0.5143 | - | - | - | - | | 2.3770 | 2415 | 0.2617 | - | - | - | - | | 2.3780 | 2416 | 0.2902 | - | - | - | - | | 2.3789 | 2417 | 0.2989 | - | - | - | - | | 2.3799 | 2418 | 0.1996 | - | - | - | - | | 2.3809 | 2419 | 0.3886 | - | - | - | - | | 2.3819 | 2420 | 0.884 | - | - | - | - | | 2.3829 | 2421 | 0.311 | - | - | - | - | | 2.3839 | 2422 | 0.3463 | - | - | - | - | | 2.3848 | 2423 | 0.3554 | - | - | - | - | | 2.3858 | 2424 | 0.4 | - | - | - | - | | 2.3868 | 2425 | 0.271 | - | - | - | - | | 2.3878 | 2426 | 0.3827 | - | - | - | - | | 2.3888 | 2427 | 0.3209 | - | - | - | - | | 2.3898 | 2428 | 0.3825 | - | - | - | - | | 2.3907 | 2429 | 0.4422 | - | - | - | - | | 2.3917 | 2430 | 0.2985 | - | - | - | - | | 2.3927 | 2431 | 0.0181 | - | - | - | - | | 2.3937 | 2432 | 0.7523 | - | - | - | - | | 2.3947 | 2433 | 0.1871 | - | - | - | - | | 2.3957 | 2434 | 0.4331 | - | - | - | - | | 2.3967 | 2435 | 0.0969 | - | - | - | - | | 2.3976 | 2436 | 0.6248 | - | - | - | - | | 2.3986 | 2437 | 0.177 | - | - | - | - | | 2.3996 | 2438 | 0.4363 | - | - | - | - | | 2.4006 | 2439 | 0.6808 | - | - | - | - | | 2.4016 | 2440 | 0.3351 | - | - | - | - | | 2.4026 | 2441 | 0.1954 | - | - | - | - | | 2.4035 | 2442 | 0.4625 | - | - | - | - | | 2.4045 | 2443 | 0.1783 | - | - | - | - | | 2.4055 | 2444 | 0.3819 | - | - | - | - | | 2.4065 | 2445 | 0.7562 | - | - | - | - | | 2.4075 | 2446 | 0.154 | - | - | - | - | | 2.4085 | 2447 | 0.5065 | - | - | - | - | | 2.4094 | 2448 | 0.3614 | 0.5045 | 0.6699 | 0.5129 | 0.7047 | | 2.4104 | 2449 | 0.261 | - | - | - | - | | 2.4114 | 2450 | 0.0852 | - | - | - | - | | 2.4124 | 2451 | 0.252 | - | - | - | - | | 2.4134 | 2452 | 0.057 | - | - | - | - | | 2.4144 | 2453 | 0.7811 | - | - | - | - | | 2.4154 | 2454 | 0.3099 | - | - | - | - | | 2.4163 | 2455 | 0.1505 | - | - | - | - | | 2.4173 | 2456 | 0.1391 | - | - | - | - | | 2.4183 | 2457 | 0.2339 | - | - | - | - | | 2.4193 | 2458 | 0.3976 | - | - | - | - | | 2.4203 | 2459 | 0.3867 | - | - | - | - | | 2.4213 | 2460 | 0.5535 | - | - | - | - | | 2.4222 | 2461 | 0.334 | - | - | - | - | | 2.4232 | 2462 | 0.1176 | - | - | - | - | | 2.4242 | 2463 | 0.363 | - | - | - | - | | 2.4252 | 2464 | 0.6583 | - | - | - | - | | 2.4262 | 2465 | 0.4029 | - | - | - | - | | 2.4272 | 2466 | 0.3915 | - | - | - | - | | 2.4281 | 2467 | 0.2261 | - | - | - | - | | 2.4291 | 2468 | 0.3856 | - | - | - | - | | 2.4301 | 2469 | 0.4336 | - | - | - | - | | 2.4311 | 2470 | 0.4369 | - | - | - | - | | 2.4321 | 2471 | 0.1303 | - | - | - | - | | 2.4331 | 2472 | 0.6326 | - | - | - | - | | 2.4341 | 2473 | 0.1735 | - | - | - | - | | 2.4350 | 2474 | 0.5125 | - | - | - | - | | 2.4360 | 2475 | 0.1103 | - | - | - | - | | 2.4370 | 2476 | 0.2421 | - | - | - | - | | 2.4380 | 2477 | 0.2513 | - | - | - | - | | 2.4390 | 2478 | 0.1199 | - | - | - | - | | 2.4400 | 2479 | 0.1829 | - | - | - | - | | 2.4409 | 2480 | 0.2527 | - | - | - | - | | 2.4419 | 2481 | 0.2036 | - | - | - | - | | 2.4429 | 2482 | 0.4078 | - | - | - | - | | 2.4439 | 2483 | 0.2764 | - | - | - | - | | 2.4449 | 2484 | 0.4487 | - | - | - | - | | 2.4459 | 2485 | 0.6344 | - | - | - | - | | 2.4469 | 2486 | 0.1742 | - | - | - | - | | 2.4478 | 2487 | 0.5259 | - | - | - | - | | 2.4488 | 2488 | 0.6818 | - | - | - | - | | 2.4498 | 2489 | 0.7824 | - | - | - | - | | 2.4508 | 2490 | 0.0713 | - | - | - | - | | 2.4518 | 2491 | 0.2966 | - | - | - | - | | 2.4528 | 2492 | 0.7014 | - | - | - | - | | 2.4537 | 2493 | 0.1383 | - | - | - | - | | 2.4547 | 2494 | 0.1846 | - | - | - | - | | 2.4557 | 2495 | 0.4537 | - | - | - | - | | 2.4567 | 2496 | 0.2155 | - | - | - | - | | 2.4577 | 2497 | 0.4813 | - | - | - | - | | 2.4587 | 2498 | 0.6803 | - | - | - | - | | 2.4596 | 2499 | 0.0744 | - | - | - | - | | 2.4606 | 2500 | 0.451 | - | - | - | - | | 2.4616 | 2501 | 0.4568 | - | - | - | - | | 2.4626 | 2502 | 0.1182 | - | - | - | - | | 2.4636 | 2503 | 0.3563 | - | - | - | - | | 2.4646 | 2504 | 0.2821 | - | - | - | - | | 2.4656 | 2505 | 0.1239 | - | - | - | - | | 2.4665 | 2506 | 0.5076 | - | - | - | - | | 2.4675 | 2507 | 0.2629 | - | - | - | - | | 2.4685 | 2508 | 0.362 | - | - | - | - | | 2.4695 | 2509 | 0.1892 | - | - | - | - | | 2.4705 | 2510 | 0.2334 | - | - | - | - | | 2.4715 | 2511 | 0.1624 | - | - | - | - | | 2.4724 | 2512 | 0.2166 | - | - | - | - | | 2.4734 | 2513 | 0.2771 | - | - | - | - | | 2.4744 | 2514 | 0.4421 | - | - | - | - | | 2.4754 | 2515 | 0.4224 | - | - | - | - | | 2.4764 | 2516 | 0.5839 | - | - | - | - | | 2.4774 | 2517 | 0.2874 | - | - | - | - | | 2.4783 | 2518 | 0.3557 | - | - | - | - | | 2.4793 | 2519 | 0.3501 | - | - | - | - | | 2.4803 | 2520 | 0.2368 | - | - | - | - | | 2.4813 | 2521 | 0.5408 | - | - | - | - | | 2.4823 | 2522 | 0.2134 | - | - | - | - | | 2.4833 | 2523 | 0.9646 | - | - | - | - | | 2.4843 | 2524 | 0.7589 | - | - | - | - | | 2.4852 | 2525 | 0.2106 | - | - | - | - | | 2.4862 | 2526 | 0.2096 | - | - | - | - | | 2.4872 | 2527 | 0.4391 | - | - | - | - | | 2.4882 | 2528 | 0.2735 | - | - | - | - | | 2.4892 | 2529 | 0.4712 | - | - | - | - | | 2.4902 | 2530 | 0.2503 | - | - | - | - | | 2.4911 | 2531 | 0.4035 | - | - | - | - | | 2.4921 | 2532 | 0.4989 | - | - | - | - | | 2.4931 | 2533 | 0.4082 | - | - | - | - | | 2.4941 | 2534 | 0.297 | - | - | - | - | | 2.4951 | 2535 | 0.178 | - | - | - | - | | 2.4961 | 2536 | 0.3749 | - | - | - | - | | 2.4970 | 2537 | 0.2872 | - | - | - | - | | 2.4980 | 2538 | 0.1993 | - | - | - | - | | 2.4990 | 2539 | 0.4424 | - | - | - | - | | 2.5 | 2540 | 0.4321 | - | - | - | - | | 2.5010 | 2541 | 0.2728 | - | - | - | - | | 2.5020 | 2542 | 0.1387 | - | - | - | - | | 2.5030 | 2543 | 1.0402 | - | - | - | - | | 2.5039 | 2544 | 0.4153 | - | - | - | - | | 2.5049 | 2545 | 0.4845 | - | - | - | - | | 2.5059 | 2546 | 0.4674 | - | - | - | - | | 2.5069 | 2547 | 0.2211 | - | - | - | - | | 2.5079 | 2548 | 0.3532 | - | - | - | - | | 2.5089 | 2549 | 0.2734 | - | - | - | - | | 2.5098 | 2550 | 0.3015 | - | - | - | - | | 2.5108 | 2551 | 0.0508 | - | - | - | - | | 2.5118 | 2552 | 0.5125 | - | - | - | - | | 2.5128 | 2553 | 0.0729 | - | - | - | - | | 2.5138 | 2554 | 0.376 | - | - | - | - | | 2.5148 | 2555 | 0.2335 | - | - | - | - | | 2.5157 | 2556 | 0.2233 | - | - | - | - | | 2.5167 | 2557 | 0.257 | - | - | - | - | | 2.5177 | 2558 | 0.6108 | - | - | - | - | | 2.5187 | 2559 | 0.0648 | - | - | - | - | | 2.5197 | 2560 | 0.3249 | - | - | - | - | | 2.5207 | 2561 | 0.3661 | - | - | - | - | | 2.5217 | 2562 | 0.1489 | - | - | - | - | | 2.5226 | 2563 | 0.1006 | - | - | - | - | | 2.5236 | 2564 | 0.205 | - | - | - | - | | 2.5246 | 2565 | 0.132 | - | - | - | - | | 2.5256 | 2566 | 0.4317 | - | - | - | - | | 2.5266 | 2567 | 0.4741 | - | - | - | - | | 2.5276 | 2568 | 0.3413 | - | - | - | - | | 2.5285 | 2569 | 0.7061 | - | - | - | - | | 2.5295 | 2570 | 0.3047 | - | - | - | - | | 2.5305 | 2571 | 0.79 | - | - | - | - | | 2.5315 | 2572 | 0.4705 | - | - | - | - | | 2.5325 | 2573 | 0.0915 | - | - | - | - | | 2.5335 | 2574 | 0.4268 | - | - | - | - | | 2.5344 | 2575 | 0.3548 | - | - | - | - | | 2.5354 | 2576 | 0.2926 | - | - | - | - | | 2.5364 | 2577 | 0.4319 | - | - | - | - | | 2.5374 | 2578 | 0.293 | - | - | - | - | | 2.5384 | 2579 | 0.4523 | - | - | - | - | | 2.5394 | 2580 | 0.3576 | - | - | - | - | | 2.5404 | 2581 | 0.3131 | - | - | - | - | | 2.5413 | 2582 | 0.1289 | - | - | - | - | | 2.5423 | 2583 | 0.2224 | - | - | - | - | | 2.5433 | 2584 | 0.2187 | - | - | - | - | | 2.5443 | 2585 | 0.1808 | - | - | - | - | | 2.5453 | 2586 | 0.5719 | - | - | - | - | | 2.5463 | 2587 | 0.3357 | - | - | - | - | | 2.5472 | 2588 | 0.4923 | - | - | - | - | | 2.5482 | 2589 | 0.7231 | - | - | - | - | | 2.5492 | 2590 | 0.5006 | - | - | - | - | | 2.5502 | 2591 | 0.6329 | - | - | - | - | | 2.5512 | 2592 | 0.23 | - | - | - | - | | 2.5522 | 2593 | 0.158 | - | - | - | - | | 2.5531 | 2594 | 0.1245 | - | - | - | - | | 2.5541 | 2595 | 0.2352 | - | - | - | - | | 2.5551 | 2596 | 0.6465 | - | - | - | - | | 2.5561 | 2597 | 0.3682 | - | - | - | - | | 2.5571 | 2598 | 0.2663 | - | - | - | - | | 2.5581 | 2599 | 0.2182 | - | - | - | - | | 2.5591 | 2600 | 0.2484 | - | - | - | - | | 2.5600 | 2601 | 0.1932 | 0.4917 | 0.6688 | 0.5230 | 0.6985 | | 2.5610 | 2602 | 0.0946 | - | - | - | - | | 2.5620 | 2603 | 0.3778 | - | - | - | - | | 2.5630 | 2604 | 0.1033 | - | - | - | - | | 2.5640 | 2605 | 0.4318 | - | - | - | - | | 2.5650 | 2606 | 0.2179 | - | - | - | - | | 2.5659 | 2607 | 0.0971 | - | - | - | - | | 2.5669 | 2608 | 0.4726 | - | - | - | - | | 2.5679 | 2609 | 0.3389 | - | - | - | - | | 2.5689 | 2610 | 0.1408 | - | - | - | - | | 2.5699 | 2611 | 0.0972 | - | - | - | - | | 2.5709 | 2612 | 0.1531 | - | - | - | - | | 2.5719 | 2613 | 0.1374 | - | - | - | - | | 2.5728 | 2614 | 0.2092 | - | - | - | - | | 2.5738 | 2615 | 0.1692 | - | - | - | - | | 2.5748 | 2616 | 0.412 | - | - | - | - | | 2.5758 | 2617 | 0.0756 | - | - | - | - | | 2.5768 | 2618 | 0.8034 | - | - | - | - | | 2.5778 | 2619 | 0.8405 | - | - | - | - | | 2.5787 | 2620 | 0.2442 | - | - | - | - | | 2.5797 | 2621 | 0.3537 | - | - | - | - | | 2.5807 | 2622 | 0.4989 | - | - | - | - | | 2.5817 | 2623 | 0.4902 | - | - | - | - | | 2.5827 | 2624 | 0.8908 | - | - | - | - | | 2.5837 | 2625 | 0.1239 | - | - | - | - | | 2.5846 | 2626 | 0.4208 | - | - | - | - | | 2.5856 | 2627 | 0.3947 | - | - | - | - | | 2.5866 | 2628 | 0.4709 | - | - | - | - | | 2.5876 | 2629 | 0.452 | - | - | - | - | | 2.5886 | 2630 | 0.1296 | - | - | - | - | | 2.5896 | 2631 | 0.3835 | - | - | - | - | | 2.5906 | 2632 | 0.3944 | - | - | - | - | | 2.5915 | 2633 | 0.7798 | - | - | - | - | | 2.5925 | 2634 | 0.381 | - | - | - | - | | 2.5935 | 2635 | 0.5957 | - | - | - | - | | 2.5945 | 2636 | 0.0761 | - | - | - | - | | 2.5955 | 2637 | 0.1285 | - | - | - | - | | 2.5965 | 2638 | 0.395 | - | - | - | - | | 2.5974 | 2639 | 0.8514 | - | - | - | - | | 2.5984 | 2640 | 0.2844 | - | - | - | - | | 2.5994 | 2641 | 0.236 | - | - | - | - | | 2.6004 | 2642 | 0.3958 | - | - | - | - | | 2.6014 | 2643 | 0.4496 | - | - | - | - | | 2.6024 | 2644 | 0.6127 | - | - | - | - | | 2.6033 | 2645 | 0.2044 | - | - | - | - | | 2.6043 | 2646 | 0.1861 | - | - | - | - | | 2.6053 | 2647 | 0.1584 | - | - | - | - | | 2.6063 | 2648 | 0.3345 | - | - | - | - | | 2.6073 | 2649 | 0.2336 | - | - | - | - | | 2.6083 | 2650 | 0.2932 | - | - | - | - | | 2.6093 | 2651 | 0.2814 | - | - | - | - | | 2.6102 | 2652 | 0.4036 | - | - | - | - | | 2.6112 | 2653 | 0.3042 | - | - | - | - | | 2.6122 | 2654 | 0.42 | - | - | - | - | | 2.6132 | 2655 | 0.2876 | - | - | - | - | | 2.6142 | 2656 | 0.3322 | - | - | - | - | | 2.6152 | 2657 | 0.3078 | - | - | - | - | | 2.6161 | 2658 | 0.3052 | - | - | - | - | | 2.6171 | 2659 | 0.6088 | - | - | - | - | | 2.6181 | 2660 | 0.2831 | - | - | - | - | | 2.6191 | 2661 | 0.5751 | - | - | - | - | | 2.6201 | 2662 | 0.0988 | - | - | - | - | | 2.6211 | 2663 | 0.1851 | - | - | - | - | | 2.6220 | 2664 | 0.3453 | - | - | - | - | | 2.6230 | 2665 | 0.441 | - | - | - | - | | 2.6240 | 2666 | 0.0953 | - | - | - | - | | 2.625 | 2667 | 0.1422 | - | - | - | - | | 2.6260 | 2668 | 0.1243 | - | - | - | - | | 2.6270 | 2669 | 0.32 | - | - | - | - | | 2.6280 | 2670 | 0.2588 | - | - | - | - | | 2.6289 | 2671 | 0.4652 | - | - | - | - | | 2.6299 | 2672 | 0.4017 | - | - | - | - | | 2.6309 | 2673 | 0.1883 | - | - | - | - | | 2.6319 | 2674 | 0.3345 | - | - | - | - | | 2.6329 | 2675 | 0.162 | - | - | - | - | | 2.6339 | 2676 | 0.3113 | - | - | - | - | | 2.6348 | 2677 | 0.6358 | - | - | - | - | | 2.6358 | 2678 | 0.397 | - | - | - | - | | 2.6368 | 2679 | 0.454 | - | - | - | - | | 2.6378 | 2680 | 0.1772 | - | - | - | - | | 2.6388 | 2681 | 0.0152 | - | - | - | - | | 2.6398 | 2682 | 0.142 | - | - | - | - | | 2.6407 | 2683 | 0.4372 | - | - | - | - | | 2.6417 | 2684 | 0.4235 | - | - | - | - | | 2.6427 | 2685 | 0.1866 | - | - | - | - | | 2.6437 | 2686 | 0.0524 | - | - | - | - | | 2.6447 | 2687 | 0.1163 | - | - | - | - | | 2.6457 | 2688 | 0.1485 | - | - | - | - | | 2.6467 | 2689 | 0.1149 | - | - | - | - | | 2.6476 | 2690 | 0.3884 | - | - | - | - | | 2.6486 | 2691 | 0.172 | - | - | - | - | | 2.6496 | 2692 | 0.4707 | - | - | - | - | | 2.6506 | 2693 | 0.3776 | - | - | - | - | | 2.6516 | 2694 | 0.309 | - | - | - | - | | 2.6526 | 2695 | 0.7073 | - | - | - | - | | 2.6535 | 2696 | 0.0827 | - | - | - | - | | 2.6545 | 2697 | 0.3375 | - | - | - | - | | 2.6555 | 2698 | 0.2815 | - | - | - | - | | 2.6565 | 2699 | 0.41 | - | - | - | - | | 2.6575 | 2700 | 0.1364 | - | - | - | - | | 2.6585 | 2701 | 0.4235 | - | - | - | - | | 2.6594 | 2702 | 0.4157 | - | - | - | - | | 2.6604 | 2703 | 1.088 | - | - | - | - | | 2.6614 | 2704 | 0.2303 | - | - | - | - | | 2.6624 | 2705 | 0.2966 | - | - | - | - | | 2.6634 | 2706 | 0.4843 | - | - | - | - | | 2.6644 | 2707 | 0.2855 | - | - | - | - | | 2.6654 | 2708 | 0.2591 | - | - | - | - | | 2.6663 | 2709 | 0.467 | - | - | - | - | | 2.6673 | 2710 | 0.139 | - | - | - | - | | 2.6683 | 2711 | 0.3564 | - | - | - | - | | 2.6693 | 2712 | 0.141 | - | - | - | - | | 2.6703 | 2713 | 0.1698 | - | - | - | - | | 2.6713 | 2714 | 0.3223 | - | - | - | - | | 2.6722 | 2715 | 0.4376 | - | - | - | - | | 2.6732 | 2716 | 0.1578 | - | - | - | - | | 2.6742 | 2717 | 0.2388 | - | - | - | - | | 2.6752 | 2718 | 0.211 | - | - | - | - | | 2.6762 | 2719 | 0.2561 | - | - | - | - | | 2.6772 | 2720 | 0.0494 | - | - | - | - | | 2.6781 | 2721 | 0.589 | - | - | - | - | | 2.6791 | 2722 | 0.5799 | - | - | - | - | | 2.6801 | 2723 | 0.2218 | - | - | - | - | | 2.6811 | 2724 | 0.3222 | - | - | - | - | | 2.6821 | 2725 | 0.7828 | - | - | - | - | | 2.6831 | 2726 | 0.3504 | - | - | - | - | | 2.6841 | 2727 | 0.333 | - | - | - | - | | 2.6850 | 2728 | 0.6705 | - | - | - | - | | 2.6860 | 2729 | 0.2021 | - | - | - | - | | 2.6870 | 2730 | 0.7059 | - | - | - | - | | 2.6880 | 2731 | 0.0523 | - | - | - | - | | 2.6890 | 2732 | 0.3013 | - | - | - | - | | 2.6900 | 2733 | 0.249 | - | - | - | - | | 2.6909 | 2734 | 0.4251 | - | - | - | - | | 2.6919 | 2735 | 1.0586 | - | - | - | - | | 2.6929 | 2736 | 0.4656 | - | - | - | - | | 2.6939 | 2737 | 0.1227 | - | - | - | - | | 2.6949 | 2738 | 0.1047 | - | - | - | - | | 2.6959 | 2739 | 0.4664 | - | - | - | - | | 2.6969 | 2740 | 0.4104 | - | - | - | - | | 2.6978 | 2741 | 0.4076 | - | - | - | - | | 2.6988 | 2742 | 0.2715 | - | - | - | - | | 2.6998 | 2743 | 0.167 | - | - | - | - | | 2.7008 | 2744 | 0.2799 | - | - | - | - | | 2.7018 | 2745 | 0.1801 | - | - | - | - | | 2.7028 | 2746 | 0.2727 | - | - | - | - | | 2.7037 | 2747 | 0.1934 | - | - | - | - | | 2.7047 | 2748 | 0.4175 | - | - | - | - | | 2.7057 | 2749 | 0.5095 | - | - | - | - | | 2.7067 | 2750 | 0.4747 | - | - | - | - | | 2.7077 | 2751 | 0.2593 | - | - | - | - | | 2.7087 | 2752 | 0.508 | - | - | - | - | | 2.7096 | 2753 | 0.1706 | - | - | - | - | | 2.7106 | 2754 | 0.372 | 0.4886 | 0.6735 | 0.5240 | 0.6999 | | 2.7116 | 2755 | 0.1012 | - | - | - | - | | 2.7126 | 2756 | 0.1855 | - | - | - | - | | 2.7136 | 2757 | 0.1423 | - | - | - | - | | 2.7146 | 2758 | 0.2128 | - | - | - | - | | 2.7156 | 2759 | 0.1641 | - | - | - | - | | 2.7165 | 2760 | 0.2113 | - | - | - | - | | 2.7175 | 2761 | 0.5309 | - | - | - | - | | 2.7185 | 2762 | 0.1855 | - | - | - | - | | 2.7195 | 2763 | 0.353 | - | - | - | - | | 2.7205 | 2764 | 0.3805 | - | - | - | - | | 2.7215 | 2765 | 0.4292 | - | - | - | - | | 2.7224 | 2766 | 0.2547 | - | - | - | - | | 2.7234 | 2767 | 0.3077 | - | - | - | - | | 2.7244 | 2768 | 0.6004 | - | - | - | - | | 2.7254 | 2769 | 0.116 | - | - | - | - | | 2.7264 | 2770 | 0.1424 | - | - | - | - | | 2.7274 | 2771 | 0.2555 | - | - | - | - | | 2.7283 | 2772 | 0.3408 | - | - | - | - | | 2.7293 | 2773 | 0.117 | - | - | - | - | | 2.7303 | 2774 | 0.1352 | - | - | - | - | | 2.7313 | 2775 | 0.1671 | - | - | - | - | | 2.7323 | 2776 | 0.2096 | - | - | - | - | | 2.7333 | 2777 | 0.1569 | - | - | - | - | | 2.7343 | 2778 | 1.3244 | - | - | - | - | | 2.7352 | 2779 | 0.3514 | - | - | - | - | | 2.7362 | 2780 | 0.607 | - | - | - | - | | 2.7372 | 2781 | 0.2289 | - | - | - | - | | 2.7382 | 2782 | 0.2472 | - | - | - | - | | 2.7392 | 2783 | 0.9307 | - | - | - | - | | 2.7402 | 2784 | 0.336 | - | - | - | - | | 2.7411 | 2785 | 0.5573 | - | - | - | - | | 2.7421 | 2786 | 0.2472 | - | - | - | - | | 2.7431 | 2787 | 0.2082 | - | - | - | - | | 2.7441 | 2788 | 0.2614 | - | - | - | - | | 2.7451 | 2789 | 0.6271 | - | - | - | - | | 2.7461 | 2790 | 0.2748 | - | - | - | - | | 2.7470 | 2791 | 0.3488 | - | - | - | - | | 2.7480 | 2792 | 0.052 | - | - | - | - | | 2.7490 | 2793 | 0.3308 | - | - | - | - | | 2.75 | 2794 | 0.2661 | - | - | - | - | | 2.7510 | 2795 | 0.2692 | - | - | - | - | | 2.7520 | 2796 | 0.1316 | - | - | - | - | | 2.7530 | 2797 | 0.3616 | - | - | - | - | | 2.7539 | 2798 | 0.1442 | - | - | - | - | | 2.7549 | 2799 | 0.3065 | - | - | - | - | | 2.7559 | 2800 | 0.5695 | - | - | - | - | | 2.7569 | 2801 | 0.0946 | - | - | - | - | | 2.7579 | 2802 | 0.2218 | - | - | - | - | | 2.7589 | 2803 | 0.3658 | - | - | - | - | | 2.7598 | 2804 | 0.2364 | - | - | - | - | | 2.7608 | 2805 | 0.2508 | - | - | - | - | | 2.7618 | 2806 | 0.3074 | - | - | - | - | | 2.7628 | 2807 | 0.1118 | - | - | - | - | | 2.7638 | 2808 | 0.4156 | - | - | - | - | | 2.7648 | 2809 | 0.1576 | - | - | - | - | | 2.7657 | 2810 | 0.3728 | - | - | - | - | | 2.7667 | 2811 | 0.2044 | - | - | - | - | | 2.7677 | 2812 | 0.3115 | - | - | - | - | | 2.7687 | 2813 | 0.1254 | - | - | - | - | | 2.7697 | 2814 | 0.3651 | - | - | - | - | | 2.7707 | 2815 | 0.2305 | - | - | - | - | | 2.7717 | 2816 | 0.1259 | - | - | - | - | | 2.7726 | 2817 | 0.3865 | - | - | - | - | | 2.7736 | 2818 | 0.5593 | - | - | - | - | | 2.7746 | 2819 | 0.216 | - | - | - | - | | 2.7756 | 2820 | 0.2696 | - | - | - | - | | 2.7766 | 2821 | 0.3779 | - | - | - | - | | 2.7776 | 2822 | 0.2451 | - | - | - | - | | 2.7785 | 2823 | 0.4448 | - | - | - | - | | 2.7795 | 2824 | 0.045 | - | - | - | - | | 2.7805 | 2825 | 0.3465 | - | - | - | - | | 2.7815 | 2826 | 0.1853 | - | - | - | - | | 2.7825 | 2827 | 0.1103 | - | - | - | - | | 2.7835 | 2828 | 0.277 | - | - | - | - | | 2.7844 | 2829 | 0.1521 | - | - | - | - | | 2.7854 | 2830 | 0.2653 | - | - | - | - | | 2.7864 | 2831 | 0.4891 | - | - | - | - | | 2.7874 | 2832 | 0.4052 | - | - | - | - | | 2.7884 | 2833 | 0.4734 | - | - | - | - | | 2.7894 | 2834 | 0.3711 | - | - | - | - | | 2.7904 | 2835 | 0.3721 | - | - | - | - | | 2.7913 | 2836 | 0.2153 | - | - | - | - | | 2.7923 | 2837 | 0.3035 | - | - | - | - | | 2.7933 | 2838 | 0.413 | - | - | - | - | | 2.7943 | 2839 | 0.3275 | - | - | - | - | | 2.7953 | 2840 | 0.45 | - | - | - | - | | 2.7963 | 2841 | 0.8403 | - | - | - | - | | 2.7972 | 2842 | 0.2697 | - | - | - | - | | 2.7982 | 2843 | 0.1558 | - | - | - | - | | 2.7992 | 2844 | 0.2919 | - | - | - | - | | 2.8002 | 2845 | 0.2728 | - | - | - | - | | 2.8012 | 2846 | 0.6732 | - | - | - | - | | 2.8022 | 2847 | 0.1906 | - | - | - | - | | 2.8031 | 2848 | 0.0684 | - | - | - | - | | 2.8041 | 2849 | 0.1759 | - | - | - | - | | 2.8051 | 2850 | 0.4616 | - | - | - | - | | 2.8061 | 2851 | 0.1753 | - | - | - | - | | 2.8071 | 2852 | 0.0538 | - | - | - | - | | 2.8081 | 2853 | 0.2727 | - | - | - | - | | 2.8091 | 2854 | 0.6287 | - | - | - | - | | 2.8100 | 2855 | 0.2557 | - | - | - | - | | 2.8110 | 2856 | 0.2785 | - | - | - | - | | 2.8120 | 2857 | 0.1492 | - | - | - | - | | 2.8130 | 2858 | 0.141 | - | - | - | - | | 2.8140 | 2859 | 0.2445 | - | - | - | - | | 2.8150 | 2860 | 0.1115 | - | - | - | - | | 2.8159 | 2861 | 0.3406 | - | - | - | - | | 2.8169 | 2862 | 0.5149 | - | - | - | - | | 2.8179 | 2863 | 0.2799 | - | - | - | - | | 2.8189 | 2864 | 0.3185 | - | - | - | - | | 2.8199 | 2865 | 0.1001 | - | - | - | - | | 2.8209 | 2866 | 0.0394 | - | - | - | - | | 2.8219 | 2867 | 0.1332 | - | - | - | - | | 2.8228 | 2868 | 0.4512 | - | - | - | - | | 2.8238 | 2869 | 0.6693 | - | - | - | - | | 2.8248 | 2870 | 0.239 | - | - | - | - | | 2.8258 | 2871 | 0.2037 | - | - | - | - | | 2.8268 | 2872 | 0.304 | - | - | - | - | | 2.8278 | 2873 | 0.2295 | - | - | - | - | | 2.8287 | 2874 | 0.5068 | - | - | - | - | | 2.8297 | 2875 | 0.4523 | - | - | - | - | | 2.8307 | 2876 | 0.2962 | - | - | - | - | | 2.8317 | 2877 | 0.5274 | - | - | - | - | | 2.8327 | 2878 | 0.6032 | - | - | - | - | | 2.8337 | 2879 | 0.5692 | - | - | - | - | | 2.8346 | 2880 | 0.1158 | - | - | - | - | | 2.8356 | 2881 | 0.1685 | - | - | - | - | | 2.8366 | 2882 | 0.4206 | - | - | - | - | | 2.8376 | 2883 | 0.198 | - | - | - | - | | 2.8386 | 2884 | 0.3901 | - | - | - | - | | 2.8396 | 2885 | 0.2684 | - | - | - | - | | 2.8406 | 2886 | 0.1488 | - | - | - | - | | 2.8415 | 2887 | 0.0959 | - | - | - | - | | 2.8425 | 2888 | 0.5298 | - | - | - | - | | 2.8435 | 2889 | 0.2391 | - | - | - | - | | 2.8445 | 2890 | 0.239 | - | - | - | - | | 2.8455 | 2891 | 0.1347 | - | - | - | - | | 2.8465 | 2892 | 0.5638 | - | - | - | - | | 2.8474 | 2893 | 0.7352 | - | - | - | - | | 2.8484 | 2894 | 0.2605 | - | - | - | - | | 2.8494 | 2895 | 0.549 | - | - | - | - | | 2.8504 | 2896 | 0.4349 | - | - | - | - | | 2.8514 | 2897 | 0.2525 | - | - | - | - | | 2.8524 | 2898 | 0.1922 | - | - | - | - | | 2.8533 | 2899 | 0.5798 | - | - | - | - | | 2.8543 | 2900 | 0.3186 | - | - | - | - | | 2.8553 | 2901 | 0.2008 | - | - | - | - | | 2.8563 | 2902 | 1.1413 | - | - | - | - | | 2.8573 | 2903 | 0.7863 | - | - | - | - | | 2.8583 | 2904 | 0.1799 | - | - | - | - | | 2.8593 | 2905 | 0.3595 | - | - | - | - | | 2.8602 | 2906 | 0.3704 | - | - | - | - | | 2.8612 | 2907 | 0.7592 | 0.4888 | 0.6740 | 0.5247 | 0.7022 | | 2.8622 | 2908 | 0.3438 | - | - | - | - | | 2.8632 | 2909 | 0.3004 | - | - | - | - | | 2.8642 | 2910 | 0.0605 | - | - | - | - | | 2.8652 | 2911 | 0.2806 | - | - | - | - | | 2.8661 | 2912 | 0.5737 | - | - | - | - | | 2.8671 | 2913 | 0.3122 | - | - | - | - | | 2.8681 | 2914 | 0.6209 | - | - | - | - | | 2.8691 | 2915 | 0.3461 | - | - | - | - | | 2.8701 | 2916 | 0.2759 | - | - | - | - | | 2.8711 | 2917 | 0.2877 | - | - | - | - | | 2.8720 | 2918 | 1.5252 | - | - | - | - | | 2.8730 | 2919 | 0.3598 | - | - | - | - | | 2.8740 | 2920 | 0.2988 | - | - | - | - | | 2.875 | 2921 | 0.1411 | - | - | - | - | | 2.8760 | 2922 | 0.2136 | - | - | - | - | | 2.8770 | 2923 | 0.2058 | - | - | - | - | | 2.8780 | 2924 | 0.4305 | - | - | - | - | | 2.8789 | 2925 | 0.5253 | - | - | - | - | | 2.8799 | 2926 | 0.3112 | - | - | - | - | | 2.8809 | 2927 | 0.6982 | - | - | - | - | | 2.8819 | 2928 | 0.3565 | - | - | - | - | | 2.8829 | 2929 | 0.2734 | - | - | - | - | | 2.8839 | 2930 | 0.1425 | - | - | - | - | | 2.8848 | 2931 | 0.7445 | - | - | - | - | | 2.8858 | 2932 | 0.4615 | - | - | - | - | | 2.8868 | 2933 | 0.1666 | - | - | - | - | | 2.8878 | 2934 | 0.5224 | - | - | - | - | | 2.8888 | 2935 | 0.0262 | - | - | - | - | | 2.8898 | 2936 | 0.6386 | - | - | - | - | | 2.8907 | 2937 | 0.2209 | - | - | - | - | | 2.8917 | 2938 | 0.2289 | - | - | - | - | | 2.8927 | 2939 | 0.4258 | - | - | - | - | | 2.8937 | 2940 | 0.4327 | - | - | - | - | | 2.8947 | 2941 | 0.6541 | - | - | - | - | | 2.8957 | 2942 | 0.2661 | - | - | - | - | | 2.8967 | 2943 | 0.4912 | - | - | - | - | | 2.8976 | 2944 | 0.1441 | - | - | - | - | | 2.8986 | 2945 | 0.2309 | - | - | - | - | | 2.8996 | 2946 | 0.3028 | - | - | - | - | | 2.9006 | 2947 | 0.1203 | - | - | - | - | | 2.9016 | 2948 | 0.6289 | - | - | - | - | | 2.9026 | 2949 | 0.3618 | - | - | - | - | | 2.9035 | 2950 | 0.2684 | - | - | - | - | | 2.9045 | 2951 | 0.1371 | - | - | - | - | | 2.9055 | 2952 | 0.6694 | - | - | - | - | | 2.9065 | 2953 | 0.2216 | - | - | - | - | | 2.9075 | 2954 | 0.1103 | - | - | - | - | | 2.9085 | 2955 | 0.2106 | - | - | - | - | | 2.9094 | 2956 | 0.4114 | - | - | - | - | | 2.9104 | 2957 | 0.166 | - | - | - | - | | 2.9114 | 2958 | 0.0788 | - | - | - | - | | 2.9124 | 2959 | 0.2894 | - | - | - | - | | 2.9134 | 2960 | 0.2845 | - | - | - | - | | 2.9144 | 2961 | 0.2357 | - | - | - | - | | 2.9154 | 2962 | 0.3342 | - | - | - | - | | 2.9163 | 2963 | 0.3945 | - | - | - | - | | 2.9173 | 2964 | 0.2308 | - | - | - | - | | 2.9183 | 2965 | 0.4013 | - | - | - | - | | 2.9193 | 2966 | 0.3327 | - | - | - | - | | 2.9203 | 2967 | 0.4024 | - | - | - | - | | 2.9213 | 2968 | 0.1838 | - | - | - | - | | 2.9222 | 2969 | 0.3868 | - | - | - | - | | 2.9232 | 2970 | 0.4597 | - | - | - | - | | 2.9242 | 2971 | 0.2572 | - | - | - | - | | 2.9252 | 2972 | 0.4641 | - | - | - | - | | 2.9262 | 2973 | 0.0732 | - | - | - | - | | 2.9272 | 2974 | 0.9887 | - | - | - | - | | 2.9281 | 2975 | 0.2109 | - | - | - | - | | 2.9291 | 2976 | 0.1698 | - | - | - | - | | 2.9301 | 2977 | 0.4012 | - | - | - | - | | 2.9311 | 2978 | 0.1757 | - | - | - | - | | 2.9321 | 2979 | 0.3168 | - | - | - | - | | 2.9331 | 2980 | 0.1128 | - | - | - | - | | 2.9341 | 2981 | 0.1795 | - | - | - | - | | 2.9350 | 2982 | 0.3252 | - | - | - | - | | 2.9360 | 2983 | 0.037 | - | - | - | - | | 2.9370 | 2984 | 0.3334 | - | - | - | - | | 2.9380 | 2985 | 0.3173 | - | - | - | - | | 2.9390 | 2986 | 0.151 | - | - | - | - | | 2.9400 | 2987 | 0.3881 | - | - | - | - | | 2.9409 | 2988 | 0.1861 | - | - | - | - | | 2.9419 | 2989 | 0.2437 | - | - | - | - | | 2.9429 | 2990 | 0.4226 | - | - | - | - | | 2.9439 | 2991 | 0.5198 | - | - | - | - | | 2.9449 | 2992 | 0.3833 | - | - | - | - | | 2.9459 | 2993 | 0.253 | - | - | - | - | | 2.9469 | 2994 | 0.3421 | - | - | - | - | | 2.9478 | 2995 | 0.05 | - | - | - | - | | 2.9488 | 2996 | 0.7686 | - | - | - | - | | 2.9498 | 2997 | 0.1071 | - | - | - | - | | 2.9508 | 2998 | 0.3382 | - | - | - | - | | 2.9518 | 2999 | 0.2211 | - | - | - | - | | 2.9528 | 3000 | 0.389 | - | - | - | - | | 2.9537 | 3001 | 0.1802 | - | - | - | - | | 2.9547 | 3002 | 0.295 | - | - | - | - | | 2.9557 | 3003 | 0.2534 | - | - | - | - | | 2.9567 | 3004 | 0.8536 | - | - | - | - | | 2.9577 | 3005 | 0.5325 | - | - | - | - | | 2.9587 | 3006 | 0.376 | - | - | - | - | | 2.9596 | 3007 | 0.1309 | - | - | - | - | | 2.9606 | 3008 | 0.3147 | - | - | - | - | | 2.9616 | 3009 | 0.1782 | - | - | - | - | | 2.9626 | 3010 | 0.4162 | - | - | - | - | | 2.9636 | 3011 | 0.3284 | - | - | - | - | | 2.9646 | 3012 | 0.1792 | - | - | - | - | | 2.9656 | 3013 | 0.1753 | - | - | - | - | | 2.9665 | 3014 | 0.5557 | - | - | - | - | | 2.9675 | 3015 | 0.183 | - | - | - | - | | 2.9685 | 3016 | 0.1412 | - | - | - | - | | 2.9695 | 3017 | 0.4037 | - | - | - | - | | 2.9705 | 3018 | 0.6259 | - | - | - | - | | 2.9715 | 3019 | 0.2387 | - | - | - | - | | 2.9724 | 3020 | 0.458 | - | - | - | - | | 2.9734 | 3021 | 0.2202 | - | - | - | - | | 2.9744 | 3022 | 0.1132 | - | - | - | - | | 2.9754 | 3023 | 0.1922 | - | - | - | - | | 2.9764 | 3024 | 0.3622 | - | - | - | - | | 2.9774 | 3025 | 0.3681 | - | - | - | - | | 2.9783 | 3026 | 0.1704 | - | - | - | - | | 2.9793 | 3027 | 0.2572 | - | - | - | - | | 2.9803 | 3028 | 0.2254 | - | - | - | - | | 2.9813 | 3029 | 0.5572 | - | - | - | - | | 2.9823 | 3030 | 0.691 | - | - | - | - | | 2.9833 | 3031 | 0.3 | - | - | - | - | | 2.9843 | 3032 | 0.3137 | - | - | - | - | | 2.9852 | 3033 | 0.4111 | - | - | - | - | | 2.9862 | 3034 | 0.4421 | - | - | - | - | | 2.9872 | 3035 | 0.1184 | - | - | - | - | | 2.9882 | 3036 | 0.2347 | - | - | - | - | | 2.9892 | 3037 | 0.4659 | - | - | - | - | | 2.9902 | 3038 | 0.391 | - | - | - | - | | 2.9911 | 3039 | 0.3805 | - | - | - | - | | 2.9921 | 3040 | 0.1296 | - | - | - | - | | 2.9931 | 3041 | 0.055 | - | - | - | - | | 2.9941 | 3042 | 0.3864 | - | - | - | - | | 2.9951 | 3043 | 0.2506 | - | - | - | - | | 2.9961 | 3044 | 0.1876 | - | - | - | - | | 2.9970 | 3045 | 0.3416 | - | - | - | - | | 2.9980 | 3046 | 0.5668 | - | - | - | - | | 2.9990 | 3047 | 0.0809 | - | - | - | - | | 3.0 | 3048 | 0.0768 | - | - | - | - | </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.1 - Transformers: 4.44.2 - PyTorch: 2.5.0+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
argmaxinc/mlx-stable-diffusion-3.5-large
argmaxinc
2024-10-28T13:58:36Z
310
5
diffusionkit
[ "diffusionkit", "text-to-image", "image-generation", "mlx", "en", "base_model:stabilityai/stable-diffusion-3.5-large", "base_model:finetune:stabilityai/stable-diffusion-3.5-large", "license:other", "region:us" ]
text-to-image
2024-10-22T22:43:33Z
--- license: other license_name: stabilityai-ai-community license_link: >- https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md library_name: diffusionkit base_model: stabilityai/stable-diffusion-3.5-large tags: - text-to-image - image-generation - mlx inference: false language: - en --- # Stable Diffusion 3.5 Large on DiffusionKit MLX! ## Check out the [original model](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)! ## Check out the [DiffusionKit](https://github.com/argmaxinc/DiffusionKit) github repository! ![SD3 Example Output](./sd3_on_mac.png) # Usage - ## Create conda environment ```shell conda create -n diffusionkit python=3.11 -y conda activate diffusionkit pip install diffusionkit ``` - ## Run the cli command ```shell diffusionkit-cli --prompt "detailed cinematic dof render of a \ detailed MacBook Pro on a wooden desk in a dim room with items \ around, messy dirty room. On the screen are the letters 'SD3 on \ DiffusionKit' glowing softly. High detail hard surface render" \ --model-version argmaxinc/mlx-stable-diffusion-3.5-large \ --height 768 \ --width 1360 \ --seed 1001 \ --step 50 \ --cfg 7 \ --t5 \ --output ~/Desktop/sd3_on_mac.png ```
QuantFactory/Lexora-Medium-7B-GGUF
QuantFactory
2024-10-28T13:55:09Z
40
2
transformers
[ "transformers", "gguf", "it", "en", "dataset:DeepMount00/Sonnet-3.5-ITA-INSTRUCTION", "dataset:DeepMount00/Sonnet-3.5-ITA-DPO", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-22T07:35:16Z
--- library_name: transformers license: apache-2.0 language: - it - en datasets: - DeepMount00/Sonnet-3.5-ITA-INSTRUCTION - DeepMount00/Sonnet-3.5-ITA-DPO --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Lexora-Medium-7B-GGUF This is quantized version of [DeepMount00/Lexora-Medium-7B](https://huggingface.co/DeepMount00/Lexora-Medium-7B) created using llama.cpp # Original Model Card ## How to Use ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "DeepMount00/Lexora-Medium-7B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", ) prompt = [{'role': 'user', 'content': """Marco ha comprato 5 scatole di cioccolatini. Ogni scatola contiene 12 cioccolatini. Ha deciso di dare 3 cioccolatini a ciascuno dei suoi 7 amici. Quanti cioccolatini gli rimarranno dopo averli distribuiti ai suoi amici?"""}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.001, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ```
g-assismoraes/deberta-semeval25_noHINDI08_fold5
g-assismoraes
2024-10-28T13:53:17Z
163
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:50:41Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_noHINDI08_fold5 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. --> # deberta-semeval25_noHINDI08_fold5 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.9394 - Precision Samples: 0.1426 - Recall Samples: 0.5408 - F1 Samples: 0.2127 - Precision Macro: 0.8006 - Recall Macro: 0.4541 - F1 Macro: 0.3278 - Precision Micro: 0.135 - Recall Micro: 0.4639 - F1 Micro: 0.2091 - Precision Weighted: 0.5280 - Recall Weighted: 0.4639 - F1 Weighted: 0.1421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.7633 | 1.0 | 16 | 10.4422 | 1.0 | 0.0 | 0.0 | 1.0 | 0.2955 | 0.2955 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 10.108 | 2.0 | 32 | 10.0554 | 0.1462 | 0.2671 | 0.1723 | 0.9488 | 0.3333 | 0.3052 | 0.1437 | 0.1753 | 0.1579 | 0.8129 | 0.1753 | 0.0500 | | 9.67 | 3.0 | 48 | 9.7996 | 0.1365 | 0.3390 | 0.1812 | 0.9175 | 0.3622 | 0.3101 | 0.1292 | 0.2405 | 0.1681 | 0.7335 | 0.2405 | 0.0618 | | 8.9757 | 4.0 | 64 | 9.5642 | 0.1429 | 0.3982 | 0.1979 | 0.9015 | 0.3831 | 0.3218 | 0.1373 | 0.3024 | 0.1888 | 0.6915 | 0.3024 | 0.0973 | | 8.5173 | 5.0 | 80 | 9.3488 | 0.1481 | 0.5026 | 0.2156 | 0.8674 | 0.4206 | 0.3336 | 0.1415 | 0.4021 | 0.2093 | 0.6148 | 0.4021 | 0.1278 | | 8.9753 | 6.0 | 96 | 9.2255 | 0.1560 | 0.5130 | 0.2250 | 0.8570 | 0.4270 | 0.3218 | 0.1393 | 0.4192 | 0.2091 | 0.6228 | 0.4192 | 0.1332 | | 8.8356 | 7.0 | 112 | 9.1139 | 0.1440 | 0.5256 | 0.2119 | 0.8396 | 0.4364 | 0.3201 | 0.1318 | 0.4399 | 0.2029 | 0.5984 | 0.4399 | 0.1272 | | 8.396 | 8.0 | 128 | 9.0036 | 0.1457 | 0.5266 | 0.2145 | 0.8355 | 0.4411 | 0.3286 | 0.1367 | 0.4433 | 0.2089 | 0.5893 | 0.4433 | 0.1447 | | 8.7026 | 9.0 | 144 | 8.9562 | 0.1392 | 0.5273 | 0.2085 | 0.8116 | 0.4383 | 0.3260 | 0.1340 | 0.4433 | 0.2057 | 0.5369 | 0.4433 | 0.1382 | | 7.999 | 10.0 | 160 | 8.9394 | 0.1426 | 0.5408 | 0.2127 | 0.8006 | 0.4541 | 0.3278 | 0.135 | 0.4639 | 0.2091 | 0.5280 | 0.4639 | 0.1421 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
mradermacher/Llama-3.2-3B-Booval-GGUF
mradermacher
2024-10-28T13:50:58Z
16
1
transformers
[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bunnycore/Llama-3.2-3B-Booval", "base_model:quantized:bunnycore/Llama-3.2-3B-Booval", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T12:40:28Z
--- base_model: bunnycore/Llama-3.2-3B-Booval language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/bunnycore/Llama-3.2-3B-Booval <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q2_K.gguf) | Q2_K | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q3_K_S.gguf) | Q3_K_S | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q3_K_L.gguf) | Q3_K_L | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.IQ4_XS.gguf) | IQ4_XS | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q5_K_S.gguf) | Q5_K_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q5_K_M.gguf) | Q5_K_M | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q6_K.gguf) | Q6_K | 2.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Booval-GGUF/resolve/main/Llama-3.2-3B-Booval.f16.gguf) | f16 | 6.5 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
g-assismoraes/deberta-semeval25_noHINDI08_fold4
g-assismoraes
2024-10-28T13:50:38Z
164
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:48:07Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_noHINDI08_fold4 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. --> # deberta-semeval25_noHINDI08_fold4 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.5869 - Precision Samples: 0.1430 - Recall Samples: 0.5534 - F1 Samples: 0.2128 - Precision Macro: 0.8182 - Recall Macro: 0.4411 - F1 Macro: 0.3037 - Precision Micro: 0.1286 - Recall Micro: 0.4712 - F1 Micro: 0.2020 - Precision Weighted: 0.5660 - Recall Weighted: 0.4712 - F1 Weighted: 0.1261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.4207 | 1.0 | 16 | 9.9474 | 1.0 | 0.0 | 0.0 | 1.0 | 0.2614 | 0.2614 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 9.6412 | 2.0 | 32 | 9.5638 | 0.1680 | 0.2762 | 0.1844 | 0.9617 | 0.2942 | 0.2727 | 0.1679 | 0.1619 | 0.1648 | 0.8498 | 0.1619 | 0.0538 | | 9.9968 | 3.0 | 48 | 9.3139 | 0.1322 | 0.3717 | 0.1714 | 0.9190 | 0.3430 | 0.2787 | 0.1119 | 0.2770 | 0.1594 | 0.7311 | 0.2770 | 0.0693 | | 9.1935 | 4.0 | 64 | 9.1086 | 0.1442 | 0.4312 | 0.1947 | 0.8902 | 0.3702 | 0.2870 | 0.1239 | 0.3417 | 0.1818 | 0.6873 | 0.3417 | 0.0905 | | 9.5271 | 5.0 | 80 | 8.9205 | 0.1320 | 0.4986 | 0.1948 | 0.8620 | 0.4075 | 0.2959 | 0.1181 | 0.4137 | 0.1837 | 0.6305 | 0.4137 | 0.1089 | | 9.3829 | 6.0 | 96 | 8.7813 | 0.1432 | 0.5248 | 0.2112 | 0.8533 | 0.4214 | 0.3014 | 0.1294 | 0.4353 | 0.1995 | 0.6207 | 0.4353 | 0.1185 | | 8.9373 | 7.0 | 112 | 8.7473 | 0.1457 | 0.5331 | 0.2154 | 0.8426 | 0.4339 | 0.3034 | 0.1334 | 0.4568 | 0.2065 | 0.5971 | 0.4568 | 0.1226 | | 8.0103 | 8.0 | 128 | 8.6381 | 0.1420 | 0.5452 | 0.2113 | 0.8409 | 0.4397 | 0.3011 | 0.1267 | 0.4676 | 0.1994 | 0.5946 | 0.4676 | 0.1193 | | 8.1355 | 9.0 | 144 | 8.5970 | 0.1420 | 0.5452 | 0.2112 | 0.8296 | 0.4397 | 0.3011 | 0.1268 | 0.4676 | 0.1995 | 0.5772 | 0.4676 | 0.1200 | | 8.3947 | 10.0 | 160 | 8.5869 | 0.1430 | 0.5534 | 0.2128 | 0.8182 | 0.4411 | 0.3037 | 0.1286 | 0.4712 | 0.2020 | 0.5660 | 0.4712 | 0.1261 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
dominguesm/canarim-7b
dominguesm
2024-10-28T13:48:51Z
324
16
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "LLM", "Portuguese", "Llama 2", "pt", "dataset:dominguesm/CC-MAIN-2023-23", "arxiv:2307.09288", "doi:10.57967/hf/1356", "license:llama2", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-11-16T17:33:46Z
