metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: deberta-v3-large
    results: []

deberta-v3-large-sentiment

This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet_eval dataset.

Model description

Test set results:

Model	Emotion	Hate	Irony	Offensive	Sentiment
deberta-v3-large	86.3	61.3	87.1	86.4	73.9
BERTweet	79.3	-	82.1	79.5	73.4
RoB-RT	79.5	52.3	61.7	80.5	69.3

Classifying attributes of interest on tweeter like data.

Fine tuned and evaluated with run_glue.py

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6417	0.27	100	0.6283	0.6533
0.5105	0.54	200	0.4588	0.7915
0.4554	0.81	300	0.4500	0.7968
0.4212	1.08	400	0.4773	0.7938
0.4054	1.34	500	0.4311	0.7983
0.3922	1.61	600	0.4588	0.7998
0.3776	1.88	700	0.4367	0.8066
0.3535	2.15	800	0.4675	0.8074
0.33	2.42	900	0.4874	0.8021
0.3113	2.69	1000	0.4949	0.8044
0.3203	2.96	1100	0.4550	0.8059
0.248	3.23	1200	0.4858	0.8036
0.2478	3.49	1300	0.5299	0.8029
0.2371	3.76	1400	0.5013	0.7991
0.2388	4.03	1500	0.5520	0.8021
0.1744	4.3	1600	0.6687	0.7915
0.1788	4.57	1700	0.7560	0.7689
0.1652	4.84	1800	0.6985	0.7832
0.1596	5.11	1900	0.7191	0.7915
0.1214	5.38	2000	0.9097	0.7893
0.1432	5.64	2100	0.9184	0.7787
0.1145	5.91	2200	0.9620	0.7878
0.1069	6.18	2300	0.9489	0.7893
0.1012	6.45	2400	1.0107	0.7817
0.0942	6.72	2500	1.0021	0.7885
0.087	6.99	2600	1.1090	0.7915
0.0598	7.26	2700	1.1735	0.7795
0.0742	7.53	2800	1.1433	0.7817
0.073	7.79	2900	1.1343	0.7953
0.0553	8.06	3000	1.2258	0.7840
0.0474	8.33	3100	1.2461	0.7817
0.0515	8.6	3200	1.2996	0.7825
0.0551	8.87	3300	1.2819	0.7855
0.0541	9.14	3400	1.2808	0.7855
0.0465	9.41	3500	1.3398	0.7817
0.0407	9.68	3600	1.3231	0.7825
0.0343	9.94	3700	1.3330	0.7825