add model

README.md

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-# tweet-topic-19-single
-
-This is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
-The original roBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
-
-- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
-- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
-
-<b>Labels</b>: 
-- 0 -> arts_&_culture;
-- 1 -> business_&_entrepreneurs;
-- 2 -> pop_culture;
-- 3 -> daily_life;
-- 4 -> sports_&_gaming;
-- 5 -> science_&_technology
-
-
-## Full classification example
-
-```python
-from transformers import AutoModelForSequenceClassification
-from transformers import AutoTokenizer
-import numpy as np
-from scipy.special import softmax
-    
-MODEL = f"antypasd/tweet-topic-19-single"
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
-# PT
-model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-class_mapping = model.config.id2label
-text = "Tesla stock is on the rise!"
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-output = model(**encoded_input)
-scores = output[0][0].detach().numpy()
-scores = softmax(scores)
-ranking = np.argsort(scores)
-ranking = ranking[::-1]
-for i in range(scores.shape[0]):
-    l = class_mapping[ranking[i]]
-    s = scores[ranking[i]]
-    print(f"{i+1}) {l} {np.round(float(s), 4)}")
-```
-
-Output: 
-
-```
-1) business_&_entrepreneurs 0.8575
-2) science_&_technology 0.0604
-3) pop_culture 0.0295
-4) daily_life 0.0217
-5) sports_&_gaming 0.0154
-6) arts_&_culture 0.0154
-```
+---
+tags:
+- generated_from_keras_callback
+model-index:
+- name: tf version
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# tf version
+
+This model is a fine-tuned version of [antypasd/tweet-topic-19-single](https://huggingface.co/antypasd/tweet-topic-19-single) on an unknown dataset.
+It achieves the following results on the evaluation set:
+
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- optimizer: None
+- training_precision: float32
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- TensorFlow 2.8.2
+- Tokenizers 0.12.1

tf_model.h5

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