Upload DistilBertForSequenceClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "AI Policy and Regulations",
+    "1": "AI Research",
+    "2": "AI Startups",
+    "3": "Adventure",
+    "4": "Aerobics & Cardio",
+    "5": "Africa Business & Economics",
+    "6": "Africa politics",
+    "7": "Agriculture",
+    "8": "Art and Culture",
+    "9": "Asia Business & Economics",
+    "10": "Asia Politics",
+    "11": "Australia Business & Economics",
+    "12": "Australia Politics",
+    "13": "Automotive and Transportation",
+    "14": "Autoracing",
+    "15": "Banking & Finance",
+    "16": "Baseball",
+    "17": "Basketball",
+    "18": "Biology",
+    "19": "Bonds Trading & Speculation",
+    "20": "Boxing",
+    "21": "Celebrity",
+    "22": "Chemistry and Material Sciences",
+    "23": "Chess",
+    "24": "Civil Rights Activism",
+    "25": "Climate Change",
+    "26": "Clothes",
+    "27": "Computer Hardware",
+    "28": "Consumer & Retail",
+    "29": "Consumer Electronics",
+    "30": "Cosmetics",
+    "31": "Cosmology & The Universe",
+    "32": "Crypto Trading & Speculation",
+    "33": "Culture",
+    "34": "Discover",
+    "35": "Disease Research",
+    "36": "Drug Discoveries",
+    "37": "Emerging Technologies",
+    "38": "Energy & Natural Resources",
+    "39": "Environmental Science",
+    "40": "Epidemics & Outbreaks",
+    "41": "Europe Business & Economics",
+    "42": "Europe Politics",
+    "43": "Extreme Sports",
+    "44": "Extreme Weather and Cataclysms",
+    "45": "Festivals",
+    "46": "Food",
+    "47": "Football",
+    "48": "Forex Trading & Speculation",
+    "49": "Gaming & VR",
+    "50": "Global Health",
+    "51": "Global Organizations",
+    "52": "Golf",
+    "53": "Health Policy",
+    "54": "Hockey",
+    "55": "Human Rights",
+    "56": "India Business & Economics",
+    "57": "India Politics",
+    "58": "Inflation",
+    "59": "Interest Rates",
+    "60": "Jewelry",
+    "61": "Keto, Paleo, Vegan, Mediterranean",
+    "62": "Labor Activism",
+    "63": "Latin America Economy",
+    "64": "Latin America Politics",
+    "65": "Longevity",
+    "66": "MMA",
+    "67": "Medical Innovations",
+    "68": "Men's Health",
+    "69": "Mental Health Treatments",
+    "70": "Middle East Business & Economics",
+    "71": "Middle East Politics",
+    "72": "Movies",
+    "73": "Music",
+    "74": "Nonprofit, Charities, & Fundraising",
+    "75": "Nutrition Research",
+    "76": "Olympic Sports",
+    "77": "Operating Systems",
+    "78": "Other Sports",
+    "79": "Personal Finance & Financial Education",
+    "80": "Photographers",
+    "81": "Physics",
+    "82": "Real Estate & Housing",
+    "83": "Renewable Energy",
+    "84": "Royal Families",
+    "85": "SCOTUS",
+    "86": "Soccer",
+    "87": "Social Media",
+    "88": "Software Applications",
+    "89": "Space Exploration",
+    "90": "Space Technology",
+    "91": "Stocks Trading & Speculation",
+    "92": "Stress and Wellness",
+    "93": "Swimming",
+    "94": "Tech Giants",
+    "95": "Tech Industry Trends",
+    "96": "Tech Startups",
+    "97": "Television",
+    "98": "Tennis",
+    "99": "US Campaigns & Elections",
+    "100": "US Circuit and Appeals Courts",
+    "101": "US Congress",
+    "102": "US Crime, Violence, Terrorism & cybercrime",
+    "103": "US Federal Elections",
+    "104": "US Federal Policies",
+    "105": "US Involvement in Foreign Conflicts",
+    "106": "US Local Elections",
+    "107": "US Local Policies",
+    "108": "US Police Misconduct",
+    "109": "US Political Corruption",
+    "110": "Unemployment",
+    "111": "United Kingdom Business & Economics",
+    "112": "United Kingdom Politics",
+    "113": "Vaccine Development",
+    "114": "Video Games",
+    "115": "Weightlifting & Bodybuilding",
+    "116": "Women\u2019s Health",
+    "117": "Workforce / Labor",
+    "118": "Writing and Literature",