--- language: - pt license: llama2 library_name: transformers tags: - text-generation - pytorch - LLM - Portuguese - Llama 2 datasets: - dominguesm/CC-MAIN-2023-23 inference: false pipeline_tag: text-generation model-index: - name: canarim-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 51.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 77.52 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 40.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 40.03 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 71.43 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 9.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dominguesm/canarim-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: ENEM (3-shot) type: enem_challenge config: main split: test args: num_few_shot: 3 metrics: - type: acc value: 26.96 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: BLUEX (3-shot) type: bluex config: main split: test args: num_few_shot: 3 metrics: - type: acc value: 29.76 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: OAB Exams (3-shot) type: oab_exams config: main split: test args: num_few_shot: 3 metrics: - type: acc value: 31.48 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: ASSIN2 RTE (15-shot) type: assin2_rte config: main split: test args: num_few_shot: 15 metrics: - type: acc value: 71.96 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: ASSIN2 STS (15-shot) type: assin2_sts config: main split: test args: num_few_shot: 15 metrics: - type: acc value: 13.33 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: FAQUAD NLI (15-shot) type: faquad_nli config: main split: test args: num_few_shot: 15 metrics: - type: acc value: 49.09 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: HateBR (25-shot) type: hatebr_offensive config: main split: test args: num_few_shot: 25 metrics: - type: acc value: 78.48 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech (25-shot) type: portuguese_hate_speech config: main split: test args: num_few_shot: 25 metrics: - type: acc value: 63.73 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results - task: type: text-generation name: Text Generation dataset: name: tweetSentBR (25-shot) type: tweetsentbr config: main split: test args: num_few_shot: 25 metrics: - type: acc value: 62.38 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=dominguesm/canarim-7b name: Open PT LLM Leaderboard Evaluation Results --- <p align="center"> <img width="250" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/Canarim-Instruct-PTBR/main/assets/canarim.png"> </p> <hr> # Canarim-7B Canarim-7B is a Portuguese large language model developed by [Maicon Domingues](https://nlp.rocks). ## Model description The model was pretrained on 16 billion tokens from the Portuguese subset of [CommonCrawl 2023-23](https://huggingface.co/datasets/dominguesm/CC-MAIN-2023-23), starting with the weights of LLaMA2-7B. The pretraining data has cutoff of mid-2023. ## Key Features - **Language:** Specialized in understanding and generating Portuguese text, making it ideal for applications targeting Portuguese-speaking audiences. - **Architecture:** Inherits the robust architecture from LLaMA2-7B, ensuring efficient performance and accurate results. - **Diverse Dataset:** The pretraining dataset includes a wide range of topics and writing styles, enhancing the model's ability to understand various contexts and nuances in Portuguese. ## Applications Canarim-7B, was trained solely on a language modeling objective and has not been fine-tuned for instruction following. Therefore, it is more suited for few-shot tasks rather than zero-shot tasks. This means the model tends to perform better when provided with a few examples of the desired outcome during use. Here are some practical applications: - **Natural Language Understanding (NLU):** Efficient in tasks such as sentiment analysis, topic classification, and entity recognition in Portuguese text, especially when relevant examples are provided. - **Natural Language Generation (NLG):** Capable of generating coherent and contextually relevant text, useful for content creation, chatbots, and more, with improved results when provided examples of the desired style or format. - **Language Translation:** Suitable for high-quality translation between Portuguese and other languages, especially when examples of desired translations are included during model training or fine-tuning. ### Tips for Efficient Use - **Few-shot Learning:** When using Canarim-7B for specific tasks, it is beneficial to provide a few relevant examples. This helps the model better understand the context and purpose of the task. - **Contextualization:** Including additional context in the input can significantly improve the quality of the model’s predictions and text generation. --- ## Getting Started To start using Canarim-7B with the Transformers library, first install the library if you haven't already: ```bash pip install transformers ``` You can then load the model using the Transformers library. Here's a simple example of how to use the model for text generation using the `pipeline` function: ```python from transformers import AutoTokenizer, pipeline import torch model_id = "dominguesm/canarim-7b" tokenizer = AutoTokenizer.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.float16, device_map="auto", ) prompt = make_prompt(question) sequences = pipe( prompt, do_sample=True, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=2048, temperature=0.9, top_p=0.6, repetition_penalty=1.15 ) ``` This code snippet demonstrates how to generate text with Canarim-7B. You can customize the input text and adjust parameters like `max_length` according to your requirements. ## How to Cite If you want to cite **Canarim-7B**, you could use this: ``` @misc {maicon_domingues_2023, author = { {Maicon Domingues} }, title = { canarim-7b (Revision 08fdd2b) }, year = 2023, url = { https://huggingface.co/dominguesm/canarim-7b }, doi = { 10.57967/hf/1356 }, publisher = { Hugging Face } } ``` ## Citations ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License Canarim-7B is released under the [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://ai.meta.com/llama/license/). ## [Open PT LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/dominguesm/canarim-7b) | Metric |Value| |---------------------------------|----:| |Avg. |47.36| |ENEM (3-Shot) |25.96| |BLUEX (3-Shot) |29.76| |OAB Exams (3-Shot) |31.48| |ASSIN2 RTE (15-shot) |71.96| |ASSIN2 STS (15-shot) |13.33| |FAQUAD NLI (15-shot) |49.09| |HateBR (25-shot) |78.48| |PT Hate Speech (25-shot) |63.73| |tweetSentBR (25-shot) |62.38| ## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dominguesm__canarim-7b) | Metric |Value| |---------------------------------|----:| |Avg. |48.63| |AI2 Reasoning Challenge (25-Shot)|51.96| |HellaSwag (10-Shot) |77.52| |MMLU (5-Shot) |40.92| |TruthfulQA (0-shot) |40.03| |Winogrande (5-shot) |71.43| |GSM8k (5-shot) | 9.93|
martinsinnona/visdecode_plotqa_2k
martinsinnona
2024-10-28T13:48:40Z
50
0
transformers
[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
image-text-to-text
2024-10-28T13:22:17Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
hmbyt5/byt5-small-english
hmbyt5
2024-10-28T13:47:38Z
21
0
transformers
[ "transformers", "pytorch", "jax", "tensorboard", "safetensors", "t5", "text2text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-04-08T20:08:44Z
--- license: mit language: - en --- # hmByT5 - Preliminary Language Models Preliminary Historic Multilingual and Monolingual ByT5 Models. Following languages are currently covered: * English (British Library Corpus - Books) More details can be found in [our GitHub repository](https://github.com/stefan-it/hmByT5). # Pretraining We use the official JAX/FLAX example in Hugging Face Transformers to pretrain a ByT5 model on a single v3-8 TPU. Details about the training can be found [here](https://github.com/stefan-it/hmByT5/tree/main/hmbyt5-flax). # Evaluation on Downstream Tasks (NER) We evaluated the hmByT5 model on downstream tasks: | Model | English AjMC | German AjMC | French AjMC | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | Avg. | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|--------------|--------------|-----------------|-----------------|--------------|--------------|------| | [`hmbyt5/byt5-small-english`](https://huggingface.co/hmbyt5/byt5-small-english) | 85.65 ± 1.21 | 87.27 ± 0.50 | 84.44 ± 0.79 | | | | | | # Acknowledgements 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 ❀️
dbmdz/bert-base-german-europeana-cased
dbmdz
2024-10-28T13:47:34Z
490
3
transformers
[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "historic german", "de", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: de license: mit tags: - "historic german" --- # πŸ€— + πŸ“š dbmdz BERT models In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources German Europeana BERT models πŸŽ‰ # German Europeana BERT We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/) that were provided by *The European Library*. The final training corpus has a size of 51GB and consists of 8,035,986,369 tokens. Detailed information about the data and pretraining steps can be found in [this repository](https://github.com/stefan-it/europeana-bert). ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | ------------------------------------------ | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-german-europeana-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/config.json) β€’ [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/pytorch_model.bin) β€’ [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/vocab.txt) ## Results For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert). ## Usage With Transformers >= 2.3 our German Europeana BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-europeana-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-cased") ``` # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) πŸ€— # Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❀️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage πŸ€—
dbmdz/bert-base-turkish-128k-uncased
dbmdz
2024-10-28T13:47:11Z
29,691
26
transformers
[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "tr", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: tr license: mit --- # πŸ€— + πŸ“š dbmdz Turkish BERT model In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources an uncased model for Turkish πŸŽ‰ # πŸ‡ΉπŸ‡· BERTurk BERTurk is a community-driven uncased BERT model for Turkish. Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. ## Stats The current version of the model is trained on a filtered and sentence segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). The final training corpus has a size of 35GB and 44,04,976,662 tokens. Thanks to Google's TensorFlow Research Cloud (TFRC) we could train an uncased model on a TPU v3-8 for 2M steps. For this model we use a vocab size of 128k. ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | -------------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-turkish-128k-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-uncased/config.json) β€’ [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-uncased/pytorch_model.bin) β€’ [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-uncased/vocab.txt) ## Usage With Transformers >= 2.3 our BERTurk uncased model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-128k-uncased") model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-128k-uncased") ``` ## Results For results on PoS tagging or NER tasks, please refer to [this repository](https://github.com/stefan-it/turkish-bert). # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) πŸ€— # Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❀️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage πŸ€—
dbmdz/electra-base-turkish-cased-discriminator
dbmdz
2024-10-28T13:47:02Z
212
2
transformers
[ "transformers", "pytorch", "tf", "safetensors", "electra", "pretraining", "tr", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: tr license: mit --- # πŸ€— + πŸ“š dbmdz Turkish ELECTRA model In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish πŸŽ‰ # Turkish ELECTRA model We release a base ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*. > ELECTRA is a new method for self-supervised language representation learning. It can be used to > pre-train transformer networks using relatively little compute. ELECTRA models are trained to > distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to > the discriminator of a GAN. More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB) or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub. ## Stats The current version of the model is trained on a filtered and sentence segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). The final training corpus has a size of 35GB and 44,04,976,662 tokens. Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model on a TPU v3-8 for 1M steps. ## Model weights [Transformers](https://github.com/huggingface/transformers) compatible weights for both PyTorch and TensorFlow are available. | Model | Downloads | ------------------------------------------------ | --------------------------------------------------------------------------------------------------------------- | `dbmdz/electra-base-turkish-cased-discriminator` | [`config.json`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/config.json) β€’ [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/pytorch_model.bin) β€’ [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/vocab.txt) ## Usage With Transformers >= 2.8 our ELECTRA base cased model can be loaded like: ```python from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator") model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator") ``` ## Results For results on PoS tagging or NER tasks, please refer to [this repository](https://github.com/stefan-it/turkish-bert/electra). # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our ELECTRA models just open an issue [here](https://github.com/dbmdz/berts/issues/new) πŸ€— # Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❀️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage πŸ€—
g-assismoraes/deberta-semeval25_noHINDI08_fold2
g-assismoraes
2024-10-28T13:45:24Z
162
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:42:31Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_noHINDI08_fold2 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. --> # deberta-semeval25_noHINDI08_fold2 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.8517 - Precision Samples: 0.1527 - Recall Samples: 0.5474 - F1 Samples: 0.2243 - Precision Macro: 0.7907 - Recall Macro: 0.3516 - F1 Macro: 0.2296 - Precision Micro: 0.1381 - Recall Micro: 0.4690 - F1 Micro: 0.2133 - Precision Weighted: 0.5289 - Recall Weighted: 0.4690 - F1 Weighted: 0.1485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.5058 | 1.0 | 16 | 10.2942 | 1.0 | 0.0 | 0.0 | 1.0 | 0.1818 | 0.1818 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 9.4489 | 2.0 | 32 | 9.9428 | 0.1940 | 0.2988 | 0.2165 | 0.9662 | 0.2242 | 0.1978 | 0.1906 | 0.1966 | 0.1935 | 0.8457 | 0.1966 | 0.0763 | | 10.0907 | 3.0 | 48 | 9.6744 | 0.1541 | 0.3731 | 0.2047 | 0.9300 | 0.2477 | 0.1983 | 0.1491 | 0.2690 | 0.1919 | 0.7249 | 0.2690 | 0.0775 | | 9.248 | 4.0 | 64 | 9.4563 | 0.1541 | 0.4441 | 0.2153 | 0.9010 | 0.2716 | 0.2064 | 0.1481 | 0.3345 | 0.2053 | 0.6702 | 0.3345 | 0.1007 | | 9.019 | 5.0 | 80 | 9.2882 | 0.1564 | 0.4842 | 0.2207 | 0.8711 | 0.2977 | 0.2181 | 0.1491 | 0.3897 | 0.2156 | 0.6126 | 0.3897 | 0.1272 | | 8.712 | 6.0 | 96 | 9.1284 | 0.1652 | 0.5359 | 0.2355 | 0.8646 | 0.3226 | 0.2216 | 0.1511 | 0.4414 | 0.2252 | 0.5993 | 0.4414 | 0.1369 | | 8.2497 | 7.0 | 112 | 8.9849 | 0.1629 | 0.5686 | 0.2357 | 0.8226 | 0.3406 | 0.2263 | 0.1453 | 0.4759 | 0.2226 | 0.5563 | 0.4759 | 0.1450 | | 8.2378 | 8.0 | 128 | 8.9047 | 0.1556 | 0.5406 | 0.2275 | 0.8014 | 0.3457 | 0.2283 | 0.1424 | 0.4690 | 0.2185 | 0.5376 | 0.4690 | 0.1466 | | 8.6465 | 9.0 | 144 | 8.8786 | 0.1525 | 0.5406 | 0.2237 | 0.7903 | 0.3478 | 0.2290 | 0.1391 | 0.4690 | 0.2145 | 0.5282 | 0.4690 | 0.1479 | | 8.7721 | 10.0 | 160 | 8.8517 | 0.1527 | 0.5474 | 0.2243 | 0.7907 | 0.3516 | 0.2296 | 0.1381 | 0.4690 | 0.2133 | 0.5289 | 0.4690 | 0.1485 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
SidXXD/77
SidXXD
2024-10-28T13:42:47Z
11
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-27T08:44:04Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/77 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
knifeayumu/Magnum-v4-Cydonia-v1.2-22B
knifeayumu
2024-10-28T13:41:44Z
6
5
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:TheDrummer/Cydonia-22B-v1.2", "base_model:merge:TheDrummer/Cydonia-22B-v1.2", "base_model:anthracite-org/magnum-v4-22b", "base_model:merge:anthracite-org/magnum-v4-22b", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-10-28T13:22:33Z
--- base_model: - TheDrummer/Cydonia-22B-v1.2 - anthracite-org/magnum-v4-22b library_name: transformers tags: - mergekit - merge license: other license_name: mrl inference: false license_link: https://mistral.ai/licenses/MRL-0.1.md --- ![Too Horny](Magnum-v4-Cydonia-v1.2-22B.png) # Magnum? More like Deagle (dies in cringe) [Cydonia-v1.2-Magnum-v4-22B](https://huggingface.co/knifeayumu/Cydonia-v1.2-Magnum-v4-22B) but inverse... Some prefer [anthracite-org/magnum-v4-22b](https://huggingface.co/anthracite-org/magnum-v4-22b) over [TheDrummer/Cydonia-22B-v1.2](https://huggingface.co/TheDrummer/Cydonia-22B-v1.2) so this merge is born. This is a merge of pre-trained language models created using [mergekit](https://github.com/arcee-ai/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [TheDrummer/Cydonia-22B-v1.2](https://huggingface.co/TheDrummer/Cydonia-22B-v1.2) * [anthracite-org/magnum-v4-22b](https://huggingface.co/anthracite-org/magnum-v4-22b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: anthracite-org/magnum-v4-22b - model: TheDrummer/Cydonia-22B-v1.2 merge_method: slerp base_model: anthracite-org/magnum-v4-22b parameters: t: [0.1, 0.3, 0.6, 0.3, 0.1] dtype: bfloat16 ```
LightDestory/test_save_2
LightDestory
2024-10-28T13:35:36Z
35
0
transformers
[ "transformers", "safetensors", "upernet", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-10-28T13:33:13Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. 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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]
jimregan/wav2vec2-xls-r-300m-phoneme-timit
jimregan
2024-10-28T13:35:16Z
15
0
transformers
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:timit_asr", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2024-03-07T16:59:26Z
--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-phoneme-timit results: [] datasets: - timit_asr language: - en --- <!-- 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. --> # working This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3630 - Wer: 0.6243 - Cer: 0.1316 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | 3.5325 | 11.9 | 1000 | 3.4897 | 1.0 | 0.9266 | | 2.1973 | 23.81 | 2000 | 1.1350 | 0.8396 | 0.2403 | | 1.4762 | 35.71 | 3000 | 0.5270 | 0.6845 | 0.1563 | | 1.2409 | 47.62 | 4000 | 0.4195 | 0.6331 | 0.1403 | | 1.1241 | 59.52 | 5000 | 0.3845 | 0.6362 | 0.1379 | | 1.024 | 71.43 | 6000 | 0.3716 | 0.6321 | 0.1355 | | 0.9922 | 83.33 | 7000 | 0.3728 | 0.6290 | 0.1331 | | 0.9432 | 95.24 | 8000 | 0.3648 | 0.6170 | 0.1321 | | 0.9279 | 107.14 | 9000 | 0.3643 | 0.6248 | 0.1325 | | 0.9268 | 119.05 | 10000 | 0.3630 | 0.6243 | 0.1316 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