+    "119": "pets_animals_wildlife"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "AI Policy and Regulations": 0,
+    "AI Research": 1,
+    "AI Startups": 2,
+    "Adventure": 3,
+    "Aerobics & Cardio": 4,
+    "Africa Business & Economics": 5,
+    "Africa politics": 6,
+    "Agriculture": 7,
+    "Art and Culture": 8,
+    "Asia Business & Economics": 9,
+    "Asia Politics": 10,
+    "Australia Business & Economics": 11,
+    "Australia Politics": 12,
+    "Automotive and Transportation": 13,
+    "Autoracing": 14,
+    "Banking & Finance": 15,
+    "Baseball": 16,
+    "Basketball": 17,
+    "Biology": 18,
+    "Bonds Trading & Speculation": 19,
+    "Boxing": 20,
+    "Celebrity": 21,
+    "Chemistry and Material Sciences": 22,
+    "Chess": 23,
+    "Civil Rights Activism": 24,
+    "Climate Change": 25,
+    "Clothes": 26,
+    "Computer Hardware": 27,
+    "Consumer & Retail": 28,
+    "Consumer Electronics": 29,
+    "Cosmetics": 30,
+    "Cosmology & The Universe": 31,
+    "Crypto Trading & Speculation": 32,
+    "Culture": 33,
+    "Discover": 34,
+    "Disease Research": 35,
+    "Drug Discoveries": 36,
+    "Emerging Technologies": 37,
+    "Energy & Natural Resources": 38,
+    "Environmental Science": 39,
+    "Epidemics & Outbreaks": 40,
+    "Europe Business & Economics": 41,
+    "Europe Politics": 42,
+    "Extreme Sports": 43,
+    "Extreme Weather and Cataclysms": 44,
+    "Festivals": 45,
+    "Food": 46,
+    "Football": 47,
+    "Forex Trading & Speculation": 48,
+    "Gaming & VR": 49,
+    "Global Health": 50,
+    "Global Organizations": 51,
+    "Golf": 52,
+    "Health Policy": 53,
+    "Hockey": 54,
+    "Human Rights": 55,
+    "India Business & Economics": 56,
+    "India Politics": 57,
+    "Inflation": 58,
+    "Interest Rates": 59,
+    "Jewelry": 60,
+    "Keto, Paleo, Vegan, Mediterranean": 61,
+    "Labor Activism": 62,
+    "Latin America Economy": 63,
+    "Latin America Politics": 64,
+    "Longevity": 65,
+    "MMA": 66,
+    "Medical Innovations": 67,
+    "Men's Health": 68,
+    "Mental Health Treatments": 69,
+    "Middle East Business & Economics": 70,
+    "Middle East Politics": 71,
+    "Movies": 72,
+    "Music": 73,
+    "Nonprofit, Charities, & Fundraising": 74,
+    "Nutrition Research": 75,
+    "Olympic Sports": 76,
+    "Operating Systems": 77,
+    "Other Sports": 78,
+    "Personal Finance & Financial Education": 79,
+    "Photographers": 80,
+    "Physics": 81,
+    "Real Estate & Housing": 82,
+    "Renewable Energy": 83,
+    "Royal Families": 84,
+    "SCOTUS": 85,
+    "Soccer": 86,
+    "Social Media": 87,
+    "Software Applications": 88,
+    "Space Exploration": 89,
+    "Space Technology": 90,
+    "Stocks Trading & Speculation": 91,
+    "Stress and Wellness": 92,
+    "Swimming": 93,
+    "Tech Giants": 94,
+    "Tech Industry Trends": 95,
+    "Tech Startups": 96,
+    "Television": 97,
+    "Tennis": 98,
+    "US Campaigns & Elections": 99,
+    "US Circuit and Appeals Courts": 100,
+    "US Congress": 101,
+    "US Crime, Violence, Terrorism & cybercrime": 102,
+    "US Federal Elections": 103,
+    "US Federal Policies": 104,
+    "US Involvement in Foreign Conflicts": 105,
+    "US Local Elections": 106,
+    "US Local Policies": 107,
+    "US Police Misconduct": 108,
+    "US Political Corruption": 109,
+    "Unemployment": 110,
+    "United Kingdom Business & Economics": 111,
+    "United Kingdom Politics": 112,
+    "Vaccine Development": 113,
+    "Video Games": 114,
+    "Weightlifting & Bodybuilding": 115,
+    "Women\u2019s Health": 116,
+    "Workforce / Labor": 117,
+    "Writing and Literature": 118,
+    "pets_animals_wildlife": 119
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.0",
+  "vocab_size": 30522
+}

model.safetensors

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