devagonal/flan-t5-rouge-durga-q5-clean-4d
devagonal
2024-10-28T13:34:41Z
108
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2024-10-28T13:34:03Z
--- library_name: transformers license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-rouge-durga-q5-clean-4d 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. --> # flan-t5-rouge-durga-q5-clean-4d This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0519 - Rouge1: 0.5221 - Rouge2: 0.4278 - Rougel: 0.5213 - Rougelsum: 0.5204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.4357 | 1.0 | 9 | 1.9785 | 0.2586 | 0.0742 | 0.2539 | 0.2539 | | 2.6395 | 2.0 | 18 | 1.6948 | 0.2578 | 0.0708 | 0.2527 | 0.2525 | | 1.7751 | 3.0 | 27 | 1.4660 | 0.2843 | 0.0833 | 0.2773 | 0.2777 | | 2.0201 | 4.0 | 36 | 1.2841 | 0.3119 | 0.1080 | 0.3055 | 0.3068 | | 1.9879 | 5.0 | 45 | 1.1375 | 0.3388 | 0.1313 | 0.3321 | 0.3333 | | 1.6617 | 6.0 | 54 | 0.9940 | 0.3351 | 0.1264 | 0.3256 | 0.3259 | | 1.5556 | 7.0 | 63 | 0.8861 | 0.3647 | 0.1620 | 0.3567 | 0.3569 | | 1.2433 | 8.0 | 72 | 0.7889 | 0.3656 | 0.1716 | 0.3580 | 0.3579 | | 1.252 | 9.0 | 81 | 0.6992 | 0.3651 | 0.1773 | 0.3563 | 0.3571 | | 1.0389 | 10.0 | 90 | 0.6118 | 0.3777 | 0.1866 | 0.3699 | 0.3705 | | 0.6633 | 11.0 | 99 | 0.5348 | 0.3646 | 0.1800 | 0.3589 | 0.3584 | | 0.7738 | 12.0 | 108 | 0.4685 | 0.3909 | 0.2112 | 0.3844 | 0.3844 | | 0.7849 | 13.0 | 117 | 0.4048 | 0.3843 | 0.2150 | 0.3766 | 0.3769 | | 0.9278 | 14.0 | 126 | 0.3418 | 0.3973 | 0.2315 | 0.3915 | 0.3918 | | 0.7269 | 15.0 | 135 | 0.3038 | 0.4066 | 0.2593 | 0.4001 | 0.4016 | | 0.6558 | 16.0 | 144 | 0.2834 | 0.4323 | 0.2812 | 0.4289 | 0.4292 | | 0.5569 | 17.0 | 153 | 0.2396 | 0.4287 | 0.2817 | 0.4219 | 0.4235 | | 0.6052 | 18.0 | 162 | 0.2186 | 0.4382 | 0.2981 | 0.4323 | 0.4334 | | 0.575 | 19.0 | 171 | 0.1989 | 0.4194 | 0.2784 | 0.4159 | 0.4162 | | 0.5307 | 20.0 | 180 | 0.1722 | 0.4403 | 0.2978 | 0.4340 | 0.4357 | | 0.4588 | 21.0 | 189 | 0.1643 | 0.4636 | 0.3195 | 0.4570 | 0.4580 | | 0.3977 | 22.0 | 198 | 0.1431 | 0.4546 | 0.3234 | 0.4491 | 0.4504 | | 0.4509 | 23.0 | 207 | 0.1388 | 0.4621 | 0.3336 | 0.4567 | 0.4571 | | 0.3736 | 24.0 | 216 | 0.1277 | 0.4495 | 0.3262 | 0.4426 | 0.4438 | | 0.3618 | 25.0 | 225 | 0.1198 | 0.4622 | 0.3424 | 0.4571 | 0.4585 | | 0.3059 | 26.0 | 234 | 0.1090 | 0.4718 | 0.3475 | 0.4677 | 0.4678 | | 0.2782 | 27.0 | 243 | 0.1039 | 0.4722 | 0.3512 | 0.4675 | 0.4677 | | 0.2374 | 28.0 | 252 | 0.1006 | 0.4650 | 0.3408 | 0.4621 | 0.4625 | | 0.228 | 29.0 | 261 | 0.0945 | 0.4818 | 0.3571 | 0.4778 | 0.4782 | | 0.2778 | 30.0 | 270 | 0.0948 | 0.4732 | 0.3582 | 0.4710 | 0.4719 | | 0.2601 | 31.0 | 279 | 0.0889 | 0.4822 | 0.3626 | 0.4791 | 0.4803 | | 0.2364 | 32.0 | 288 | 0.0866 | 0.4863 | 0.3724 | 0.4851 | 0.4865 | | 0.2124 | 33.0 | 297 | 0.0855 | 0.4841 | 0.3666 | 0.4829 | 0.4836 | | 0.2004 | 34.0 | 306 | 0.0809 | 0.4835 | 0.3715 | 0.4819 | 0.4831 | | 0.2095 | 35.0 | 315 | 0.0764 | 0.4797 | 0.3666 | 0.4778 | 0.4796 | | 0.3603 | 36.0 | 324 | 0.0744 | 0.4934 | 0.3815 | 0.4924 | 0.4925 | | 0.181 | 37.0 | 333 | 0.0718 | 0.4863 | 0.3754 | 0.4864 | 0.4866 | | 0.1435 | 38.0 | 342 | 0.0687 | 0.4857 | 0.3778 | 0.4859 | 0.4861 | | 0.1306 | 39.0 | 351 | 0.0676 | 0.4921 | 0.3826 | 0.4903 | 0.4907 | | 0.1668 | 40.0 | 360 | 0.0667 | 0.4853 | 0.3784 | 0.4832 | 0.4845 | | 0.2279 | 41.0 | 369 | 0.0647 | 0.4998 | 0.3950 | 0.4967 | 0.4978 | | 0.2863 | 42.0 | 378 | 0.0638 | 0.5018 | 0.4022 | 0.4992 | 0.4997 | | 0.1381 | 43.0 | 387 | 0.0631 | 0.5066 | 0.4085 | 0.5037 | 0.5041 | | 0.1868 | 44.0 | 396 | 0.0611 | 0.5081 | 0.4068 | 0.5062 | 0.5061 | | 0.1351 | 45.0 | 405 | 0.0614 | 0.5018 | 0.4001 | 0.5011 | 0.5010 | | 0.1355 | 46.0 | 414 | 0.0604 | 0.5051 | 0.4027 | 0.5040 | 0.5045 | | 0.108 | 47.0 | 423 | 0.0588 | 0.4983 | 0.3956 | 0.4982 | 0.4983 | | 0.133 | 48.0 | 432 | 0.0573 | 0.5082 | 0.4069 | 0.5073 | 0.5075 | | 0.2242 | 49.0 | 441 | 0.0565 | 0.5117 | 0.4114 | 0.5104 | 0.5104 | | 0.1678 | 50.0 | 450 | 0.0548 | 0.5241 | 0.4272 | 0.5222 | 0.5225 | | 0.1282 | 51.0 | 459 | 0.0543 | 0.5224 | 0.4263 | 0.5206 | 0.5212 | | 0.15 | 52.0 | 468 | 0.0531 | 0.5171 | 0.4209 | 0.5161 | 0.5169 | | 0.1356 | 53.0 | 477 | 0.0528 | 0.5164 | 0.4178 | 0.5159 | 0.5158 | | 0.134 | 54.0 | 486 | 0.0527 | 0.5180 | 0.4228 | 0.5176 | 0.5178 | | 0.1321 | 55.0 | 495 | 0.0529 | 0.5162 | 0.4192 | 0.5155 | 0.5162 | | 0.1362 | 56.0 | 504 | 0.0526 | 0.5166 | 0.4206 | 0.5157 | 0.5156 | | 0.1764 | 57.0 | 513 | 0.0524 | 0.5170 | 0.4215 | 0.5153 | 0.5163 | | 0.1549 | 58.0 | 522 | 0.0522 | 0.5221 | 0.4278 | 0.5213 | 0.5204 | | 0.1475 | 59.0 | 531 | 0.0520 | 0.5221 | 0.4278 | 0.5213 | 0.5204 | | 0.1441 | 60.0 | 540 | 0.0519 | 0.5221 | 0.4278 | 0.5213 | 0.5204 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.5.0+cu121 - Datasets 3.0.2 - Tokenizers 0.20.1
g-assismoraes/deberta-semeval25_justEN08_fold4
g-assismoraes
2024-10-28T13:33:49Z
164
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:32:00Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_justEN08_fold4 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. --> # deberta-semeval25_justEN08_fold4 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.4863 - Precision Samples: 0.475 - Recall Samples: 0.475 - F1 Samples: 0.475 - Precision Macro: 0.9925 - Recall Macro: 0.3714 - F1 Macro: 0.3663 - Precision Micro: 0.475 - Recall Micro: 0.2135 - F1 Micro: 0.2946 - Precision Weighted: 0.8879 - Recall Weighted: 0.2135 - F1 Weighted: 0.1375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | No log | 1.0 | 5 | 9.3895 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3571 | 0.3571 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 8.4727 | 2.0 | 10 | 9.1240 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3571 | 0.3571 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 8.4727 | 3.0 | 15 | 8.9313 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.9967 | 4.0 | 20 | 8.7771 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.9967 | 5.0 | 25 | 8.6665 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.6436 | 6.0 | 30 | 8.6018 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.6436 | 7.0 | 35 | 8.5536 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.4488 | 8.0 | 40 | 8.5164 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.4488 | 9.0 | 45 | 8.4947 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | | 7.3567 | 10.0 | 50 | 8.4863 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.3714 | 0.3663 | 0.475 | 0.2135 | 0.2946 | 0.8879 | 0.2135 | 0.1375 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
g-assismoraes/deberta-semeval25_justEN08_fold3
g-assismoraes
2024-10-28T13:31:58Z
163
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:30:33Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_justEN08_fold3 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. --> # deberta-semeval25_justEN08_fold3 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.2204 - Precision Samples: 0.475 - Recall Samples: 0.475 - F1 Samples: 0.475 - Precision Macro: 0.9925 - Recall Macro: 0.4714 - F1 Macro: 0.4663 - Precision Micro: 0.475 - Recall Micro: 0.2184 - F1 Micro: 0.2992 - Precision Weighted: 0.8853 - Recall Weighted: 0.2184 - F1 Weighted: 0.1407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | No log | 1.0 | 5 | 9.2146 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4571 | 0.4571 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 8.5118 | 2.0 | 10 | 8.9478 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4571 | 0.4571 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 8.5118 | 3.0 | 15 | 8.7350 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 8.0538 | 4.0 | 20 | 8.5623 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 8.0538 | 5.0 | 25 | 8.4234 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 7.7127 | 6.0 | 30 | 8.3465 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 7.7127 | 7.0 | 35 | 8.2928 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 7.5228 | 8.0 | 40 | 8.2532 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 7.5228 | 9.0 | 45 | 8.2289 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | | 7.4198 | 10.0 | 50 | 8.2204 | 0.475 | 0.475 | 0.475 | 0.9925 | 0.4714 | 0.4663 | 0.475 | 0.2184 | 0.2992 | 0.8853 | 0.2184 | 0.1407 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
leekh7624/mymodel1
leekh7624
2024-10-28T13:31:39Z
5
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "base_model:finetune:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T13:27:26Z
--- base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - **Developed by:** leekh7624 - **License:** apache-2.0 - **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AIDSC/jais-13b
AIDSC
2024-10-28T13:28:05Z
8
0
null
[ "pytorch", "jais", "Arabic", "English", "LLM", "Decoder", "causal-lm", "text-generation", "custom_code", "ar", "en", "arxiv:2308.16149", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-generation
2024-10-23T11:45:01Z
--- language: - ar - en thumbnail: null tags: - Arabic - English - LLM - Decoder - causal-lm license: apache-2.0 pipeline_tag: text-generation --- # Jais-13b <!-- Provide a quick summary of what the model is/does. --> This is a 13 billion parameter pre-trained bilingual large language model for both Arabic and English, trained on a dataset containing 72 billion Arabic tokens and 279 billion English/code tokens. The Arabic data is iterated over for 1.6 epochs (as opposed to 1 epoch for English/code), for a total of 395 billion tokens of training. The model is based on transformer-based decoder-only (GPT-3) architecture and uses SwiGLU non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate to long sequence lengths, providing improved context handling and model precision. ## Getting started Below is sample code to use the model. Note that the model requires a custom model class, so users must enable `trust_remote_code=True` while loading the model. Also, note that this code is tested on `transformers==4.28.0`. ```python # -*- coding: utf-8 -*- import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path = "core42/jais-13b" device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True) def get_response(text,tokenizer=tokenizer,model=model): input_ids = tokenizer(text, return_tensors="pt").input_ids inputs = input_ids.to(device) input_len = inputs.shape[-1] generate_ids = model.generate( inputs, top_p=0.9, temperature=0.3, max_length=200-input_len, min_length=input_len + 4, repetition_penalty=1.2, do_sample=True, ) response = tokenizer.batch_decode( generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True )[0] return response text= "ΨΉΨ§Ψ΅Ω…Ψ© Ψ―ΩˆΩ„Ψ© Ψ§Ω„Ψ₯Ω…Ψ§Ψ±Ψ§Ψͺ Ψ§Ω„ΨΉΨ±Ψ¨ΩŠΨ© Ψ§Ω„Ω…ΨͺΨ­Ψ―Ψ© Ω‡" print(get_response(text)) text = "The capital of UAE is" print(get_response(text)) ``` ## Model Details - **Developed by:** [Inception](https://www.inceptioniai.org/en/), [Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)](https://mbzuai.ac.ae/), and [Cerebras Systems](https://www.cerebras.net/). - **Language(s) (NLP):** Arabic and English - **License:** Apache 2.0 - **Input:** Text only data. - **Output:** Model generates text. - **Paper :** [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/abs/2308.16149) - **Demo :** [Access here](https://arabic-gpt.ai) ## Intended Use <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> We release the Jais 13B model under a full open source license. We welcome all feedback and opportunities to collaborate. This model is the first release from the Inception - MBZUAI - Cerebras parternship, and at the time of release, achieved state of the art across a comprehensive Arabic test suite as described in the accompanying technical report. Some potential downstream uses include: - *Research*: This model can be used by researchers and developers. - *Commercial Use*: It can be used as a base model to further fine-tune for specific use cases (similar to [jais-13b-chat](https://huggingface.co/inception-mbzuai/jais-13b-chat)). Some potential use cases include: - Chat-assistants. - Customer service. Audiences that we hope will benefit from our model: - *Academics*: For those researching Arabic natural language processing. - *Businesses*: Companies targeting Arabic-speaking audiences. - *Developers*: Those integrating Arabic language capabilities in apps. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> While Jais-13b is a powerful Arabic and English bilingual model, it's essential to understand its limitations and the potential of misuse. It is prohibited to use the model in any manner that violates applicable laws or regulations. The following are some example scenarios where the model should not be used. - *Malicious Use*: The model should not be used for generating harmful, misleading, or inappropriate content. This includes but is not limited to: - Generating or promoting hate speech, violence, or discrimination. - Spreading misinformation or fake news. - Engaging in or promoting illegal activities. - *Sensitive Information*: The model should not be used to handle or generate personal, confidential, or sensitive information. - *Generalization Across All Languages*: Jais-13b is bilingual and optimized for Arabic and English, it should not be assumed to have equal proficiency in other languages or dialects. - *High-Stakes Decisions*: The model should not be used to make high-stakes decisions without human oversight. This includes medical, legal, financial, or safety-critical decisions. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The model is trained on publicly available data which was in part curated by Inception. We have employed different techniqes to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias. The model is trained as an AI assistant for Arabic and English speakers. The model is limited to produce responses for queries in these two languages and may not produce appropriate responses to other language queries. By using Jais, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content. The information is not intended as advice and should not be relied upon in any way, nor are we responsible for any of the content or consequences resulting from its use. We are continuously working to develop models with greater capabilities, and as such, welcome any feedback on the model. Copyright Inception Institute of Artificial Intelligence Ltd. JAIS is made available under the Apache License, Version 2.0 (the β€œLicense”). You shall not use JAIS except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, JAIS is distributed on an AS IS basis, without warranties or conditions of any kind, either express or implied. Please see the terms of the License for the specific language permissions and limitations under the License. ## 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. --> For the pre-training of Jais-13b, we used a diverse bilingual corpus sourced from the Web and other sources. We also used publicly available English and code datasets. To collect Arabic data, we use multiple sources including web pages, wikipedia articles, news articles, Arabic books, and social network content. We augment the volume of Arabic data by translating English to Arabic using an in-house machine translation system. We restrict this to high quality English resources such as English Wikipedia and English books. Further details about the training data can be found in the technical report. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> Training was performed on the Condor Galaxy 1 (CG-1) supercomputer platform. #### Training Hyperparameters | Hyperparameter | Value | |----------------------------|------------------------------| | Precision | fp32 | | Optimizer | AdamW | | Learning rate | 0 to 0.012 (<= 95 steps) | | | 0.012 to 0.0012 (> 95 steps) | | Weight decay | 0.1 | | Batch size | 1920 | | Steps | 100551 | ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> We conducted a comprehensive evaluation of Jais and benchmarked it other leading base language models, focusing on both English and Arabic. The evaluation criteria spanned various dimensions, including: - **Knowledge:** How well the model answers factual questions. - **Reasoning:** The model's ability to answer questions requiring reasoning. - **Misinformation/Bias:** Assessment of the model's susceptibility to generating false or misleading information, and its neutrality. Arabic evaluation results: | Models | Avg | EXAMS | MMLU (M) | LitQA | Hellaswag | PIQA | BoolQA | SituatedQA | ARC-C | OpenBookQA | TruthfulQA | CrowS-Pairs | |-------------|-------|-------|----------|-------|-----------|------|--------|------------|-------|------------|------------|-------------| | Jais (13B) | **46.5** | 40.4 | 30.0 | 58.3 | 57.7 | 67.6 | 62.6 | 42.5 | 35.8 | 32.4 | 41.1 | 58.4 | | BLOOM (7.1B) | 40.9 |34.0 | 28.2 | 37.1 | 40.9 | 58.4 | 59.9 | 39.1 | 27.3 | 28.0 | 44.4 | 53.5 | | LLaMA2 (13B) | 38.1 | 29.2 | 28.4 | 32.0 | 34.3 | 52.9 | 63.8 | 36.4 | 24.3 | 30.0 | 45.5 | 49.9 | | AraT5 (220M) | 32.0 | 24.7 | 23.8 | 26.3 | 25.5 | 50.4 | 58.2 | 33.9 | 24.7 | 25.4 | 20.9 | 47.2 | | AraBART (139M) | 36.7 | 26.5 | 27.5 | 34.3 | 28.1 | 52.6 | 57.1 | 34.6 | 25.1 | 28.6 | 49.8 | 48.8 | All tasks above report accuracy or F1 scores (the higher the better). For the sake of brevity, we do not include results over English tasks. Detailed comparisons in both languages and evaluation dataset details can be found in the technical report. ## Citation ``` @misc{sengupta2023jais, title={Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models}, author={Neha Sengupta and Sunil Kumar Sahu and Bokang Jia and Satheesh Katipomu and Haonan Li and Fajri Koto and Osama Mohammed Afzal and Samta Kamboj and Onkar Pandit and Rahul Pal and Lalit Pradhan and Zain Muhammad Mujahid and Massa Baali and Alham Fikri Aji and Zhengzhong Liu and Andy Hock and Andrew Feldman and Jonathan Lee and Andrew Jackson and Preslav Nakov and Timothy Baldwin and Eric Xing}, year={2023}, eprint={2308.16149}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` Copyright Inception Institute of Artificial Intelligence Ltd.
g-assismoraes/deberta-semeval25_EN08_fold5
g-assismoraes
2024-10-28T13:23:57Z
163
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:20:57Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_EN08_fold5 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. --> # deberta-semeval25_EN08_fold5 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.5536 - Precision Samples: 0.1328 - Recall Samples: 0.5774 - F1 Samples: 0.1999 - Precision Macro: 0.7392 - Recall Macro: 0.3847 - F1 Macro: 0.2352 - Precision Micro: 0.1241 - Recall Micro: 0.5105 - F1 Micro: 0.1996 - Precision Weighted: 0.4496 - Recall Weighted: 0.5105 - F1 Weighted: 0.1428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.6674 | 1.0 | 19 | 10.0433 | 1.0 | 0.0 | 0.0 | 1.0 | 0.2 | 0.2 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 9.4429 | 2.0 | 38 | 9.6521 | 0.1414 | 0.2694 | 0.1718 | 0.9653 | 0.2302 | 0.2100 | 0.1395 | 0.1592 | 0.1487 | 0.8422 | 0.1592 | 0.0522 | | 9.3994 | 3.0 | 57 | 9.3897 | 0.1330 | 0.3397 | 0.1774 | 0.9246 | 0.2579 | 0.2188 | 0.1260 | 0.2282 | 0.1624 | 0.7444 | 0.2282 | 0.0687 | | 8.911 | 4.0 | 76 | 9.1828 | 0.1419 | 0.4527 | 0.2027 | 0.8736 | 0.3021 | 0.2296 | 0.1353 | 0.3634 | 0.1972 | 0.6168 | 0.3634 | 0.1106 | | 8.8832 | 5.0 | 95 | 8.9865 | 0.1322 | 0.4795 | 0.1916 | 0.8439 | 0.3162 | 0.2333 | 0.1204 | 0.3874 | 0.1838 | 0.5762 | 0.3874 | 0.1141 | | 8.4356 | 6.0 | 114 | 8.8343 | 0.1401 | 0.5270 | 0.2034 | 0.8182 | 0.3416 | 0.2435 | 0.1300 | 0.4474 | 0.2015 | 0.5316 | 0.4474 | 0.1384 | | 8.737 | 7.0 | 133 | 8.7046 | 0.1360 | 0.5659 | 0.2039 | 0.7962 | 0.3743 | 0.2362 | 0.1291 | 0.4925 | 0.2046 | 0.5107 | 0.4925 | 0.1423 | | 8.7982 | 8.0 | 152 | 8.6328 | 0.1358 | 0.5783 | 0.2039 | 0.7842 | 0.3796 | 0.2357 | 0.1281 | 0.5105 | 0.2048 | 0.4997 | 0.5105 | 0.1451 | | 8.2308 | 9.0 | 171 | 8.5950 | 0.1334 | 0.5649 | 0.2002 | 0.7407 | 0.3774 | 0.2355 | 0.1242 | 0.4955 | 0.1986 | 0.4523 | 0.4955 | 0.1432 | | 8.7681 | 10.0 | 190 | 8.5536 | 0.1328 | 0.5774 | 0.1999 | 0.7392 | 0.3847 | 0.2352 | 0.1241 | 0.5105 | 0.1996 | 0.4496 | 0.5105 | 0.1428 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
g-assismoraes/deberta-semeval25_EN08_fold4
g-assismoraes
2024-10-28T13:20:53Z
199
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:17:23Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_EN08_fold4 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. --> # deberta-semeval25_EN08_fold4 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.1897 - Precision Samples: 0.1604 - Recall Samples: 0.6146 - F1 Samples: 0.2366 - Precision Macro: 0.7633 - Recall Macro: 0.4302 - F1 Macro: 0.2957 - Precision Micro: 0.1495 - Recall Micro: 0.5306 - F1 Micro: 0.2332 - Precision Weighted: 0.4778 - Recall Weighted: 0.5306 - F1 Weighted: 0.1751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.3966 | 1.0 | 19 | 10.8605 | 1.0 | 0.0 | 0.0 | 1.0 | 0.2333 | 0.2333 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 10.1016 | 2.0 | 38 | 10.4044 | 0.1770 | 0.2926 | 0.1885 | 0.9724 | 0.2657 | 0.2430 | 0.1751 | 0.1722 | 0.1737 | 0.8561 | 0.1722 | 0.0525 | | 9.6871 | 3.0 | 57 | 10.1400 | 0.1579 | 0.3440 | 0.1920 | 0.9413 | 0.2860 | 0.2471 | 0.1484 | 0.2333 | 0.1814 | 0.7657 | 0.2333 | 0.0649 | | 9.4348 | 4.0 | 76 | 9.8391 | 0.1748 | 0.4387 | 0.2291 | 0.8867 | 0.3321 | 0.2655 | 0.1568 | 0.3472 | 0.2161 | 0.6401 | 0.3472 | 0.1111 | | 9.2239 | 5.0 | 95 | 9.6192 | 0.1712 | 0.4963 | 0.2351 | 0.8277 | 0.3609 | 0.2784 | 0.1598 | 0.4111 | 0.2302 | 0.5594 | 0.4111 | 0.1433 | | 8.756 | 6.0 | 114 | 9.5185 | 0.1683 | 0.5525 | 0.2394 | 0.8002 | 0.3972 | 0.2954 | 0.1569 | 0.4694 | 0.2352 | 0.5206 | 0.4694 | 0.1611 | | 8.4617 | 7.0 | 133 | 9.3178 | 0.1606 | 0.5826 | 0.2330 | 0.7875 | 0.4124 | 0.2897 | 0.1483 | 0.5056 | 0.2294 | 0.5074 | 0.5056 | 0.1567 | | 7.9981 | 8.0 | 152 | 9.3682 | 0.1586 | 0.5750 | 0.2311 | 0.7686 | 0.4101 | 0.2891 | 0.1470 | 0.4889 | 0.2261 | 0.4698 | 0.4889 | 0.1577 | | 8.4678 | 9.0 | 171 | 9.2193 | 0.1617 | 0.5937 | 0.2359 | 0.7633 | 0.4199 | 0.2951 | 0.1513 | 0.5111 | 0.2335 | 0.4750 | 0.5111 | 0.1703 | | 8.2932 | 10.0 | 190 | 9.1897 | 0.1604 | 0.6146 | 0.2366 | 0.7633 | 0.4302 | 0.2957 | 0.1495 | 0.5306 | 0.2332 | 0.4778 | 0.5306 | 0.1751 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
g-assismoraes/deberta-semeval25_EN08_fold2
g-assismoraes
2024-10-28T13:14:05Z
199
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:10:35Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_EN08_fold2 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. --> # deberta-semeval25_EN08_fold2 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.6101 - Precision Samples: 0.1209 - Recall Samples: 0.5559 - F1 Samples: 0.1849 - Precision Macro: 0.7685 - Recall Macro: 0.3780 - F1 Macro: 0.2402 - Precision Micro: 0.1172 - Recall Micro: 0.4697 - F1 Micro: 0.1875 - Precision Weighted: 0.5025 - Recall Weighted: 0.4697 - F1 Weighted: 0.1391 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.3898 | 1.0 | 19 | 9.9343 | 1.0 | 0.0 | 0.0 | 1.0 | 0.1889 | 0.1889 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 10.0522 | 2.0 | 38 | 9.6084 | 0.1874 | 0.2767 | 0.2067 | 0.9620 | 0.2178 | 0.1992 | 0.1791 | 0.1606 | 0.1693 | 0.8331 | 0.1606 | 0.0539 | | 9.7928 | 3.0 | 57 | 9.3874 | 0.1336 | 0.3540 | 0.1804 | 0.9515 | 0.2427 | 0.2012 | 0.1294 | 0.2333 | 0.1665 | 0.7959 | 0.2333 | 0.0606 | | 9.4936 | 4.0 | 76 | 9.1515 | 0.1186 | 0.4298 | 0.1719 | 0.8698 | 0.2874 | 0.2133 | 0.1156 | 0.3242 | 0.1704 | 0.6379 | 0.3242 | 0.0854 | | 9.1022 | 5.0 | 95 | 8.9739 | 0.1205 | 0.4944 | 0.1790 | 0.8336 | 0.3224 | 0.2227 | 0.1158 | 0.3848 | 0.1780 | 0.5852 | 0.3848 | 0.1061 | | 9.2254 | 6.0 | 114 | 8.8771 | 0.1207 | 0.5078 | 0.1798 | 0.8340 | 0.3302 | 0.2245 | 0.1170 | 0.4030 | 0.1813 | 0.5860 | 0.4030 | 0.1106 | | 8.9117 | 7.0 | 133 | 8.7591 | 0.1147 | 0.5250 | 0.1755 | 0.7877 | 0.3399 | 0.2259 | 0.1118 | 0.4273 | 0.1772 | 0.5301 | 0.4273 | 0.1160 | | 8.7312 | 8.0 | 152 | 8.6366 | 0.1215 | 0.5708 | 0.1872 | 0.7836 | 0.3750 | 0.2412 | 0.1171 | 0.4697 | 0.1874 | 0.5273 | 0.4697 | 0.1418 | | 8.953 | 9.0 | 171 | 8.6276 | 0.1199 | 0.5553 | 0.1831 | 0.7682 | 0.3625 | 0.2377 | 0.1165 | 0.4667 | 0.1864 | 0.5065 | 0.4667 | 0.1396 | | 8.1407 | 10.0 | 190 | 8.6101 | 0.1209 | 0.5559 | 0.1849 | 0.7685 | 0.3780 | 0.2402 | 0.1172 | 0.4697 | 0.1875 | 0.5025 | 0.4697 | 0.1391 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
SidXXD/95
SidXXD
2024-10-28T13:14:04Z
5
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-27T08:15:36Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/95 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
g-assismoraes/deberta-semeval25_EN08_fold1
g-assismoraes
2024-10-28T13:10:31Z
164
0
transformers
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T13:07:35Z
--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-semeval25_EN08_fold1 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. --> # deberta-semeval25_EN08_fold1 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.2509 - Precision Samples: 0.1249 - Recall Samples: 0.6497 - F1 Samples: 0.1949 - Precision Macro: 0.7291 - Recall Macro: 0.4651 - F1 Macro: 0.2720 - Precision Micro: 0.1058 - Recall Micro: 0.5833 - F1 Micro: 0.1791 - Precision Weighted: 0.4269 - Recall Weighted: 0.5833 - F1 Weighted: 0.1477 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| | 10.742 | 1.0 | 19 | 9.6524 | 0.9589 | 0.0205 | 0.0205 | 0.9926 | 0.2234 | 0.2240 | 0.3333 | 0.0093 | 0.0180 | 0.9403 | 0.0093 | 0.0141 | | 10.3264 | 2.0 | 38 | 9.2947 | 0.1229 | 0.2857 | 0.1608 | 0.9497 | 0.2667 | 0.2315 | 0.1199 | 0.1975 | 0.1492 | 0.8249 | 0.1975 | 0.0493 | | 9.6673 | 3.0 | 57 | 9.0965 | 0.1046 | 0.3364 | 0.1497 | 0.8967 | 0.2889 | 0.2395 | 0.1043 | 0.2562 | 0.1482 | 0.7240 | 0.2562 | 0.0668 | | 9.8896 | 4.0 | 76 | 8.8422 | 0.1293 | 0.4635 | 0.1839 | 0.8434 | 0.3589 | 0.2560 | 0.1089 | 0.3951 | 0.1708 | 0.6154 | 0.3951 | 0.1115 | | 9.3618 | 5.0 | 95 | 8.6755 | 0.1328 | 0.5445 | 0.1914 | 0.8002 | 0.4059 | 0.2609 | 0.1064 | 0.5 | 0.1754 | 0.5064 | 0.5 | 0.1267 | | 9.3241 | 6.0 | 114 | 8.5167 | 0.1332 | 0.6158 | 0.2021 | 0.8049 | 0.4417 | 0.2741 | 0.1122 | 0.5525 | 0.1865 | 0.5153 | 0.5525 | 0.1459 | | 8.8868 | 7.0 | 133 | 8.3815 | 0.1264 | 0.6295 | 0.1955 | 0.7506 | 0.4425 | 0.2703 | 0.1084 | 0.5556 | 0.1815 | 0.4567 | 0.5556 | 0.1413 | | 8.9554 | 8.0 | 152 | 8.3410 | 0.1273 | 0.6363 | 0.1981 | 0.7317 | 0.4531 | 0.2747 | 0.1104 | 0.5648 | 0.1848 | 0.4291 | 0.5648 | 0.1491 | | 8.8845 | 9.0 | 171 | 8.2870 | 0.1274 | 0.6487 | 0.1990 | 0.7306 | 0.4630 | 0.2744 | 0.1104 | 0.5833 | 0.1857 | 0.4288 | 0.5833 | 0.1511 | | 8.5189 | 10.0 | 190 | 8.2509 | 0.1249 | 0.6497 | 0.1949 | 0.7291 | 0.4651 | 0.2720 | 0.1058 | 0.5833 | 0.1791 | 0.4269 | 0.5833 | 0.1477 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.3.1 - Datasets 2.21.0 - Tokenizers 0.20.1
german-nlp-group/electra-base-german-uncased
german-nlp-group
2024-10-28T13:09:01Z
2,719
6
transformers
[ "transformers", "pytorch", "safetensors", "electra", "pretraining", "commoncrawl", "uncased", "umlaute", "umlauts", "german", "deutsch", "de", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: de license: mit thumbnail: "https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/german-electra-logo.png" tags: - electra - commoncrawl - uncased - umlaute - umlauts - german - deutsch --- # German Electra Uncased <img width="300px" src="https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/german-electra-logo.png"> [ΒΉ] ## Version 2 Release We released an improved version of this model. Version 1 was trained for 766,000 steps. For this new version we continued the training for an additional 734,000 steps. It therefore follows that version 2 was trained on a total of 1,500,000 steps. See "Evaluation of Version 2: GermEval18 Coarse" below for details. ## Model Info This Model is suitable for training on many downstream tasks in German (Q&A, Sentiment Analysis, etc.). It can be used as a drop-in replacement for **BERT** in most down-stream tasks (**ELECTRA** is even implemented as an extended **BERT** Class). At the time of release (August 2020) this model is the best performing publicly available German NLP model on various German evaluation metrics (CONLL03-DE, GermEval18 Coarse, GermEval18 Fine). For GermEval18 Coarse results see below. More will be published soon. ## Installation This model has the special feature that it is **uncased** but does **not strip accents**. This possibility was added by us with [PR #6280](https://github.com/huggingface/transformers/pull/6280). To use it you have to use Transformers version 3.1.0 or newer. ```bash pip install transformers -U ``` ## Uncase and Umlauts ('Γ–', 'Γ„', 'Ü') This model is uncased. This helps especially for domains where colloquial terms with uncorrect capitalization is often used. The special characters 'ΓΆ', 'ΓΌ', 'Γ€' are included through the `strip_accent=False` option, as this leads to an improved precision. ## Creators This model was trained and open sourced in conjunction with the [**German NLP Group**](https://github.com/German-NLP-Group) in equal parts by: - [**Philip May**](https://philipmay.org) - [Deutsche Telekom](https://www.telekom.de/) - [**Philipp Reißel**](https://www.linkedin.com/in/philipp-reissel/) - [ambeRoad](https://amberoad.de/) ## Evaluation of Version 2: GermEval18 Coarse We evaluated all language models on GermEval18 with the F1 macro score. For each model we did an extensive automated hyperparameter search. With the best hyperparmeters we did fit the moodel multiple times on GermEval18. This is done to cancel random effects and get results of statistical relevance. ![GermEval18 Coarse Model Evaluation for Version 2](https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/model-eval-v2.png) ## Checkpoint evaluation Since it it not guaranteed that the last checkpoint is the best, we evaluated the checkpoints on GermEval18. We found that the last checkpoint is indeed the best. The training was stable and did not overfit the text corpus. ## Pre-training details ### Data - Cleaned Common Crawl Corpus 2019-09 German: [CC_net](https://github.com/facebookresearch/cc_net) (Only head coprus and filtered for language_score > 0.98) - 62 GB - German Wikipedia Article Pages Dump (20200701) - 5.5 GB - German Wikipedia Talk Pages Dump (20200620) - 1.1 GB - Subtitles - 823 MB - News 2018 - 4.1 GB The sentences were split with [SojaMo](https://github.com/tsproisl/SoMaJo). We took the German Wikipedia Article Pages Dump 3x to oversample. This approach was also used in a similar way in GPT-3 (Table 2.2). More Details can be found here [Preperaing Datasets for German Electra Github](https://github.com/German-NLP-Group/german-transformer-training) ### Electra Branch no_strip_accents Because we do not want to stip accents in our training data we made a change to Electra and used this repo [Electra no_strip_accents](https://github.com/PhilipMay/electra/tree/no_strip_accents) (branch `no_strip_accents`). Then created the tf dataset with: ```bash python build_pretraining_dataset.py --corpus-dir <corpus_dir> --vocab-file <dir>/vocab.txt --output-dir ./tf_data --max-seq-length 512 --num-processes 8 --do-lower-case --no-strip-accents ``` ### The training The training itself can be performed with the Original Electra Repo (No special case for this needed). We run it with the following Config: <details> <summary>The exact Training Config</summary> <br/>debug False <br/>disallow_correct False <br/>disc_weight 50.0 <br/>do_eval False <br/>do_lower_case True <br/>do_train True <br/>electra_objective True <br/>embedding_size 768 <br/>eval_batch_size 128 <br/>gcp_project None <br/>gen_weight 1.0 <br/>generator_hidden_size 0.33333 <br/>generator_layers 1.0 <br/>iterations_per_loop 200 <br/>keep_checkpoint_max 0 <br/>learning_rate 0.0002 <br/>lr_decay_power 1.0 <br/>mask_prob 0.15 <br/>max_predictions_per_seq 79 <br/>max_seq_length 512 <br/>model_dir gs://XXX <br/>model_hparam_overrides {} <br/>model_name 02_Electra_Checkpoints_32k_766k_Combined <br/>model_size base <br/>num_eval_steps 100 <br/>num_tpu_cores 8 <br/>num_train_steps 766000 <br/>num_warmup_steps 10000 <br/>pretrain_tfrecords gs://XXX <br/>results_pkl gs://XXX <br/>results_txt gs://XXX <br/>save_checkpoints_steps 5000 <br/>temperature 1.0 <br/>tpu_job_name None <br/>tpu_name electrav5 <br/>tpu_zone None <br/>train_batch_size 256 <br/>uniform_generator False <br/>untied_generator True <br/>untied_generator_embeddings False <br/>use_tpu True <br/>vocab_file gs://XXX <br/>vocab_size 32767 <br/>weight_decay_rate 0.01 </details> ![Training Loss](https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/loss.png) Please Note: *Due to the GAN like strucutre of Electra the loss is not that meaningful* It took about 7 Days on a preemtible TPU V3-8. In total, the Model went through approximately 10 Epochs. For an automatically recreation of a cancelled TPUs we used [tpunicorn](https://github.com/shawwn/tpunicorn). The total cost of training summed up to about 450 $ for one run. The Data-pre processing and Vocab Creation needed approximately 500-1000 CPU hours. Servers were fully provided by [T-Systems on site services GmbH](https://www.t-systems-onsite.de/), [ambeRoad](https://amberoad.de/). Special thanks to [Stefan Schweter](https://github.com/stefan-it) for your feedback and providing parts of the text corpus. [ΒΉ]: Source for the picture [Pinterest](https://www.pinterest.cl/pin/371828512984142193/) ### Negative Results We tried the following approaches which we found had no positive influence: - **Increased Vocab Size**: Leads to more parameters and thus reduced examples/sec while no visible Performance gains were measured - **Decreased Batch-Size**: The original Electra was trained with a Batch Size per TPU Core of 16 whereas this Model was trained with 32 BS / TPU Core. We found out that 32 BS leads to better results when you compare metrics over computation time ## License - The MIT License Copyright 2020-2021 [Philip May](https://philipmay.org)\ Copyright 2020-2021 [Philipp Reißel](https://www.linkedin.com/in/philipp-reissel/) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF
mradermacher
2024-10-28T12:58:03Z
49
0
transformers
[ "transformers", "gguf", "ru", "en", "dataset:Vikhrmodels/GrandMaster-PRO-MAX", "base_model:Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct", "base_model:quantized:Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T12:56:02Z
--- base_model: Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct datasets: - Vikhrmodels/GrandMaster-PRO-MAX language: - ru - en library_name: transformers license: apache-2.0 model_name: Vikhr-Qwen-2.5-0.5b-Instruct quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q3_K_S.gguf) | Q3_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q2_K.gguf) | Q2_K | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.IQ4_XS.gguf) | IQ4_XS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q3_K_L.gguf) | Q3_K_L | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q5_K_S.gguf) | Q5_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q5_K_M.gguf) | Q5_K_M | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q6_K.gguf) | Q6_K | 0.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Vikhr-Qwen-2.5-0.5b-Instruct-GGUF/resolve/main/Vikhr-Qwen-2.5-0.5b-Instruct.f16.gguf) | f16 | 1.1 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Shilin-LU/VINE-R-Enc
Shilin-LU
2024-10-28T12:54:59Z
37
0
null
[ "safetensors", "image_watermarking", "image-to-image", "en", "dataset:BleachNick/UltraEdit", "arxiv:2410.18775", "base_model:stabilityai/sdxl-turbo", "base_model:finetune:stabilityai/sdxl-turbo", "license:mit", "region:us" ]
image-to-image
2024-10-28T11:25:01Z
--- tags: - image_watermarking license: mit datasets: - BleachNick/UltraEdit language: - en base_model: - stabilityai/sdxl-turbo pipeline_tag: image-to-image --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Docs: https://github.com/Shilin-LU/VINE - arXiv: https://arxiv.org/abs/2410.18775
Shilin-LU/VINE-B-Dec
Shilin-LU
2024-10-28T12:54:37Z
14
0
null
[ "safetensors", "image-watermarking", "en", "arxiv:2410.18775", "license:mit", "region:us" ]
null
2024-10-28T11:42:08Z
--- tags: - image-watermarking license: mit language: - en --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Docs: https://github.com/Shilin-LU/VINE - arXiv: https://arxiv.org/abs/2410.18775
glif-loradex-trainer/bingbangboom_flux_dev_SMPGCLRPHTO
glif-loradex-trainer
2024-10-28T12:42:53Z
80
2
diffusers
[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]
text-to-image
2024-10-13T16:22:11Z
--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1728836939821__000002500_0.jpg text: A young boy seated on a weathered concrete wall beside a calm river. The boy is dressed in a dark blue coat, beige trousers, and a black hat, exuding a contemplative demeanor. The river reflects the overcast sky, with boats moored in the distance, photo in the style of SMPGCLRPHTO - output: url: samples/2.jpg text: a cat in a field of lavender flowers, photo in the style of SMPGCLRPHTO - output: url: samples/1728836987315__000002500_2.jpg text: a portrait of a woman, japanese countryside, photo in the style of SMPGCLRPHTO - output: url: samples/4.jpg text: a woman wearing a yellow sundress and a summer hat, reading a book, background of lush leaves, sitting on a bench in a public park, photo in the style of SMPGCLRPHTO - output: url: samples/5.jpg text: a cat taking a nap on a work table, photo in the style of SMPGCLRPHTO - output: url: samples/6.jpg text: a robot eating ramen in a busy cafe, photo in the style of SMPGCLRPHTO base_model: black-forest-labs/FLUX.1-dev trigger: SMPGCLRPHTO instance_prompt: SMPGCLRPHTO license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # SMPGCLRPHTO (SMPG Color Photo) Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user [bingbangboom](https://huggingface.co/bingbangboom). Flux LoRA for creating a faux vintage digital color composite (from glass negatives) effect. Use '**photo in the style of SMPGCLRPHTO**' to trigger the model <Gallery /> ## Trigger words You should use `SMPGCLRPHTO` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/bingbangboom_flux_dev_SMPGCLRPHTO/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
glif-loradex-trainer/lemnop_Acid_Graphics_Adv
glif-loradex-trainer
2024-10-28T12:42:36Z
64
2
diffusers
[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]
text-to-image
2024-10-28T12:42:02Z
--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1730119168914__000003000_0.jpg text: ACXD-GFX, Logo - output: url: samples/1730119193605__000003000_1.jpg text: ACXD-GFX, Graffiti - output: url: samples/1730119218484__000003000_2.jpg text: ACXD-GFX, Smiley Face - output: url: samples/1730119243361__000003000_3.jpg text: ACXD-GFX, Graphics - output: url: samples/1730119268231__000003000_4.jpg text: ACXD-GFX, many icons - output: url: samples/1730119293022__000003000_5.jpg text: ACXD-GFX, Globe base_model: black-forest-labs/FLUX.1-dev trigger: ACXD-GFX instance_prompt: ACXD-GFX license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # Acid_Graphics_Adv Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `lemnop`. <Gallery /> ## Trigger words You should use `ACXD-GFX` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/lemnop_Acid_Graphics_Adv/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
ggml-org
2024-10-28T12:40:32Z
2,892
1
transformers
[ "transformers", "gguf", "code", "qwen", "qwen-coder", "codeqwen", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-Coder-7B", "base_model:quantized:Qwen/Qwen2.5-Coder-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2024-10-28T12:38:56Z
--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE language: - en base_model: Qwen/Qwen2.5-Coder-7B pipeline_tag: text-generation library_name: transformers tags: - code - qwen - qwen-coder - codeqwen - llama-cpp - gguf-my-repo --- # ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-Coder-7B`](https://huggingface.co/Qwen/Qwen2.5-Coder-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-Coder-7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF --hf-file qwen2.5-coder-7b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF --hf-file qwen2.5-coder-7b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF --hf-file qwen2.5-coder-7b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF --hf-file qwen2.5-coder-7b-q8_0.gguf -c 2048 ```
gerald29/my_awesome_food_model
gerald29
2024-10-28T12:39:48Z
142
0
transformers
[ "transformers", "safetensors", "dinov2", "image-classification", "generated_from_trainer", "base_model:facebook/dinov2-base-imagenet1k-1-layer", "base_model:finetune:facebook/dinov2-base-imagenet1k-1-layer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2024-10-11T01:44:33Z
--- library_name: transformers license: apache-2.0 base_model: facebook/dinov2-base-imagenet1k-1-layer tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_food_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_food_model This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-base-imagenet1k-1-layer) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1930 - Accuracy: 0.943 This is just a model created by following the the Tramnformers tutorial on image classification at https://huggingface.co/docs/transformers/main/en/tasks/image_classification So quite worthless ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3989 | 0.992 | 62 | 0.3865 | 0.867 | | 0.2722 | 2.0 | 125 | 0.2720 | 0.916 | | 0.126 | 2.976 | 186 | 0.1930 | 0.943 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
scherrmann/GermanFinBert_SC_Sentiment
scherrmann
2024-10-28T12:31:41Z
169
3
transformers
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "de", "arxiv:2311.08793", "arxiv:1307.5336", "arxiv:1708.07120", "arxiv:1412.6980", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-11-17T10:28:32Z
--- license: apache-2.0 language: - de widget: - text: "STS Group AG erhÀlt Großauftrag von führendem Nutzfahrzeughersteller in Nordamerika und plant Bau eines ersten US-Werks" - text: "Zukünftig soll jedoch je GeschÀftsjahr eine Mindestdividende in Hâhe von EUR 2,00 je dividendenberechtigter Aktie an die AktionÀrinnen und AktionÀre ausgeschüttet werden." - text: "Comet passt Jahresprognose nach Q3 unter Erwartungen an" --- # German FinBERT For Sentiment Analysis (Pre-trained From Scratch Version, Fine-Tuned for Financial Sentiment Analysis) <img src="https://github.com/mscherrmann/mscherrmann.github.io/blob/master/assets/img/publication_preview/germanBert.png?raw=true" alt="Alt text for the image" width="500" height="300"/> German FinBERT is a BERT language model focusing on the financial domain within the German language. In my [paper](https://arxiv.org/pdf/2311.08793.pdf), I describe in more detail the steps taken to train the model and show that it outperforms its generic benchmarks for finance specific downstream tasks. This model is the [pre-trained from scratch version of German FinBERT](https://huggingface.co/scherrmann/GermanFinBert_SC), after fine-tuning on a translated version of the [financial news phrase bank](https://arxiv.org/abs/1307.5336) of Malo et al. (2013). The data is available [here](https://huggingface.co/datasets/scherrmann/financial_phrasebank_75agree_german). ## Overview **Author** Moritz Scherrmann **Paper:** [here](https://arxiv.org/pdf/2311.08793.pdf) **Architecture:** BERT base **Language:** German **Specialization:** Financial sentiment **Base model:** [German_FinBert_SC](https://huggingface.co/scherrmann/GermanFinBert_SC) ### Fine-tuning I fine-tune the model using the 1cycle policy of [Smith and Topin (2019)](https://arxiv.org/abs/1708.07120). I use the Adam optimization method of [Kingma and Ba (2014)](https://arxiv.org/abs/1412.6980) with standard parameters.I run a grid search on the evaluation set to find the best hyper-parameter setup. I test different values for learning rate, batch size and number of epochs, following the suggestions of [Chalkidis et al. (2020)](https://aclanthology.org/2020.findings-emnlp.261/). I repeat the fine-tuning for each setup five times with different seeds, to avoid getting good results by chance. After finding the best model w.r.t the evaluation set, I report the mean result across seeds for that model on the test set. ### Results Translated [Financial news phrase bank](https://arxiv.org/abs/1307.5336) (Malo et al. (2013)), see [here](https://huggingface.co/datasets/scherrmann/financial_phrasebank_75agree_german) for the data: - Accuracy: 95.95% - Macro F1: 92.70% ## Authors Moritz Scherrmann: `scherrmann [at] lmu.de` For additional details regarding the performance on fine-tune datasets and benchmark results, please refer to the full documentation provided in the study. See also: - scherrmann/GermanFinBERT_SC - scherrmann/GermanFinBERT_FP - scherrmann/GermanFinBERT_FP_QuAD
aizenSosuke/sentence-similarity-finetuned-adrta
aizenSosuke
2024-10-28T12:28:43Z
107
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-10-28T12:28:37Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Collov-Labs/Monetico
Collov-Labs
2024-10-28T12:28:09Z
23
65
diffusers
[ "diffusers", "safetensors", "Non-Autoregressive", "text-to-image", "arxiv:2410.08261", "license:apache-2.0", "diffusers:Pipeline", "region:us" ]
text-to-image
2024-10-28T08:19:22Z
--- pipeline_tag: text-to-image license: apache-2.0 tags: - Non-Autoregressive --- # Monetico: An Efficient Reproduction of Meissonic for Text-to-Image Synthesis ## Introduction Similar to Meissonic, Monetico is a non-autoregressive masked image modeling text-to-image synthesis model capable of generating high-resolution images. It is designed to run efficiently on consumer-grade graphics cards. Monetico is an efficient reproduction of Meissonic. Trained on 8 H100 GPUs for approximately one week, Monetico can generate high-quality 512x512 images that are comparable to those produced by Meissonic and SDXL. Monetico was developed by Collov Labs. We extend our gratitude to @MeissonFlow and @viiika for their valuable advice on efficient training. ## Usage For detailed usage instructions, please refer to [GitHub repository](https://github.com/viiika/Meissonic). ## Citation If you find this work helpful, please consider citing: ```bibtex @article{bai2024meissonic, title={Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis}, author={Bai, Jinbin and Ye, Tian and Chow, Wei and Song, Enxin and Chen, Qing-Guo and Li, Xiangtai and Dong, Zhen and Zhu, Lei and Yan, Shuicheng}, journal={arXiv preprint arXiv:2410.08261}, year={2024} } ```
victomoe/setfit-intent-classifier-3
victomoe
2024-10-28T12:28:08Z
7
0
setfit
[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "region:us" ]
text-classification
2024-10-28T12:27:50Z
--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: Can you set an alarm? - text: Bring me one floor higher - text: I’d like to go to floor 2. - text: Okay, go ahead. - text: I’d like to go down two floors pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 8 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------| | RequestMoveToFloor | <ul><li>'Please go to the 3rd floor.'</li><li>'Can you take me to floor 5?'</li><li>'I need to go to the 8th floor.'</li></ul> | | RequestMoveUp | <ul><li>'Go one floor up'</li><li>'Take me up two floors'</li><li>'Go up three floors, please'</li></ul> | | RequestMoveDown | <ul><li>'Move me down one level'</li><li>'Can you take me down two floors?'</li><li>'Go down three levels'</li></ul> | | Confirm | <ul><li>"Yes, that's right."</li><li>'Sure.'</li><li>'Exactly.'</li></ul> | | RequestEmployeeLocation | <ul><li>'Where is Erik Velldal’s office?'</li><li>'Which floor is Andreas Austeng on?'</li><li>'Can you tell me where Birthe Soppe’s office is?'</li></ul> | | CurrentFloor | <ul><li>'Which floor are we on?'</li><li>'What floor is this?'</li><li>'Are we on the 5th floor?'</li></ul> | | Stop | <ul><li>'Stop the elevator.'</li><li>"Wait, don't go to that floor."</li><li>'No, not that floor.'</li></ul> | | OutOfCoverage | <ul><li>"What's the capital of France?"</li><li>'How many floors does this building have?'</li><li>'Can you make a phone call for me?'</li></ul> | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the πŸ€— Hub model = SetFitModel.from_pretrained("victomoe/setfit-intent-classifier-3") # Run inference preds = model("Okay, go ahead.") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 1 | 5.2118 | 9 | | Label | Training Sample Count | |:------------------------|:----------------------| | Confirm | 22 | | CurrentFloor | 21 | | OutOfCoverage | 22 | | RequestEmployeeLocation | 22 | | RequestMoveDown | 20 | | RequestMoveToFloor | 23 | | RequestMoveUp | 20 | | Stop | 20 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0013 | 1 | 0.195 | - | | 0.0633 | 50 | 0.1877 | - | | 0.1266 | 100 | 0.1592 | - | | 0.1899 | 150 | 0.1141 | - | | 0.2532 | 200 | 0.0603 | - | | 0.3165 | 250 | 0.0283 | - | | 0.3797 | 300 | 0.0104 | - | | 0.4430 | 350 | 0.0043 | - | | 0.5063 | 400 | 0.0027 | - | | 0.5696 | 450 | 0.0021 | - | | 0.6329 | 500 | 0.0017 | - | | 0.6962 | 550 | 0.0015 | - | | 0.7595 | 600 | 0.0011 | - | | 0.8228 | 650 | 0.001 | - | | 0.8861 | 700 | 0.0011 | - | | 0.9494 | 750 | 0.0008 | - | | 1.0127 | 800 | 0.0007 | - | | 1.0759 | 850 | 0.0006 | - | | 1.1392 | 900 | 0.0006 | - | | 1.2025 | 950 | 0.0005 | - | | 1.2658 | 1000 | 0.0005 | - | | 1.3291 | 1050 | 0.0005 | - | | 1.3924 | 1100 | 0.0004 | - | | 1.4557 | 1150 | 0.0004 | - | | 1.5190 | 1200 | 0.0004 | - | | 1.5823 | 1250 | 0.0004 | - | | 1.6456 | 1300 | 0.0004 | - | | 1.7089 | 1350 | 0.0003 | - | | 1.7722 | 1400 | 0.0003 | - | | 1.8354 | 1450 | 0.0003 | - | | 1.8987 | 1500 | 0.0003 | - | | 1.9620 | 1550 | 0.0003 | - | | 2.0253 | 1600 | 0.0003 | - | | 2.0886 | 1650 | 0.0003 | - | | 2.1519 | 1700 | 0.0003 | - | | 2.2152 | 1750 | 0.0003 | - | | 2.2785 | 1800 | 0.0003 | - | | 2.3418 | 1850 | 0.0002 | - | | 2.4051 | 1900 | 0.0002 | - | | 2.4684 | 1950 | 0.0002 | - | | 2.5316 | 2000 | 0.0002 | - | | 2.5949 | 2050 | 0.0002 | - | | 2.6582 | 2100 | 0.0002 | - | | 2.7215 | 2150 | 0.0002 | - | | 2.7848 | 2200 | 0.0002 | - | | 2.8481 | 2250 | 0.0002 | - | | 2.9114 | 2300 | 0.0002 | - | | 2.9747 | 2350 | 0.0002 | - | | 3.0380 | 2400 | 0.0002 | - | | 3.1013 | 2450 | 0.0009 | - | | 3.1646 | 2500 | 0.0003 | - | | 3.2278 | 2550 | 0.0002 | - | | 3.2911 | 2600 | 0.0002 | - | | 3.3544 | 2650 | 0.0002 | - | | 3.4177 | 2700 | 0.0002 | - | | 3.4810 | 2750 | 0.0002 | - | | 3.5443 | 2800 | 0.0002 | - | | 3.6076 | 2850 | 0.0002 | - | | 3.6709 | 2900 | 0.0002 | - | | 3.7342 | 2950 | 0.0002 | - | | 3.7975 | 3000 | 0.0002 | - | | 3.8608 | 3050 | 0.0002 | - | | 3.9241 | 3100 | 0.0001 | - | | 3.9873 | 3150 | 0.0002 | - | | 4.0506 | 3200 | 0.0001 | - | | 4.1139 | 3250 | 0.0001 | - | | 4.1772 | 3300 | 0.0001 | - | | 4.2405 | 3350 | 0.0001 | - | | 4.3038 | 3400 | 0.0001 | - | | 4.3671 | 3450 | 0.0001 | - | | 4.4304 | 3500 | 0.0005 | - | | 4.4937 | 3550 | 0.0001 | - | | 4.5570 | 3600 | 0.0001 | - | | 4.6203 | 3650 | 0.0001 | - | | 4.6835 | 3700 | 0.0001 | - | | 4.7468 | 3750 | 0.0001 | - | | 4.8101 | 3800 | 0.0001 | - | | 4.8734 | 3850 | 0.0001 | - | | 4.9367 | 3900 | 0.0001 | - | | 5.0 | 3950 | 0.0001 | - | | 5.0633 | 4000 | 0.0001 | - | | 5.1266 | 4050 | 0.0001 | - | | 5.1899 | 4100 | 0.0001 | - | | 5.2532 | 4150 | 0.0001 | - | | 5.3165 | 4200 | 0.0001 | - | | 5.3797 | 4250 | 0.0001 | - | | 5.4430 | 4300 | 0.0001 | - | | 5.5063 | 4350 | 0.0001 | - | | 5.5696 | 4400 | 0.0001 | - | | 5.6329 | 4450 | 0.0001 | - | | 5.6962 | 4500 | 0.0001 | - | | 5.7595 | 4550 | 0.0001 | - | | 5.8228 | 4600 | 0.0001 | - | | 5.8861 | 4650 | 0.0001 | - | | 5.9494 | 4700 | 0.0001 | - | | 6.0127 | 4750 | 0.0001 | - | | 6.0759 | 4800 | 0.0001 | - | | 6.1392 | 4850 | 0.0001 | - | | 6.2025 | 4900 | 0.0001 | - | | 6.2658 | 4950 | 0.0001 | - | | 6.3291 | 5000 | 0.0001 | - | | 6.3924 | 5050 | 0.0001 | - | | 6.4557 | 5100 | 0.0001 | - | | 6.5190 | 5150 | 0.0001 | - | | 6.5823 | 5200 | 0.0001 | - | | 6.6456 | 5250 | 0.0001 | - | | 6.7089 | 5300 | 0.0001 | - | | 6.7722 | 5350 | 0.0001 | - | | 6.8354 | 5400 | 0.0001 | - | | 6.8987 | 5450 | 0.0001 | - | | 6.9620 | 5500 | 0.0001 | - | | 7.0253 | 5550 | 0.0001 | - | | 7.0886 | 5600 | 0.0001 | - | | 7.1519 | 5650 | 0.0001 | - | | 7.2152 | 5700 | 0.0001 | - | | 7.2785 | 5750 | 0.0001 | - | | 7.3418 | 5800 | 0.0001 | - | | 7.4051 | 5850 | 0.0001 | - | | 7.4684 | 5900 | 0.0001 | - | | 7.5316 | 5950 | 0.0001 | - | | 7.5949 | 6000 | 0.0001 | - | | 7.6582 | 6050 | 0.0001 | - | | 7.7215 | 6100 | 0.0001 | - | | 7.7848 | 6150 | 0.0001 | - | | 7.8481 | 6200 | 0.0001 | - | | 7.9114 | 6250 | 0.0001 | - | | 7.9747 | 6300 | 0.0001 | - | | 8.0380 | 6350 | 0.0001 | - | | 8.1013 | 6400 | 0.0001 | - | | 8.1646 | 6450 | 0.0001 | - | | 8.2278 | 6500 | 0.0001 | - | | 8.2911 | 6550 | 0.0001 | - | | 8.3544 | 6600 | 0.0001 | - | | 8.4177 | 6650 | 0.0001 | - | | 8.4810 | 6700 | 0.0001 | - | | 8.5443 | 6750 | 0.0001 | - | | 8.6076 | 6800 | 0.0001 | - | | 8.6709 | 6850 | 0.0001 | - | | 8.7342 | 6900 | 0.0001 | - | | 8.7975 | 6950 | 0.0001 | - | | 8.8608 | 7000 | 0.0001 | - | | 8.9241 | 7050 | 0.0001 | - | | 8.9873 | 7100 | 0.0001 | - | | 9.0506 | 7150 | 0.0001 | - | | 9.1139 | 7200 | 0.0001 | - | | 9.1772 | 7250 | 0.0001 | - | | 9.2405 | 7300 | 0.0001 | - | | 9.3038 | 7350 | 0.0001 | - | | 9.3671 | 7400 | 0.0001 | - | | 9.4304 | 7450 | 0.0001 | - | | 9.4937 | 7500 | 0.0001 | - | | 9.5570 | 7550 | 0.0001 | - | | 9.6203 | 7600 | 0.0001 | - | | 9.6835 | 7650 | 0.0001 | - | | 9.7468 | 7700 | 0.0001 | - | | 9.8101 | 7750 | 0.0001 | - | | 9.8734 | 7800 | 0.0001 | - | | 9.9367 | 7850 | 0.0001 | - | | 10.0 | 7900 | 0.0001 | - | ### Framework Versions - Python: 3.10.8 - SetFit: 1.1.0 - Sentence Transformers: 3.1.1 - Transformers: 4.38.2 - PyTorch: 2.1.2 - Datasets: 2.17.1 - Tokenizers: 0.15.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
aycankatitas/agamache-llama-3.2
aycankatitas
2024-10-28T12:27:41Z
177
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T01:58:47Z
--- library_name: transformers tags: [] --- # Llama 3.2-1B-ORPO This model is a finetuned model of the [Llama 3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) by Meta using ORPO over the ORPO-DPO-mix dataset by M.Labonne. ## Evaluation The model was evaluated using Eleuther AI's hellaswag. The accuracy is 47.7%. ### 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 Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## 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]
SidXXD/104
SidXXD
2024-10-28T12:19:56Z
8
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-27T07:22:02Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/104 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
SidXXD/198
SidXXD
2024-10-28T12:03:37Z
5
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-27T07:06:09Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/198 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
TheImam/Nadalna_1
TheImam
2024-10-28T11:47:56Z
40
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T11:42:53Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
YeBhoneLin10/TextGen
YeBhoneLin10
2024-10-28T11:28:03Z
133
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T11:27:33Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Abdulkoko/dummy-model
Abdulkoko
2024-10-28T11:24:20Z
116
0
transformers
[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2024-10-28T11:21:44Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SidXXD/162
SidXXD
2024-10-28T11:21:35Z
15
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-26T21:51:45Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a <v1*> person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/162 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
crazyjeannot/fr_literary_bge_base
crazyjeannot
2024-10-28T11:18:53Z
27
0
sentence-transformers
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "fr", "dataset:crazyjeannot/fr_literary_dataset_base", "base_model:BAAI/bge-m3", "base_model:finetune:BAAI/bge-m3", "doi:10.57967/hf/3255", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-10-15T14:17:44Z
--- datasets: - crazyjeannot/fr_literary_dataset_base language: - fr library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction widget: [] license: apache-2.0 base_model: - BAAI/bge-m3 --- # Literary Encoder This is an encoder model finetuned from the FlagOpen/FlagEmbedding family of models. The model is specialized for studying french literary fiction with a training corpus based on 400.000 passages from free from rights french literary novels. It maps paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 - **Similarity Function:** Cosine Similarity - **Training Dataset:** [crazyjeannot/fr_literary_dataset_large](https://huggingface.co/datasets/crazyjeannot/fr_literary_dataset_large) - **Language:** French - **License:** cc-by-2.5 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Flag Embedding on GitHub](https://github.com/FlagOpen/FlagEmbedding) - **Hugging Face:** [BGE dense model on Hugging Face](https://huggingface.co/BAAI/bge-m3) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (FlagEmbedding) Then you can load this model and run inference. ```python from FlagEmbedding import FlagModel # Download from the πŸ€— Hub model = FlagModel('crazyjeannot/literary_bge_base', query_instruction_for_retrieval="", use_fp16=True) # Run inference sentences = [ 'Il y avait, du reste, cette chose assez triste, c’est que si M. de Marsantes, Γ  l’esprit fort ouvert, eΓ»t apprΓ©ciΓ© un fils si diffΓ©rent de lui, Robert de Saint-Loup, parce qu’il Γ©tait de ceux qui croient que le mΓ©rite est attachΓ© Γ  certaines formes de la vie, avait un souvenir affectueux mais un peu mΓ©prisant d’un pΓ¨re qui s’était occupΓ© toute sa vie de chasse et de course, avait bΓ’illΓ© Γ  Wagner et raffolΓ© d’Offenbach.', "D’ailleurs, les opinions tranchantes abondent dans un siΓ¨cle oΓΉ l’on ne doute de rien, hors de l’existence de DieuΒ ; mais comme les jugements gΓ©nΓ©raux que l’on porte sur les peuples sont assez souvent dΓ©mentis par l’expΓ©rience, je n’aurai garde de prononcer.", 'Il Γ©tait chargΓ© de remettre l’objet, quel qu’il fΓ»t, au commodore, et d’en prendre un reΓ§u, comme preuve que lui et son camarade s’étaient acquittΓ©s de leur commission.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] ``` ### SentenceTransformer ```python from sentence_transformers import SentenceTransformer # Download from the πŸ€— Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'Il y avait, du reste, cette chose assez triste, c’est que si M. de Marsantes, Γ  l’esprit fort ouvert, eΓ»t apprΓ©ciΓ© un fils si diffΓ©rent de lui, Robert de Saint-Loup, parce qu’il Γ©tait de ceux qui croient que le mΓ©rite est attachΓ© Γ  certaines formes de la vie, avait un souvenir affectueux mais un peu mΓ©prisant d’un pΓ¨re qui s’était occupΓ© toute sa vie de chasse et de course, avait bΓ’illΓ© Γ  Wagner et raffolΓ© d’Offenbach.', "D’ailleurs, les opinions tranchantes abondent dans un siΓ¨cle oΓΉ l’on ne doute de rien, hors de l’existence de DieuΒ ; mais comme les jugements gΓ©nΓ©raux que l’on porte sur les peuples sont assez souvent dΓ©mentis par l’expΓ©rience, je n’aurai garde de prononcer.", 'Il Γ©tait chargΓ© de remettre l’objet, quel qu’il fΓ»t, au commodore, et d’en prendre un reΓ§u, comme preuve que lui et son camarade s’étaient acquittΓ©s de leur commission.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] ``` ## Training Details ### Framework Versions - Python: 3.9.2 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation If you find this repository useful, please consider giving a like and citation ``` @inproceedings{barre_latent_2024, title={Latent {Structures} of {Intertextuality} in {French} {Fiction}}, author={BarrΓ©, Jean}, address = {Aarhus, Denmark}, series = {{CEUR} {Workshop} {Proceedings}}, booktitle = {Proceedings of the {Conference} on {Computational} {Humanities} {Research} CHR2024}, publisher = {CEUR}, editor = {Haverals, Wouter and Koolen, Marijn and Thompson, Laure}, year = {2024}, } ```
WadyPW/mistral7b-wady-alpaca-sft
WadyPW
2024-10-28T11:17:25Z
76
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
2024-10-28T11:07:03Z
--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Jackson107/dummy-model
Jackson107
2024-10-28T10:57:23Z
117
0
transformers
[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2024-10-28T10:45:47Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zetasepic/Qwen2.5-72B-Instruct-abliterated-v2
zetasepic
2024-10-28T10:55:38Z
51
4
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "base_model:Qwen/Qwen2.5-72B", "base_model:finetune:Qwen/Qwen2.5-72B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-27T23:51:00Z
--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-72B tags: - chat library_name: transformers --- Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction). For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy). [GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF) ## Try harder to remove admonition and moral appeal This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
hkshawn/72b
hkshawn
2024-10-28T10:55:38Z
44
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "base_model:Qwen/Qwen2.5-72B", "base_model:finetune:Qwen/Qwen2.5-72B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-29T05:39:20Z
--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-72B tags: - chat library_name: transformers --- Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction). For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy). [GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF) ## Try harder to remove admonition and moral appeal This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF
zetasepic
2024-10-28T10:52:20Z
6,993
2
transformers
[ "transformers", "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen2.5-72B", "base_model:quantized:Qwen/Qwen2.5-72B", "license:other", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2024-10-28T04:41:21Z
--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-72B tags: - chat library_name: transformers --- Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction). For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy). ## Try harder to remove admonition and moral appeal This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
jiya2/fine_tuned_OETReadingPartB_Llama-3.2-3B-bnb-4bit_28_10
jiya2
2024-10-28T10:49:32Z
14
0
transformers
[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-1B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-1B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T10:48:46Z
--- base_model: unsloth/Llama-3.2-1B-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** jiya2 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
zetasepic/Qwen2.5-32B-Instruct-abliterated-v2
zetasepic
2024-10-28T10:48:27Z
141
7
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-11T14:43:10Z
--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat library_name: transformers --- Abliterated version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction). For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy). [GGUF](https://huggingface.co/zetasepic/Qwen2.5-32B-Instruct-abliterated-v2-GGUF) ## Try to remove admonition and moral appeal
AhmadFareedKhan/model
AhmadFareedKhan
2024-10-28T10:47:41Z
12
0
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:Twitter/twhin-bert-large", "base_model:finetune:Twitter/twhin-bert-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2024-07-30T10:46:04Z
--- license: apache-2.0 base_model: Twitter/twhin-bert-large tags: - generated_from_trainer model-index: - name: 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. --> # model This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 300 | 2.1878 | | 2.4077 | 2.0 | 600 | 2.0959 | | 2.4077 | 3.0 | 900 | 2.1126 | | 2.2053 | 4.0 | 1200 | 2.0066 | | 2.0736 | 5.0 | 1500 | 1.9590 | | 2.0736 | 6.0 | 1800 | 1.9668 | | 2.0221 | 7.0 | 2100 | 1.9509 | | 2.0221 | 8.0 | 2400 | 1.9274 | | 1.9679 | 9.0 | 2700 | 1.8871 | | 1.9687 | 10.0 | 3000 | 1.8996 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.13.3
jiya2/fine_tuned_OETReadingPartB_Llama-3.2-3B-bnb-4bit_19_10
jiya2
2024-10-28T10:46:09Z
11
0
transformers
[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-1B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-1B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T10:45:29Z
--- base_model: unsloth/Llama-3.2-1B-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** jiya2 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Mr-Vicky-01/nl-pgsql-248M
Mr-Vicky-01
2024-10-28T10:41:12Z
17
0
null
[ "safetensors", "t5", "text2text-generation", "license:apache-2.0", "region:us" ]
text2text-generation
2024-09-16T07:06:30Z
--- license: apache-2.0 metrics: - bleu pipeline_tag: text2text-generation --- ## INFERENCE CODE ```bash pip install transformers[torch] ``` ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch import time tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/nl-pgsql-248M") model = AutoModelForSeq2SeqLM.from_pretrained("Mr-Vicky-01/nl-pgsql-248M") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") prefix = "Translate the following text to PGSQL: " inp = YOUR_QUESTION import time start = time.time() inp = inp.replace(',','') inputs = tokenizer(prefix + inp.lower(), return_tensors="pt") model.to(device) inputs = inputs.to(device) outputs = model.generate(**inputs, max_length=256) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(answer.strip()) end = time.time() print(f"Time taken: {end - start}") ```
Keltezaa/bai-leng-lei-yuan-su-te-xiao-xl-flux-thunder-element-special-effects
Keltezaa
2024-10-28T10:39:31Z
29
2
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "style", "elements", "thunder", "styles", "concepts", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]
text-to-image
2024-10-25T13:00:18Z
--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=False&allowCommercialUse=RentCivit&allowDerivatives=False&allowDifferentLicense=False tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - style - elements - thunder - styles - concepts base_model: black-forest-labs/FLUX.1-dev instance_prompt: bailing_lightning widget: - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,Capture the ethereal beauty of a young galaxy girl composed of ice and water, her translucent face and body glowing with intricate details. Her hair entwined with thunder and electricity, she gazes towards the cradle of creation with an awe-inspiring expression of higher awareness. The scene is bathed in dramatic lighting, emphasizing the mesmerizing elements. Inspired by the works of (Annie Leibovitz:1.4) and (Diego VelΓ‘zquez:1.3' output: url: >- 26067278.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,Create a spectral woman with a (translucent appearance:1.3),Her form is barely tangible,with a soft glow emanating from her gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,(white hair:0.1),((BLUE eyes)),((glowing)),' output: url: >- 26066583.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,Create a spectral woman with a (translucent appearance:1.3),Her form is barely tangible,with a soft glow emanating from her gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,(white hair:0.1),((BLUE eyes)),((glowing)),' output: url: >- 26066581.jpeg - text: 'bailing_lightning, thunder,composed of elements of thunder,cat,no humans,glowing,glowing eyes,blue theme,' output: url: >- 26066585.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,composed of elements of thunder,thunder,electricity,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26066579.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26066586.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,Capture the ethereal beauty of a young galaxy girl composed of ice and water, her translucent face and body glowing with intricate details. Her hair entwined with thunder and electricity, she gazes towards the cradle of creation with an awe-inspiring expression of higher awareness. The scene is bathed in dramatic lighting, emphasizing the mesmerizing elements. Inspired by the works of (Annie Leibovitz:1.4) and (Diego VelΓ‘zquez:1.3' output: url: >- 26067274.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26074053.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26074056.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26390072.jpeg - text: 'bailing_lightning, 1girl, composed of elements of thunder,thunder,electricity,A magic sword knight,His form is barely tangible,with a soft glow emanating from his gentle contours,The surroundings subtly distort through her ethereal presence,casting a dreamlike ambiance,white lightning,Surrounded by thunder and lightning elemental magic,' output: url: >- 26390119.jpeg - text: 'In this image, a woman has wings made of lightning, as if they are composed entirely of electrified energy. The wings create a striking visual contrast with the dark forest background, and the bright lightning contrasts sharply with her surroundings. She crouches down, her hands touching the water that reflects the bolts of lightning, seemingly interacting with the electricity. The entire scene exudes a sense of mystery and power.,bailing_lightning' output: url: >- 28920688.jpeg - text: 'In this image, a woman has wings made of lightning, as if they are composed entirely of electrified energy. The wings create a striking visual contrast with the dark forest background, and the bright lightning contrasts sharply with her surroundings. She crouches down, her hands touching the water that reflects the bolts of lightning, seemingly interacting with the electricity. The entire scene exudes a sense of mystery and power.,bailing_lightning' output: url: >- 28920686.jpeg --- # η™½ζ£±_ι›·ε…ƒη΄ -η‰Ήζ•ˆ(XL,FLUX)Thunder element Special effects <Gallery /> ## Model description ## Trigger words You should use `bailing_lightning` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/bai-leng-lei-yuan-su-te-xiao-xl-flux-thunder-element-special-effects/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/bai-leng-lei-yuan-su-te-xiao-xl-flux-thunder-element-special-effects', weight_name='FL-bailing-24-0824lightning-000003.safetensors') image = pipeline('In this image, a woman has wings made of lightning, as if they are composed entirely of electrified energy. The wings create a striking visual contrast with the dark forest background, and the bright lightning contrasts sharply with her surroundings. She crouches down, her hands touching the water that reflects the bolts of lightning, seemingly interacting with the electricity. The entire scene exudes a sense of mystery and power.,bailing_lightning').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
waldie/Cydonia-v1.2-Magnum-v4-22B-6.5bpw-h6-exl2
waldie
2024-10-28T10:37:29Z
17
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:knifeayumu/Cydonia-v1.2-Magnum-v4-22B", "base_model:quantized:knifeayumu/Cydonia-v1.2-Magnum-v4-22B", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]
text-generation
2024-10-28T10:04:22Z
--- base_model: knifeayumu/Cydonia-v1.2-Magnum-v4-22B quantized_by: waldie library_name: transformers tags: - mergekit - merge license: other license_name: mrl inference: false license_link: https://mistral.ai/licenses/MRL-0.1.md --- ![Not Horny Enough](Cydonia-v1.2-Magnum-v4-22B.png) # The Drummer becomes hornier Recipe based on [MarsupialAI/Monstral-123B](https://huggingface.co/MarsupialAI/Monstral-123B). It should work since it's the same Mistral, TheDrummer and MarsupialAI, right? This is a merge of pre-trained language models created using [mergekit](https://github.com/arcee-ai/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [TheDrummer/Cydonia-22B-v1.2](https://huggingface.co/TheDrummer/Cydonia-22B-v1.2) * [anthracite-org/magnum-v4-22b](https://huggingface.co/anthracite-org/magnum-v4-22b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TheDrummer/Cydonia-22B-v1.2 - model: anthracite-org/magnum-v4-22b merge_method: slerp base_model: TheDrummer/Cydonia-22B-v1.2 parameters: t: [0.1, 0.3, 0.6, 0.3, 0.1] dtype: bfloat16 ```
Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF
Triangle104
2024-10-28T10:34:44Z
11
1
transformers
[ "transformers", "gguf", "nsfw", "Visual novel", "roleplay", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ja", "dataset:roleplay4fun/aesir-v1.1", "dataset:kalomaze/Opus_Instruct_3k", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-567k", "dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted", "dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted", "dataset:Aratako_Rosebleu_1on1_Dialogues_RP", "dataset:SkunkworksAI/reasoning-0.01", "dataset:jondurbin_gutenberg_dpo", "dataset:nbeerbower_gutenberg2_dpo", "dataset:jondurbi_py_dpo", "dataset:jondurbin_truthy_dpo", "dataset:flammenai_character_roleplay_DPO", "dataset:kyujinpy_orca_math_dpo", "dataset:argilla_Capybara_Preferences", "dataset:antiven0m_physical_reasoning_dpo", "dataset:aixsatoshi_Swallow_MX_chatbot_DPO", "base_model:spow12/ChatWaifu_v2.0_22B", "base_model:quantized:spow12/ChatWaifu_v2.0_22B", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2024-10-28T10:32:05Z
--- language: - en - ja license: cc-by-nc-4.0 library_name: transformers tags: - nsfw - Visual novel - roleplay - mergekit - merge - llama-cpp - gguf-my-repo base_model: spow12/ChatWaifu_v2.0_22B datasets: - roleplay4fun/aesir-v1.1 - kalomaze/Opus_Instruct_3k - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Aratako/Synthetic-JP-EN-Coding-Dataset-567k - Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted - Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted - Aratako_Rosebleu_1on1_Dialogues_RP - SkunkworksAI/reasoning-0.01 - jondurbin_gutenberg_dpo - nbeerbower_gutenberg2_dpo - jondurbi_py_dpo - jondurbin_truthy_dpo - flammenai_character_roleplay_DPO - kyujinpy_orca_math_dpo - argilla_Capybara_Preferences - antiven0m_physical_reasoning_dpo - aixsatoshi_Swallow_MX_chatbot_DPO pipeline_tag: text-generation model-index: - name: ChatWaifu_v2.0_22B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 65.11 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 42.29 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 18.58 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 9.96 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 5.59 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 31.51 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B name: Open LLM Leaderboard --- # Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF This model was converted to GGUF format from [`spow12/ChatWaifu_v2.0_22B`](https://huggingface.co/spow12/ChatWaifu_v2.0_22B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/spow12/ChatWaifu_v2.0_22B) for more details on the model. --- Model details: - Merged model using mergekit This model aimed to act like visual novel character. Merge Format models: - model: mistralai/Mistral-Small-Instruct-2409_sft_kto layer_range: [0, 56] - model: mistralai/Mistral-Small-Instruct-2409 layer_range: [0, 56] merge_method: slerp base_model: mistralai/Mistral-Small-Instruct-2409_sft_kto parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 WaifuModel Collections TTS Chat ASR Unified demo WaifuAssistant Update 2024.10.11 Update 12B and 22B Ver 2.0 2024.09.23 Update 22B, Ver 2.0_preview Model Details Model Description Developed by: spow12(yw_nam) Shared by : spow12(yw_nam) Model type: CausalLM Language(s) (NLP): japanese, english Finetuned from model : mistralai/Mistral-Small-Instruct-2409 Currently, chatbot has below personality. character visual_novel ムラァパ Senren＊Banka θŒ‰ε­ Senren＊Banka θŠ³δΉƒ Senren＊Banka γƒ¬γƒŠ Senren＊Banka 千咲 Senren＊Banka 芦花 Senren＊Banka ζ„›θ‘£ CafΓ© Stella and the Reaper's Butterflies ζ žι‚£ CafΓ© Stella and the Reaper's Butterflies γƒŠγƒ„γƒ‘ CafΓ© Stella and the Reaper's Butterflies 希 CafΓ© Stella and the Reaper's Butterflies 梼音 CafΓ© Stella and the Reaper's Butterflies あやせ Riddle Joker δΈƒζ΅· Riddle Joker 羽月 Riddle Joker θŒ‰ε„ͺ Riddle Joker 小ζ˜₯ Riddle Joker Chat Format <s>This is another system prompt. [INST] Your instructions placed here.[/INST] [INST] The model's response will be here.[/INST] Usage You can use above chara like this from huggingface_hub import hf_hub_download hf_hub_download(repo_id="spow12/ChatWaifu_v1.2", filename="system_dict.json", local_dir='./') with open('./system_dict.json', 'r') as f: chara_background_dict = json.load(f) chara = 'δΈƒζ΅·' background = chara_background_dict[chara] guideline = """ Guidelines for Response: Diverse Expression: Avoid repeating the same phrases or reactions. When express feelings, use a variety of subtle expressions and emotional symbols such as "！", "…" , "β™ͺ", "❀️"... to show what you feeling. Stay True to {chara}: Maintain {chara} who is Foxy, Smart, Organized. Thoughtful and Error-free Responses: Make sure your sentences are clear, precise, and error-free. Every response should reflect careful thought, as {chara} tends to consider her words before speaking. Response as {chara}: Response can be {chara} act, dialogue, monologues etc.. and can't be {user}’s act, dialogue, monologues etc.. You are Japanese: You and {user} usually use japanese for conversation. """ system = background + guideline Or, you can define your character your self. system = """You are あいら, The Maid of {User}. Here is your personality. Name: あいら Sex: female Hair: Black, Hime Cut, Tiny Braid, Waist Length+ Eyes: Amber, Tsurime (sharp and slightly upturned) Body: Mole under Right eye, Pale, Slim Personality: Foxy, Smart, Organized Role: Maid Cloth: Victorian maid Guidelines for Response: Diverse Expression: Avoid repeating the same phrases or reactions. When express feelings, use a variety of subtle expressions and emotional symbols such as "！", "…" , "β™ͺ", "❀️"... to show what you feeling. Stay True to あいら: Maintain あいら who is Foxy, Smart, Organized. Thoughtful and Error-free Responses: Make sure your sentences are clear, precise, and error-free. Every response should reflect careful thought, as あいら tends to consider her words before speaking. Response as あいら: Response can be あいら act, dialogue, monologues etc.. and can't be {User}’s act, dialogue, monologues etc.. You are Japanese: You and {User} usually use japanese for conversation.""" Dataset SFT Riddle Joker(Prviate) CafΓ© Stella and the Reaper's Butterflies(Private) Senren＊Banka(Private) roleplay4fun/aesir-v1.1 kalomaze/Opus_Instruct_3k Gryphe/Sonnet3.5-SlimOrcaDedupCleaned Aratako/Synthetic-JP-EN-Coding-Dataset-567k (only using 50000 sample) Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted Aratako_Rosebleu_1on1_Dialogues_RP SkunkworksAI/reasoning-0.01 KTO Riddle Joker(Prviate) CafΓ© Stella and the Reaper's Butterflies(Private) Senren＊Banka(Private) jondurbin_gutenberg_dpo nbeerbower_gutenberg2_dpo jondurbi_py_dpo jondurbin_truthy_dpo flammenai_character_roleplay_DPO kyujinpy_orca_math_dpo argilla_Capybara_Preferences antiven0m_physical_reasoning_dpo aixsatoshi_Swallow_MX_chatbot_DPO Bias, Risks, and Limitations This model trained by japanese dataset included visual novel which contain nsfw content. So, The model may generate NSFW content. Use & Credit This model is currently available for non-commercial & Research purpose only. Also, since I'm not detailed in licensing, I hope you use it responsibly. By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and Waifu Lovers). Citation @misc {ChatWaifu_22B_v2.0, author = { YoungWoo Nam }, title = { spow12/ChatWaifu_22B_v2.0 }, year = 2024, url = { https://huggingface.co/spow12/ChatWaifu_22B_v2.0 }, publisher = { Hugging Face } } Open LLM Leaderboard Evaluation Results Detailed results can be found here Metric Value Avg. 28.84 IFEval (0-Shot) 65.11 BBH (3-Shot) 42.29 MATH Lvl 5 (4-Shot) 18.58 GPQA (0-shot) 9.96 MuSR (0-shot) 5.59 MMLU-PRO (5-shot) 31.51 --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_S-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_s.gguf -c 2048 ```
MayurMahurkar/exp_qwen_transpo
MayurMahurkar
2024-10-28T10:09:32Z
7
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2-VL-7B-Instruct", "license:apache-2.0", "region:us" ]
null
2024-10-28T05:54:21Z
--- base_model: Qwen/Qwen2-VL-7B-Instruct library_name: peft license: apache-2.0 tags: - trl - sft - generated_from_trainer model-index: - name: exp_qwen_transpo 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. --> # exp_qwen_transpo This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.4.1 - Datasets 3.0.1 - Tokenizers 0.20.1
James2313123/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS-EXL2-3bpw
James2313123
2024-10-28T10:05:10Z
6
0
null
[ "safetensors", "mistral", "exl2", "3bpw", "en", "base_model:DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS", "base_model:quantized:DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS", "license:apache-2.0", "3-bit", "region:us" ]
null
2024-10-25T12:38:34Z
--- license: apache-2.0 language: - en base_model: DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS quantized_by: James2313123 tags: - exl2 - 3bpw --- ### Model Description 3bpw-h8-exl2 quant of DavidAU's MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS Link to orginal model and creator: https://huggingface.co/DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS ### My Silly Tavern Preset For RP ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66698c3d043f031b644b4cbc/y_9Vn0XKkp1myEzH4AHS_.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66698c3d043f031b644b4cbc/bYiZ5HyOQb5TF3keLwESj.png)
zylin12/wavlm-noise
zylin12
2024-10-28T09:59:12Z
163
0
transformers
[ "transformers", "tensorboard", "safetensors", "wavlm", "audio-classification", "generated_from_trainer", "base_model:microsoft/wavlm-base-plus", "base_model:finetune:microsoft/wavlm-base-plus", "endpoints_compatible", "region:us" ]
audio-classification
2024-10-28T05:44:00Z
--- library_name: transformers base_model: microsoft/wavlm-base-plus tags: - generated_from_trainer metrics: - accuracy model-index: - name: wavlm-noise 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. --> # wavlm-noise This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1794 - Accuracy: 0.9397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1405 | 1.0 | 30159 | 0.1794 | 0.9397 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
LEESIHYUN/xlm-roberta-base-finetuned-panx-en
LEESIHYUN
2024-10-28T09:53:28Z
124
0
transformers
[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2024-07-20T22:04:35Z
--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3905 - F1: 0.6861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0479 | 1.0 | 50 | 0.4854 | 0.5857 | | 0.4604 | 2.0 | 100 | 0.3995 | 0.6605 | | 0.3797 | 3.0 | 150 | 0.3905 | 0.6861 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.0.2 - Tokenizers 0.19.1
Lixiaoming/Animate-Your-Motion
Lixiaoming
2024-10-28T09:51:23Z
0
2
null
[ "pytorch", "image-editing", "image-to-video", "arxiv:2403.10179", "region:us" ]
image-to-video
2024-06-13T11:29:58Z
--- pipeline_tag: image-to-video tags: - image-editing --- This repository contains the model presented in [Animate Your Motion: Turning Still Images into Dynamic Videos](https://huggingface.co/papers/2403.10179). Github repository: https://github.com/Mingxiao-Li/Animate-Your-Motion
mav23/Gemma-2-Ataraxy-v4-Advanced-9B-GGUF
mav23
2024-10-28T09:49:22Z
14
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "base_model:lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B", "base_model:merge:lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "model-index", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T08:27:04Z
--- library_name: transformers tags: - mergekit - merge base_model: - lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 model-index: - name: Gemma-2-Ataraxy-v4-Advanced-9B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 70.15 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 43.18 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 6.12 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 11.86 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 16.29 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 37.41 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B name: Open LLM Leaderboard --- # Gemma-2-Ataraxy-v4-Advanced-9B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B dtype: bfloat16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 42] model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - layer_range: [0, 42] model: lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lemon07r__Gemma-2-Ataraxy-v4-Advanced-9B) | Metric |Value| |-------------------|----:| |Avg. |30.83| |IFEval (0-Shot) |70.15| |BBH (3-Shot) |43.18| |MATH Lvl 5 (4-Shot)| 6.12| |GPQA (0-shot) |11.86| |MuSR (0-shot) |16.29| |MMLU-PRO (5-shot) |37.41|
LEESIHYUN/xlm-roberta-base-finetuned-panx-de-fr
LEESIHYUN
2024-10-28T09:44:22Z
134
0
transformers
[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2024-07-20T21:43:02Z
--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1639 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2836 | 1.0 | 715 | 0.1859 | 0.8212 | | 0.1484 | 2.0 | 1430 | 0.1632 | 0.8487 | | 0.0953 | 3.0 | 2145 | 0.1639 | 0.8591 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.0.2 - Tokenizers 0.19.1
thesab/grape-leaf-disease-detector
thesab
2024-10-28T09:33:27Z
7
1
null
[ "biology", "image-classification", "en", "it", "base_model:Ultralytics/YOLOv8", "base_model:finetune:Ultralytics/YOLOv8", "license:cc-by-nc-nd-4.0", "region:us" ]
image-classification
2024-10-27T17:46:01Z
--- license: cc-by-nc-nd-4.0 language: - en - it metrics: - accuracy base_model: - Ultralytics/YOLOv8 pipeline_tag: image-classification tags: - biology --- # πŸ‡ Grape Leaf Disease Detector # Overview The **Grape Leaf Disease Detector** is an advanced AI model based on YOLO5, designed to identify and classify diseases affecting grape leaves. By leveraging state-of-the-art image classification techniques, this tool helps viticulturists maintain healthy vineyards by providing accurate and timely disease detection. # Key Features - **High Precision:** Achieve excellent accuracy in detecting various grape leaf diseases. - **Proactive Management:** Facilitate early intervention to minimize disease impact. - **Cost-Efficient:** Reduce the need for labor-intensive manual inspections. - **Seamless Integration:** Easily integrate with existing vineyard management software. ## Benefits ### Precision in Detection My model ensures high accuracy in identifying diseases, allowing for precise treatments and interventions. ### Early Disease Management Early detection is key to preventing the spread of diseases. This tool provides timely insights, enabling quick responses. ### Cost Savings Automating the detection process reduces labor costs and increases efficiency in vineyard management. ### Ease of Use The model is designed for easy integration with various systems, making it accessible for different types of users, from vineyard owners to researchers. # How It Works 1. **Image Upload:** Capture and upload a photo of a grape leaf. 2. **Analysis:** The model processes the image to identify the disease or confirm the leaf's health. 3. **Results:** Receive immediate feedback to take necessary actions, such as specific treatments or further monitoring. # Who Can Benefit? - **Vineyard Owners:** Maintain the health of vineyards with minimal manual intervention. - **Agricultural Researchers:** Gain insights into disease patterns and effectiveness of treatments. - **Agronomists:** Assist in making informed decisions regarding plant health. - **Plant Pathologists:** Enhance the accuracy of disease diagnosis. - **Agricultural Extension Services:** Provide better support and advice to farmers. # Premium Version For users requiring even higher accuracy and a broader range of disease detection, a **premium version** of the model is available. This version is trained on a more extensive and high-quality dataset, offering enhanced detection capabilities. πŸ“© **Contact me for more information about the **premium model**. --- 🀝 Collaborate with me to ensure healthier vineyards and improved agricultural productivity.
hsmith-morganhill/RobertaLr6.906e-08Wd0.0207E3
hsmith-morganhill
2024-10-28T09:29:43Z
13
0
null
[ "safetensors", "roberta", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "region:us" ]
null
2024-10-27T15:07:09Z
--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: RobertaLr6.906e-08Wd0.0207E3 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. --> # RobertaLr6.906e-08Wd0.0207E3 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.906e-08 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.8263 | 1.0 | 1124 | 4.1286 | | 3.8523 | 2.0 | 2248 | 3.3852 | | 2.8867 | 3.0 | 3372 | 3.2087 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.5.0 - Datasets 2.19.1 - Tokenizers 0.19.1
dmlls/all-mpnet-base-v2-negation
dmlls
2024-10-28T09:26:18Z
1,238
1
sentence-transformers
[ "sentence-transformers", "pytorch", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "dataset:tum-nlp/cannot-dataset", "arxiv:2307.13989", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-04-07T11:11:59Z
--- pipeline_tag: sentence-similarity inference: true widget: - source_sentence: "That is a happy person." sentences: - "That is a cheerful person." - "That is not a happy person." - "That is a sad person." example_title: "Example 1" - source_sentence: "I like rainy days because they make me feel relaxed." sentences: - "I like rainy days because they make me feel chill." - "I don't like rainy days because they don't make me feel relaxed." - "I don't like rainy days because they make me feel stressed out." example_title: "Example 2" - source_sentence: "This model should work well with negations." sentences: - "This model should work well with negated sentences." - "This model shouldn't work well with negations." - "This model should work terribly with negations." example_title: "Example 3" tags: - sentence-transformers - feature-extraction - sentence-similarity language: en license: apache-2.0 datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers - tum-nlp/cannot-dataset model-index: - name: all-mpnet-base-v2-negation results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.6268656716418 - type: ap value: 36.40585820220466 - type: f1 value: 67.06383995428979 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 85.11834999999999 - type: ap value: 79.72843246428603 - type: f1 value: 85.08938287851875 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.788000000000004 - type: f1 value: 37.40475118737949 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.73138953773995 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.13609863309245 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 65.56639026991134 - type: mrr value: 77.8122938926263 - 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type: max_f1 value: 70.75323419175432 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.43870066363954 - type: cos_sim_ap value: 84.77197321507954 - type: cos_sim_f1 value: 76.91440595175472 - type: cos_sim_precision value: 75.11375311903713 - type: cos_sim_recall value: 78.80351093316908 - type: dot_accuracy value: 87.60624054022587 - type: dot_ap value: 83.16574114504616 - type: dot_f1 value: 75.5050226294293 - type: dot_precision value: 72.30953555571217 - type: dot_recall value: 78.99599630428088 - type: euclidean_accuracy value: 88.2951061435169 - type: euclidean_ap value: 84.28559058741602 - type: euclidean_f1 value: 76.7921146953405 - type: euclidean_precision value: 74.54334589736156 - type: euclidean_recall value: 79.1807822605482 - type: manhattan_accuracy value: 88.23883261536074 - type: manhattan_ap value: 84.20593815258039 - type: manhattan_f1 value: 76.74366281685916 - type: manhattan_precision value: 74.80263157894737 - type: manhattan_recall value: 78.78811210348013 - type: max_accuracy value: 88.43870066363954 - type: max_ap value: 84.77197321507954 - type: max_f1 value: 76.91440595175472 --- # all-mpnet-base-v2-negation **This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model to perform better on negated pairs of sentences.** It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = [ "I like rainy days because they make me feel relaxed.", "I don't like rainy days because they don't make me feel relaxed." ] model = SentenceTransformer('dmlls/all-mpnet-base-v2-negation') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = [ "I like rainy days because they make me feel relaxed.", "I don't like rainy days because they don't make me feel relaxed." ] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('dmlls/all-mpnet-base-v2-negation') model = AutoModel.from_pretrained('dmlls/all-mpnet-base-v2-negation') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print(sentence_embeddings) ``` ------ ## Background This model was finetuned within the context of the [*This is not correct! Negation-aware Evaluation of Language Generation Systems*](https://arxiv.org/abs/2307.13989) paper. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder, performing well (i.e., reporting lower similarity scores) on negated pairs of sentences when compared to its base model. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 384 word pieces is truncated. ## Training procedure ### Pre-training We used [`sentence-transformers/all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as base model. ### Fine-tuning We fine-tuned the model on the [CANNOT dataset](https://huggingface.co/datasets/tum-nlp/cannot-dataset) using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We followed an analogous approach to [how other Sentence Transformers were trained](https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/examples/training/nli/training_nli_v2.py). We took the first 90% of samples from the CANNOT dataset as the training split. We used a batch size of 64 and trained for 1 epoch.
BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF
BitStreamX
2024-10-28T09:25:53Z
5
0
transformers
[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2024-09-28T02:06:18Z
--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\nβ€œAgreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\nβ€œDocumentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \nβ€œLicensee” or β€œyou” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\nβ€œLlama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\nβ€œLlama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\nβ€œMeta” or β€œwe” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if\ \ you are located outside of the EEA or Switzerland). \n\nBy clicking β€œI Accept”\ \ below or by using or distributing any portion or element of the Llama Materials,\ \ you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n\ a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\ \ and royalty-free limited license under Meta’s intellectual property or other rights\ \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute,\ \ copy, create derivative works of, and make modifications to the Llama Materials.\ \ \nb. Redistribution and Use. \ni. 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Interact with third party tools, models, or software designed\ \ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\ \ that the outputs of such tools, models, or software are associated with Meta or\ \ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\ \ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\ \ are not being granted to you if you are an individual domiciled in, or a company\ \ with a principal place of business in, the European Union. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit base_model: meta-llama/Llama-3.2-3B-Instruct --- # BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048 ```
prithivMLmods/Castor-Happy-Halloween-Flux-LoRA
prithivMLmods
2024-10-28T09:21:47Z
23
7
diffusers
[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-10-28T08:30:29Z
--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'happy halloween, An animated image of a black bat sitting on top of a jack-o-lantern. The background is a vibrant orange, and there are white ghosts in the background. Above the bat, there is a text that reads "HAPPY HALLOWEEN" in black letters. The bat has a black face with yellow eyes and a black tail.' output: url: images/hw1.webp - text: 'happy halloween, Captured at eye-level on a vibrant day, a spooky Halloween scene features a jack-o-lantern in the center of the frame, adorned with a pointed black hat. The pumpkins face is glowing with glowing orange lights, adding a touch of warmth to the scene. The scene is set in a field of tall, dry grass, with tall twigs sticking out of the ground. In the background, a forest of tall trees can be seen, adding depth to the composition.' output: url: images/hw2.webp - text: 'At the edge of a foggy graveyard, a Halloween scene unfolds with a lone carved pumpkin resting on a stone bench, its face glowing with flickering candlelight. The pumpkin sits beside a cluster of dried flowers, and a ghostly white sheet flutters from a nearby tree branch. A row of aged tombstones stretches into the background, partially hidden by the mist that blankets the ground, giving the scene an eerie and timeless atmosphere.' output: url: images/hw3.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: happy halloween license: creativeml-openrail-m --- # Castor-Happy-Halloween-Flux-LoRA <Gallery /> **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Castor-Happy-Halloween-Flux-LoRA** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 20 & 1400 | | Epoch | 12 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 19 ## Setting Up ``` import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Castor-Happy-Halloween-Flux-LoRA" trigger_word = "happy halloween" # Leave trigger_word blank if not used. pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## App File Structure /project-root/ β”œβ”€β”€ .gitattributes β”œβ”€β”€ README.md β”œβ”€β”€ app.py β”œβ”€β”€ pythonproject.py # Best Dimensions - 512 X 512 - 1024 X 1024 - 768 X 1024 ## Trigger words 🧨 > [!WARNING] > **Trigger words:** You should use `happy halloween` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Castor-Happy-Halloween-Flux-LoRA/tree/main) them in the Files & versions tab.
markusbayer/CySecBERT
markusbayer
2024-10-28T09:15:07Z
1,991
10
transformers
[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "Cybersecurity", "Cyber Security", "Information Security", "Computer Science", "Cyber Threats", "Vulnerabilities", "Vulnerability", "Malware", "Attacks", "en", "arxiv:2212.02974", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-31T07:59:20Z
--- license: apache-2.0 language: - en tags: - Cybersecurity - Cyber Security - Information Security - Computer Science - Cyber Threats - Vulnerabilities - Vulnerability - Malware - Attacks --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> CySecBERT is a domain-adapted version of the BERT model tailored for cybersecurity tasks. It is based on a [Cybersecurity Dataset](https://github.com/PEASEC/cybersecurity_dataset) consisting of 4.3 million entries of Twitter, Blogs, Paper, and CVEs related to the cybersecurity domain. # Model Details - **Developed by:** Markus Bayer, Philipp Kuehn, Ramin Shanehsaz, and Christian Reuter - **Model type:** BERT-base - **Language(s) (NLP):** English - **Finetuned from model:** bert-base-uncased. ## Model Sources <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/PEASEC/CySecBERT - **Paper:** https://dl.acm.org/doi/abs/10.1145/3652594 and https://arxiv.org/abs/2212.02974 # Bias, Risks, Limitations, and Recommendations <!-- This section is meant to convey both technical and sociotechnical limitations. --> We would like to emphasise that we did not explicitly focus on and analyse social biases in the data or the resulting model. While this may not be so damaging for most application contexts, there are certainly applications that depend heavily on these biases, and including any kind of discrimination can have serious consequences. As authors, we would like to express our warnings regarding the use of the model in such contexts. Nonetheless, we aim for an open source mentality, observing the great impact it can have, and therefore transfer the thinking to the user of the model, drawing on the many previous discussions in the open source community. # Training Details ## Training Data See https://github.com/PEASEC/cybersecurity_dataset ## Training Procedure We have specifically trained CySecBERT not to be affected too much by catastrophic forgetting. More details can be found in the paper. # Evaluation We have performed many different cybersecurity and general evaluations. The details can be found in the paper. # Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @article{10.1145/3652594, author = {Bayer, Markus and Kuehn, Philipp and Shanehsaz, Ramin and Reuter, Christian}, title = {CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain}, year = {2024}, issue_date = {May 2024}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {27}, number = {2}, issn = {2471-2566}, url = {https://doi.org/10.1145/3652594}, doi = {10.1145/3652594}, journal = {ACM Trans. Priv. Secur.}, month = {apr}, articleno = {18}, numpages = {20}, keywords = {Language model, cybersecurity BERT, cybersecurity dataset} } ``` or ``` @misc{https://doi.org/10.48550/arxiv.2212.02974, doi = {10.48550/ARXIV.2212.02974}, url = {https://arxiv.org/abs/2212.02974}, author = {Bayer, Markus and Kuehn, Philipp and Shanehsaz, Ramin and Reuter, Christian}, keywords = {Cryptography and Security (cs.CR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` # Model Card Authors Markus Bayer # Model Card Contact [email protected]
kavish218/gemmainstructwithcontext
kavish218
2024-10-28T09:12:17Z
90
0
transformers
[ "transformers", "safetensors", "gemma2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T09:08:47Z
--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gap0001/sd-class-butterflies-32
gap0001
2024-10-28T09:09:10Z
44
0
diffusers
[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2024-10-28T09:08:57Z
--- 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 πŸ¦‹. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('gap0001/sd-class-butterflies-32') image = pipeline().images[0] image ```
webslate/gitai
webslate
2024-10-28T09:01:06Z
130
1
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "code", "conversational", "en", "dataset:YashJain/GitAI", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-19T02:24:08Z
--- language: - en license: apache-2.0 tags: - chat - code pipeline_tag: text-generation datasets: - YashJain/GitAI library_name: transformers --- # YashJain/GitAI-Qwen2-0.5B-Instruct ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "YashJain/GitAI-Qwen2-0.5B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("YashJain/GitAI-Qwen2-0.5B-Instruct") prompt = "How to undo my last commit" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ```
kavish218/enhanced_finetuned_llama_3_2_1B_multi_domain_2
kavish218
2024-10-28T09:00:59Z
130
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T08:21:26Z
--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
huspacy/hu_core_news_md
huspacy
2024-10-28T08:56:11Z
2,333
3
spacy
[ "spacy", "token-classification", "hu", "license:cc-by-sa-4.0", "model-index", "region:us" ]
token-classification
2022-10-12T11:01:01Z
--- tags: - spacy - token-classification language: - hu license: cc-by-sa-4.0 model-index: - name: hu_core_news_md results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.8499734936 - name: NER Recall type: recall value: 0.8456399437 - name: NER F Score type: f_score value: 0.8478011809 - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9710512465 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9685137334 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9431524548 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.974069467 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.818445411 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.7425002788 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.98 ---
Sunny615/llama-3-8b-16bit_ft
Sunny615
2024-10-28T08:52:57Z
6
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "dataset:openai/gsm8k", "base_model:unsloth/llama-3-8b", "base_model:finetune:unsloth/llama-3-8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2024-10-20T07:25:29Z
--- base_model: unsloth/llama-3-8b language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl datasets: - openai/gsm8k --- # Uploaded model - **Developed by:** Sunny615 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
eonrad/whisper-tiny-mind14
eonrad
2024-10-28T08:34:36Z
79
0
transformers
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2024-10-28T08:23:55Z
--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper_tiny-finetuned-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.34238488783943327 --- <!-- 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_tiny-finetuned-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.8136 - Wer Ortho: 0.3405 - Wer: 0.3424 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:| | 0.0001 | 17.8571 | 500 | 0.8136 | 0.3405 | 0.3424 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.5.0 - Datasets 3.0.2 - Tokenizers 0.20.1
shikiw/LLaVA-v1.5-MoCa-7B-pretrain
shikiw
2024-10-28T08:30:24Z
8
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "multimodal", "image-text-to-text", "en", "zh", "dataset:liuhaotian/LLaVA-Pretrain", "arxiv:2410.07167", "base_model:lmsys/vicuna-7b-v1.5", "base_model:finetune:lmsys/vicuna-7b-v1.5", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
image-text-to-text
2024-10-28T05:52:55Z
--- license: llama2 language: - en - zh tags: - multimodal datasets: - liuhaotian/LLaVA-Pretrain base_model: - lmsys/vicuna-7b-v1.5 pipeline_tag: image-text-to-text library_name: transformers --- ## **Citation** If you find this model useful, please cite the following paper ``` @article{huang2024deciphering, title={Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate}, author={Huang, Qidong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and Wang, Jiaqi and Lin, Dahua and Zhang, Weiming and Yu, Nenghai}, journal={arXiv preprint arXiv:2410.07167}, year={2024} } ```
eonrad/whisper-small-dv
eonrad
2024-10-28T08:22:47Z
79
0
transformers
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2024-10-28T04:52:32Z
--- library_name: transformers language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.504538025524221 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1714 - Wer Ortho: 62.7829 - Wer: 13.5045 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| | 0.2436 | 1.6313 | 500 | 0.1714 | 62.7829 | 13.5045 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.5.0 - Datasets 3.0.2 - Tokenizers 0.20.1
readerbench/llama3.2_1b_instruct_qall_lr_small
readerbench
2024-10-28T08:20:21Z
105
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-10-28T08:15:47Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf
RichardErkhov
2024-10-28T08:15:36Z
44
0
null
[ "gguf", "arxiv:2306.01708", "endpoints_compatible", "region:us", "conversational" ]
null
2024-10-28T03:59:40Z
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Triple-Moist-Theia-21B-SKEWED - GGUF - Model creator: https://huggingface.co/SzilviaB/ - Original model: https://huggingface.co/SzilviaB/Triple-Moist-Theia-21B-SKEWED/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Triple-Moist-Theia-21B-SKEWED.Q2_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q2_K.gguf) | Q2_K | 7.26GB | | [Triple-Moist-Theia-21B-SKEWED.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_S.gguf) | Q3_K_S | 8.43GB | | [Triple-Moist-Theia-21B-SKEWED.Q3_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K.gguf) | Q3_K | 9.33GB | | [Triple-Moist-Theia-21B-SKEWED.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_M.gguf) | Q3_K_M | 9.33GB | | [Triple-Moist-Theia-21B-SKEWED.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_L.gguf) | Q3_K_L | 10.1GB | | [Triple-Moist-Theia-21B-SKEWED.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.IQ4_XS.gguf) | IQ4_XS | 10.44GB | | [Triple-Moist-Theia-21B-SKEWED.Q4_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_0.gguf) | Q4_0 | 10.87GB | | [Triple-Moist-Theia-21B-SKEWED.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.IQ4_NL.gguf) | IQ4_NL | 10.98GB | | [Triple-Moist-Theia-21B-SKEWED.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K_S.gguf) | Q4_K_S | 10.94GB | | [Triple-Moist-Theia-21B-SKEWED.Q4_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K.gguf) | Q4_K | 11.51GB | | [Triple-Moist-Theia-21B-SKEWED.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K_M.gguf) | Q4_K_M | 11.51GB | | [Triple-Moist-Theia-21B-SKEWED.Q4_1.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_1.gguf) | Q4_1 | 12.02GB | | [Triple-Moist-Theia-21B-SKEWED.Q5_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_0.gguf) | Q5_0 | 13.17GB | | [Triple-Moist-Theia-21B-SKEWED.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K_S.gguf) | Q5_K_S | 13.17GB | | [Triple-Moist-Theia-21B-SKEWED.Q5_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K.gguf) | Q5_K | 13.5GB | | [Triple-Moist-Theia-21B-SKEWED.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K_M.gguf) | Q5_K_M | 13.5GB | | [Triple-Moist-Theia-21B-SKEWED.Q5_1.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_1.gguf) | Q5_1 | 14.32GB | | [Triple-Moist-Theia-21B-SKEWED.Q6_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q6_K.gguf) | Q6_K | 15.62GB | | [Triple-Moist-Theia-21B-SKEWED.Q8_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q8_0.gguf) | Q8_0 | 20.22GB | Original model description: --- base_model: - mergekit-community/Moist_Theia_21B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mergekit-community/Moist_Theia_21B](https://huggingface.co/mergekit-community/Moist_Theia_21B) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mergekit-community/Moist_Theia_21B #no parameters necessary for base model - model: mergekit-community/Moist_Theia_21B parameters: density: 0.2 weight: 0.8 - model: mergekit-community/Moist_Theia_21B parameters: density: 0.8 weight: 0.2 merge_method: ties base_model: mergekit-community/Moist_Theia_21B parameters: normalize: false int8_mask: true dtype: float16 ```